Introduction | Required Packages | Imputation Rules | Imputation on a Vector | Imputation Level | Minimum/Maximum Dates | Imputation Flags | Imputation Functions | Examples | Create an Imputed Datetime and Date Variable and Imputation Flag Variables | Create an Imputed Date Variable and Imputation Flag Variable | Impute Time without Imputing Date | Avoid Imputed Dates Before a Particular Date | Avoid Imputed Dates After a Particular Date | Imputation Without Creating a New Variable | Using More Than One Imputation Rule for a Variable

Step 1/5 - Preparing the Data | Step 2/5 - Creating data extract specifications | Step 3/5 - Creating the UI | Step 4/5 - Creating the Server Logic | Step 5/5 - Creating the shiny App | Try it out in Shinylive

merge_expression_module | Step 1/5 - Preparing the Data | Step 2/5 - Creating the Data Extracts | Step 3/5 - Creating the UI | Step 4/5 - Creating the Server Logic | Step 5/5 - Creating the shiny App | data_extract_multiple_srv + merge_expression_srv | Modifying the Server Logic | Updating the shiny app | Output from merging | Merging of non CDISC datasets

Introduction | Your first teal application: | Try the above app in shinylive | Creating your own applications | Application data | Modules | Defining filters | Reporting | Reproducible code | Embedding teal in shiny application | Where to go next

GENERAL | General Concepts | 1. Input dataset and dataset names | 2. Expected variables in input analysis dataset | 3. Character vs Factor | 4. Testing the codes for plot generation | General Control Arguments | 1. lbl_overall: Column of Total | 2. Column counts: N=xxx | TABLES | Safety Summary (AET01) | 1. Safety Summary | 2. Safety Summary with Modified Rows | Safety Summary (Adverse Events of Special Interest) (AET01_AESI) | 1. Safety Summary (Adverse Events of Special Interest) | 2. Safety Summary (Adverse Events of Special Interest) (optional lines) | 3. Safety Summary (Adverse Events of Special Interest) (for studies with multiple drugs) | Adverse Events (AET02) | 1. Adverse Events | 2. Adverse Events (with High-level Term) | 3. Adverse Events (Preferred Terms only) | Adverse Events by Greatest Intensity(AET03) | 1. Adverse Events by Greatest Intensity | Adverse Events by Highest NCI CTCAE Grade (AET04) | 1. Adverse Events by Highest NCI CTCAE Grade | 2. Adverse Events by Highest NCI CTCAE Grade (Fill in of Grades) | 3. Adverse Events by Highest NCI CTCAE Grade with modified grouping of grade | Adverse Event Rate Adjusted for Patient-Years at Risk - First Occurrence (AET05) | 1. Adverse Event Rate Adjusted for Patient-Years at Risk - First Occurrence | 2. Adverse Event Rate Adjusted for Patient-Years at Risk - First Occurrence (setting type of confidence interval) | Adverse Event Rate Adjusted for Patient-Years at Risk - All Occurrences (AET05_ALL) | 1. Adverse Event Rate Adjusted for Patient-Years at Risk - All Occurrences | 2. Adverse Event Rate Adjusted for Patient-Years at Risk - All Occurrences (setting type of confidence interval) | Most Common (>=5%) Adverse Events (AET10) | 1. Most Common (>=5%) Adverse Events | 2. Most Common (>=8%) Adverse Events (setting threshold) | Absolute Value and Change from Baseline by Visit (CFBT01) | 1. Absolute Value and Change from Baseline by Visit | 2. Absolute Value and Change from Baseline by Visit without Screening | 3. Absolute Value by Visit | Concomitant Medications by Medication Class and Preferred Name (CMT01A) | 1. Concomitant Medications by Medication Class and Preferred Name | 2. Concomitant Medications by Medication Class and Preferred Name (changing ATC class level) | 3. Concomitant Medications by Medication Class and Preferred Name (classes sorted by frequency) | 4. Concomitant Medications by Medication Class and Preferred Name (total number of treatments per medication class suppressed) | Concomitant Medications by Preferred Name (CMT02_PT) | 1. Concomitant Medications by Preferred Name | Cox Regression (COXT01) | 1. Cox Regression | 2. Cox Regression (with interaction term) | 3. Cox Regression (specifying covariates) | 4. Cox Regression (setting strata, ties, and alpha level) | Multi-variable Cox Regression (COXT02) | 1. Multi-variable Cox Regression | 2. Multi-variable Cox Regression (specifying covariates) | 3. Multi-variable Cox Regression (setting strata, ties, and alpha level) | Demographics and Baseline Characteristics (DMT01) | 1. Demographics and Baseline Characteristics with All Patients | 2. Demographics and Baseline Characteristics without All Patients | 3. Demographics and Baseline Characteristics with an additional study specific continuous variable | 4. Demographics and Baseline Characteristics with an additional study specific categorical variable | 5. Demographics and Baseline Characteristics with additional vital signs baseline values from ADVS or ADSUB | Patient Disposition (DST01) | 1. Patient Disposition | 2. Patient Disposition (with grouping of reasons) | 3. Patient Disposition (adding end of treatment status) | 4. Patient Disposition (adding details of study ongoing status) | Deaths (DTHT01) | 1. Deaths | 2. Deaths (adding "Primary Cause of Death" details for 'Other' category) | 3. Deaths (adding summary by days from last study drug administration) | ECG Results and Change from Baseline by Visit (EGT01) | 1. ECG Results and Change from Baseline by Visit | ECG Abnormalities (Regardless of Abnormality at Baseline) (EGT02_1) | 1. ECG Abnormalities (Regardless of Abnormality at Baseline) | ECG Abnormalities (Among Subject Without Abnormality at Baseline) (EGT02_2) | 1. ECG Abnormalities (Among Subject Without Abnormality at Baseline) | Shift Table of ECG Interval Data - Baseline versus Minimum/Maximum Post-Baseline (EGT03) | 1. Shift Table of ECG Interval Data - Baseline versus Minimum Post-Baseline | 2. Shift Table of ECG Interval Data - Baseline versus Maximum Post-Baseline | ECG Actual Values and Changes from Baseline by Visit (EGT05_QTCAT) | 1. ECG Actual Values and Changes from Baseline by Visit | 2. ECG Actual Values and Changes from Baseline by Visit (removing default analyses) | Study Drug Exposure (EXT01) | 1. Study Drug Exposure | Laboratory Test Results and Change from Baseline by Visit (LBT01) | 1. Laboratory Test Results and Change from Baseline by Visit | 2. Laboratory Test Results and Change from Baseline by Visit (customized precision) | Laboratory Abnormalities (LBT04) | 1. Laboratory Abnormalities | Laboratory Abnormalities with Single and Replicated Marked (LBT05) | 1. Laboratory Abnormalities with Single and Replicated Marked | 2. Laboratory Abnormalities with Single and Replicated Marked showing all categories | 3. Laboratory Abnormalities with Single and Replicated Marked with study specific MLAs | Laboratory Abnormalities by Visit and Baseline Status (LBT06) | 1. Laboratory Abnormalities by Visit and Baseline Status | Laboratory Test Results with Highest NCI CTCAE Grade Post-Baseline (LBT07) | 1. Laboratory Test Results with Highest NCI CTCAE Grade Post-Baseline | Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (LBT14) | 1. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (High) | 2. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (Low) | 3. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (High) Without Patients with Missing Baseline | 4. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (Low) with Missing Baseline Considered as Grade 0 | 4. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (with fill in of grades) | Medical History (MHT01) | 1. Medical History | 2. Medical History showing additional column 'All Patients' | Major Protocol Deviations (PDT01) | 1. Major Protocol Deviations | Reasons for Major Protocol Deviations Related to Epidemic/Pandemic (PDT02) | 1. Reasons for Major Protocol Deviations Related to Epidemic/Pandemic | Duration of Exposure for Risk Management Plan (RMPT01) | 1. Duration of Exposure for Risk Management Plan | Extent of Exposure by Age Group and Gender for Risk Management Plan (RMPT03) | 1. Extent of Exposure by Age Group and Gender for Risk Management Plan | Extent of Exposure by Ethnic Origin for Risk Management Plan (RMPT04) | 1. Extent of Exposure by Ethnic Origin for Risk Management Plan | Extent of Exposure by Race for Risk Management Plan (RMPT05) | 1. Extent of Exposure by Race for Risk Management Plan | Best Overall Response (RSPT01) | 1. Best Overall Response | 2. Best Overall Response (Ordering of treatment groups) | 3. Best Overall Response (selecting sections to display) | 4. Best Overall Response (with stratified analysis) | 5. Best Overall Response (modifying analysis details like type of confidence interval, alpha level, test for p-value) | 6. Best Overall Response (modifying the definition of overall response) | Time-to-event Summary (TTET01) | 1. Time-to-event Summary | 2. Time-to-event Summary (selecting sections to display) | 3. Time-to-event Summary (modifying analysis details like confidence interval type, ties, and alpha level) | 4. Time-to-event Summary (with stratified analysis) | 5. Time-to-event Summary (modifying time point for the "survival at xx months" analysis) | 6. Time-to-event Summary (modifying the p-value method for testing hazard ratio) | Vital Signs (VST01) | 1. Vital Sign Results and Change from Baseline by Visit | Vital Signs Abnormalities (Regardless of Abnormality at Baseline) (VST02_1) | 1. Vital Sign Abnormalities (Regardless of Abnormality at Baseline) | Vital Signs Abnormalities (Among Subject Without Abnormality at Baseline) (VST02_2) | 1. Vital Sign Abnormalities (Among Subject Without Abnormality at Baseline) | LISTINGS | Glossary of Adverse Event Preferred Terms and Investigator-Specified Terms (AEL01_NOLLT) | 1. Glossary of Adverse Event Preferred Terms and Investigator-Specified Terms | Graphics | Forest Plot for Odds Ratio (FSTG01) | 1. Forest Plot for Odds Ratio (with subgroup analysis) | 2. Forest Plot for Odds Ratio (with a user-defined confidence level) | 3. Forest Plot for Odds Ratio (with p-values and/or different statistics) | 4. Forest Plot for Odds Ratio (with user-defined subgroup analysis) | 5. Forest Plot for Odds Ratio (with stratified analysis) | 6. Forest Plot for Odds Ratio (without proportional sizing of the odds ratio symbol) | Forest Plot for Hazard Ratio (FSTG02) | 1. Forest Plot for Hazard Ratio (with subgroup analysis) | 2. Forest Plot for Hazard Ratio (with p-values and/or different statistics) | 3. Forest Plot for Hazard Ratio (with user-defined subgroup analysis) | 4. Forest Plot for Hazard Ratio (with stratified analysis) | 5. Forest Plot for Hazard Ratio (without proportional sizing of the hazard ratio symbol) | Kaplan-Meier Plot (KMG01) | 1. Kaplan-Meier Plot (without comparative statistics) | 2. Kaplan-Meier Plot (with comparative statistics) | 3. Kaplan-Meier Plot (without censoring marks) | 4. Kaplan-Meier Plot (without estimation of median survival time) | 5. Kaplan-Meier Plot (with statistical annotation of either median or min of survival time) | 6. Kaplan-Meier Plot (without the table of patients at risk) | Mean Plot (MNG01) | 1. Plot of Mean and Confidence Interval (with Table Section) | 2. Plot of Mean and Confidence Interval of Change from Baseline of Vital Signs | 3. Plot of Mean (+/-SD) (Changing the Statistics) | 4. Plot of Mean and Confidence Interval (Modify Alpha Level) | 5. Plot of Mean and Confidence Interval （With Number of Patients Only) | 6. Plot of Mean and Confidence Interval (without Table Section)

Introduction | Quick Start | Reproducibility | Relational data models | Hidden objects

Overview | Uses of join_keys class in teal applications | Anatomy of join_keys | join_keys(...): Join Keys Constructor / Getter / Setter | join_key(dataset_1, dataset_2, key, parent): Single Join Key Constructor | Example & Output | Accessing and Modifying keys | Merging Join Keys | Join Keys Relationships | Primary Key with teal_data | Foreign Keys with teal_data | Implicit Relationships | Join Keys in ADaM Datasets

Introduction | Integration with teal | Automatic Report Functionality | More control

Introduction | Creating a teal_report | Adding content to the teal_report | Adding arbitrary markdown content | Adding reproducible code chunks | Modify teal_report content | Document metadata | Preview report | Output teal_report | Key Benefits | Further Reading

Introduction | Custom module | Add support for reporting | Modify the declaration of the server function | Return the reporter object | Add content to the card | Add non-text content to the card | Removing support for displaying reproducible code | Removing reporting | Customizing the reporter

Introduction | Understanding the Inputs and Requirements | Setting Up the teal Module UI | Setting Up the teal Module Server | Passing the data Argument to the Server Function | Understanding teal_data as a Reactive Object in Server Logic | Using names() to Access Dataset Names in teal_data object | Accessing Specific Datasets with Double Brackets ([[ ]]) | Setting Up Server Logic Using teal_data and Dynamic Variable Injection | Creating the Custom teal Module Function | Integrating the Custom teal Module into a teal App | What's next? | Adding reporting to a module | Using standard widgets in your custom module

teal apps with the filter panel | Extending teal.slice | Filter panel respective to teal_module | Global and module specific filter panel

Data in teal Applications | General data | Reproducible data | code from file | Creating data in-app | Clinical data | ADaM data | MultiAssayExperiment data | teal_data properties | join_keys | verified | Hidden datasets | Further reading

Introduction | Creating a data transformation module | Single transformator | Multiple transformators | Targeting multiple datasets | Targeting one dataset

Introduction to qenv | Initialization | qenv basic usage | Substitutions | Combining qenv objects | Warnings and messages in qenv objects | Utilizing qenv inside shiny applications | Reproducibility | qenv and teal applications

Introduction | Dataset Contents | Required Packages | Programming Workflow | Initial set up of ADBCVA | Deriving LogMAR Score Parameters | Further Derivations of Standard BDS Variables | Deriving Analysis Value Categories for Snellen Scores | Deriving Criterion Flags for BCVA Change | Additional Notes | Example Script

Introduction | Dataset Contents | Acronyms | Required Packages | Programming Workflow | Initial set up of ADGA | Creating Square Root Transformed GA Area measured by FAF derived parameter | Assigning AVISIT/AVISITN | Deriving Worst Post-Baseline Assessment Flag | Further Derivations of Standard BDS Variables | Example Script

Introduction | What does {admiralophtha} offer? | Starting a Script | Support

Introduction | Dataset Subdivision | Criterion Flags | Affected Eye Derivation | Differences between pre and post-dose IOP

Why would I use this feature? | How do I use this feature? | 1. Create a named list | 2. Build your approved list | 3. Save as approved.rds or approved.yaml | 4. Update the logrx.approved option | 5. You're done. Let's axecute! | Stopping Execution on Unapproved Packages | Example | A Few Words of Caution

Introduction | Framework | Algorithm | Examples | Standard components | No backfill cohorts | Simple backfill cohorts | More complex backfill cohorts | Simulations with backfill cohorts | Investigating single trial data | Investigating simulation results | Limitations | Backwards Compatibility | References

Introduction | Single combination arm | Model specification | Model implementation | Design implementation | Other single agent models | Parallel monotherapy and combination arms | Customizations | Historical arms | Delayed arm opening | Disabling borrowing for arm decisions | Evaluating a fixed data scenario | References

Installation | Getting started | Data | Structure of the model class | Model setup | Logistic model with bivariate (log) normal prior | Advanced model specification | Obtaining the posterior | Plotting the model fit | Escalation Rules | Increments rules | Rules for next best dose recommendation | Cohort size rules | Stopping rules | Simulations | Examining single trial behavior | Simulating from a true scenario | Predicting the future course of the trial | Simulating 3+3 design outcomes | Dual-endpoint dose escalation designs | Dual-endpoint designs with a joint model | Dual-endpoint designs with separate models | References

Introduction | How is this done? | Using knit_print in crmPack | Common customisations | Rendering complex classes | Design | Dose toxicity model | Stopping rule | Escalation rule | Use of placebo | Backfill cohorts | Dose recommendation | Cohort size | Observed data | Starting dose | Restoring console-like behaviour | Accessing the output of knit_print | Providing your own knit_print method | Class coverage

Abstract | Introduction | Framework | Using crmPack | Implementing a CRM trial | Dose escalation with safety and efficacy | Extending crmPack functionality | Summary | Acknowledgments | References

Introduction | Implementation | Ordinal data | The LogisticLogNormalOrdinal class | Model fitting | Rules classes for ordinal models | On the need for a diagonal covariance matrix | Some observations | Environment | References

Introduction | High level usage | Important information for usage | Information for debugging | Worked out example | Alternative: read user code from external file | Note | References

Simulation setting | Design definition | Simulation run

Introduction | Study definition | Incoherence and rigidity | Does the prior make sense? | Final observation | References

Class and slot changes | Naming convention motivation | New classes | Renamed classes | Renamed slots | Moved dose and prob Functions from Slots to Methods | Generate data, define a model and get samples | Dose | Prob | New Random Number Generator settings for the MCMC | New no-argument constructors | Handling of NA or placebo returned as next dose | Evaluation of stopping rules at a specific dose | Further details in class and methods name changes | Classes | Methods | References

Introducing tidy methods to crmPack | Basic approach | Exceptions | Examples | Using tidy crmPack data | Cohort size | Environment

Introduction | Example | Setup | Model Fitting | Tabulation

Introduction | Input and Output | {admiral} Functions and Options | Handling of Missing Values | Floating Point Comparisons | Expressions in Scripts | Quoting and Unquoting: Introducing expr(), exprs(), !! and !!! | expr() and exprs() | Bang-Bang (!!) and Bang-Bang-Bang (!!!) | Summary | Common pitfalls | 1. Mistakenly passing something that isn't an expression to an argument | 2. Forgetting that expressions must be evaluable in the dataset | See also

Introduction | Grading metadata | Structure of metadata set | Handling floating points when comparing numeric values | Creating the Lab Grade | Mapping ADLB to the TERM variable in the {admiral} metadata data set | Deriving Baseline Toxicity Grades | NCI-CTCAEV4 implementation | Terms graded | Updates made to TERM | Assumptions made when grading | NCI-CTCAEV5 implementation | Criteria dependent on the Baseline Value | NCI-CTCAEV6 implementation | DAIDS implementation | Terms with age or sex dependent grading criteria | US (Conventional) unit implementation | Conclusion

Model definition | Linear model | Mathematical Details of Kenward-Roger method | Special Considerations for mmrm models | Derivative of the overall covariance matrix $\Sigma$ | Derivative of the $\Sigma^{-1}$ | Subjects with missed visits | Scenario under group specific covariance estimates | Scenario under weighted mmrm | Contracted linear covariance adjustment | Subject contributions with weights | Contraction of $Q$ | Contraction of $P$ | Inference | Degrees of freedom in contrast space | One-dimensional shortcut | Parameterization methods and Kenward-Roger | Implementation in mmrm | Spatial Exponential Derivatives | Spatial Gaussian Derivatives | References

Model definition | Linear model | Covariance matrix model | Unstructured covariance matrix | Grouped covariance matrix | Spatial covariance matrix | Maximum Likelihood Estimation | Weighted least squares estimator | Determinant and quadratic form | Restricted Maximum Likelihood Estimation | Completing the square | Objective function

Working pharmaRTF Into Your Process | A Small Note | How It Works | Custom Reader Functions | Putting It Together | Further Reading

Satterthwaite degrees of freedom for asymptotic covariance | One-dimensional contrast | Jacobian approach | Jacobian calculation | Connection to the scalar Kenward-Roger shortcut | Multi-dimensional contrast | Satterthwaite degrees of freedom for empirical covariance | Appendix: Trace identities | Cyclic invariance of trace | Trace and squared eigenvalues | References

Scope and Audience | Chapters

Analysis Function Return Values - RowsVerticalSections | Combining RowsVerticalSection Objects | Combining Existing Analysis Functions | Different Analysis Functions For Different Columns | Stacking Analysis Functions | Conditional Stacking | Appendix A - Code For c.RowsVerticalSection

Split Context | Designing Conditional Behavior in afuns | Conditional-On-Column Behavior in afuns | Determining Column-Space Position Within An afun | General Template For Column Aware afun | Using Row Faceting Information Within afuns

Introduction | Declaring Rows And Cell Contents With analyze() | Core Idea | Basic Usage | Declaring Columns With split_cols_by() | Declaring Row-Grouping With split_rows_by() | Adding Marginal Summaries With summarize_row_groups | Some Relevant Details | All Together - A Basic Rectangular Table

Requirements | Workflow | The specifications specs.yml | The filters filters.yml | The functions | The backend machinery | Medical history workflow | Writing custom functions | create example function | Integrate it into the general workflow

Introduction | Nesting | Creating 'Multi-Section' Tables With nested = FALSE | A Brief Note On Removing Sections | Intermediate Nesting | Anchor Resolution | Order Of Intermediate Nesting Placement | Designing Multi-Section Row-Layouts To Support Subgroup Variants

{admiral} Manifesto | Usability | Simplicity | Findability | Readability

Introduction | Programming Flow | Read in Data | Derive Period, Subperiod, and Phase Variables (e.g. APxxSDT, APxxEDT, ...) | Derive Treatment Variables (TRT0xP, TRT0xA) | Derive/Impute Numeric Treatment Date/Time and Duration (TRTSDTM, TRTEDTM, TRTDURD) | Derive Disposition Variables | Disposition Dates (e.g. EOSDT) | Disposition Status (e.g. EOSSTT) | Disposition Reason(s) (e.g. DCSREAS, DCSREASP) | Randomization Date (RANDDT) | Derive Birth Date and Analysis Age (BRTHDT, AAGE, AAGEU) | Derive Death Variables | Death Date (DTHDT) | Cause of Death (DTHCAUS) | Duration Relative to Death | Derive the Last Date Known Alive (LSTALVDT) | Derive Groupings and Populations | Grouping (e.g. AGEGR1 or REGION1) | Population Flags (e.g. SAFFL) | Derive Other Variables | Example Script

Introduction | Where the data comes from | OSV data base - Defined ecosystems | Load the package | Retrieving vulnerabilities directly | Stand-alone assessment | Within a full assessment | How results are reported | Limitations

Overview | Pattern A: autoslider Base Theme | Pattern B: Simple Theme Wrappers | Pattern C: Standalone Formats | How to Apply Your Custom Formats | Example: Visualize the output of your format implementation

Overview | Prerequisite | Speaker notes workflow | Step 1: Initial Slide Generation | Step 2: Adding AI Footnotes | Example: Using a Local LLM | Step 3: Create the Final PowerPoint File | Story slides workflow | Step 1: Generate the content deck | Step 2: Add the AI story | Under the hood | From the MCP server | Appendix | Running Your Local LLM

Overview | Generating a Deck with append_all_slides | Example: Generating placeholder slides | Controlling slide text with metadata tokens | Notes

Overview | List available templates | Generate a template for your own table | Notes | Customize the template

Introduction | Programming Workflow | Read in Data | Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADY, ADTF, ATMF) | Assign PARAMCD, PARAM, PARAMN, PARCAT1 | Derive Results (AVAL, AVALC) | Derive Additional Parameters (e.g. BSA, BMI or MAP for ADVS) | Derive Timing Variables (e.g. APHASE, AVISIT, APERIOD) | Timing Flag Variables (e.g. ONTRTFL) | Assign Reference Range Indicator (ANRIND) | Derive Baseline (BASETYPE, ABLFL, BASE, BNRIND) | Derive Change from Baseline (CHG, PCHG) | Derive Shift (e.g. SHIFT1) | Derive Analysis Ratio (R2BASE) | Derive Analysis Flags (e.g. ANL01FL) | Assign Treatment (TRTA, TRTP) | Derive Categorization Variables (AVALCATy) | Derive Criterion Variables (CRITy, CRITyFL, CRITyFN) | Derive New Rows | Example 1 (Creating a New Record): | Adding a New Record for the Last Value | Adding a New Record for the Minimum Value | Example 2 (Deriving a Summary Record) | Example 3 (Deriving a New PARAMCD) | Assign ASEQ | Add ADSL variables | Example Scripts

Introduction | The criterion | Basic usage | Choosing a test based on the criterion | Empty strata | References

Introduction | Programming Workflow | Read in Data | Derive/Impute End and Start Analysis Date/time and Relative Day | Derive Durations | Derive ATC variables | Derive Planned and Actual Treatment | Derive Date/Date-time of Last Dose | Derive Treatment Dose and Unit | Derive Severity, Causality, and Toxicity Grade | Derive Treatment Emergent Flag | Derive Occurrence Flags | Derive Query Variables | Add the ADSL variables | Derive Analysis Sequence Number | Example Scripts

Introduction | Outputs that can be decorated | Decorating ggplot

Overview | Prerequisites | Example 1: Claude Code as the MCP client | Step 1 — Find the server script path | Step 2 — Register the server | Step 3 — Verify the server is registered | Step 4 — Drive the pipeline conversationally | Step 5 — Use your own data | Example 2: Ollama local model (DeepSeek) for AI notes | Step 1 — Install Ollama and pull a model | Step 2 — Register the server (no API key required) | Step 3 — Ask for Ollama-backed notes | Running R in a Docker container? | Calling the R functions directly | Choosing a provider

Introduction | A Brief Review | Split Function Basics | Default Faceting | Recognizing Non-Full-Factorial Faceting | Excluding Factor Levels | Adding Combination Levels | Nested Faceting On Non-Independent Variables | Facets That Vary Meaning Instead of Data Subset | Combining These Faceting Needs

Methods of Execution | axecute() | log_*() functions | Handling Existing Environments | Command Line Execution | Scripting with logrx

Introduction | Outputs that can be decorated | Decorating ggplot | Decorating grob | Decorating gtable

Empty Value Formatting | Auto Precision | Overall Precision Maximums | External Precision | Parenthesis Hugging

Default Layer Formats | Precision Cap | Overall Precision Maximum | Custom Summaries | Scientific Notation | Quantile Algorithms | IBM Rounding

Introduction | CA-125 response categories | Ways the data is collected in the Ovarian Cancer study CRF | Required: | May be Collected: | Assumptions: | styler: off | styler: on | Required Packages | Programming Workflow | Read in Data | Pre-processing of Input Records | Select Overall Response Records per Response Criteria Category | Partial Date Imputation and Deriving ADT, ADTF, AVISIT etc. | Derive AVALC and AVAL | Analysis Flag Derivation | Flag One Assessment at Each Analysis Date (ANL01FL) | Flag Assessments up to First PD or After Receiving Mouse Antibodies (ANL02FL) | CA-125 Response Evaluable Flag | Select Source Assessments for Parameter Derivations | CA-125 Progression | CA-125 Progression Category | CA-125 Best Confirmed Overall Response | Combined Best Unconfirmed Overall Response | Combined Best Confirmed Overall Response | Other Endpoints

Introduction | Outputs that can be decorated | Decorating listing_df, ElementaryTable, TableTree | Decorating ggplot | Decorating datatables

Introduction | Main Features | A Simple Application | Try it out in Shinylive

Introduction | PCWG3 Guidelines for Prostate Cancer Response | Prostate-specific antigen (PSA) | Bone Response Categories Based on PCWG3 Guidelines: | Progressive Disease Unconfirmed (PDu) | Progressive Disease (PD) | Not Evaluable (NE) | No Evidence of Disease (NED) | Non-Progressive Disease (Non-PD) | Bone Lesion Confirmation Guidance (The 2+2 Flare Rule) | Examples of the 2+2 Rule | Programming Workflow | Required Packages | Read in Data | Pre-processing of Input Records | Partial Date Imputation and Deriving ADT, ADTF, AVISIT, AVISITN etc. | Derive PARAMCD, PARAM, PARAMN | RECIST 1.1 and Bone Response Combined Based on PCWG3 Guidelines | Derive Combined Overall Time Point Response by Investigator (OVRLRESC) Parameter | Table : Combined Overall Time Point Response as per summarized PCWG3 guidelines | Derive Combined Overall Time Point Response by Investigator (OVRLRESC) Records referenced from above table. | Derive AVAL (Numeric tumor response from AVALC values) | Best Overall Response (BOR) and Confirmed Best Overall Response (CBOR) | Define Events for Best Overall Response (BOR) | Derive Best Overall Response (BOR) | Derive Confirmed BOR (CBOR) | PSA Analysis | Derive BASE, CHG, PCHG for PSA (ADPSA) | Define Events for all PSA endpoints | Derive Unconfirmed PSA50 PSA90 (PSAURS) | Derive Confirmed PSA50 PSA90 (PSACRS) | Other Endpoints

Introduction | What goes in a Submission to a Health Authority? | Study Data Standardization Plan | SDTM and ADaM Data Packages | What is {xportr} validating in these Data Packages? | {xportr} in action | Using xportr_metadata() to reduce repetition | Going meta | Warnings and Errors | Setting up our metadata object | xportr_type() | xportr_length() | xportr_label() | xportr_order() | xportr_format() | xportr_write() | xportr() | Future Work

Introduction | You've got options() or xportr_options() | Are we being too verbose? | Type coercion options | Putting it all together

Introduction | Required Packages | Programming Workflow | Read in Data | Define Lookup Tables | Derive Core ADLB Variables | Derive Log Transformation of Biomarkers | Remaining ADLB Set-up | Example Script

Introduction | Creating Neuroscience ADaM Datasets | Support

Introduction | Required Packages | Programming Workflow | Read in Data | Derive Parameters (CNSR, ADT, STARTDT) | Pre-Defined Time-to-Event Source Objects | Creating Your Own Time-to-Event Source Objects | Derive Analysis Value (AVAL) | Derive Analysis Sequence Number (ASEQ) | Add ADSL Variables | Example Script

Introduction | Lugano 2014 Response Categories for Lymphoma | PET-CT Based Response Categories | CT-Based Response Categories | Programming Workflow | Required Packages | Read in Data | Pre-processing of Input Records | Partial Date Imputation and Deriving ADT, ADTF, AVISIT, AVISITN etc. | Derive PARAMCD, PARAM, PARAMN | Derive Combined Overall Timepoint Response(OVRLRESC) Parameter | General Derivation Assumptions Used in This Vignette | Table: Combined Overall Timepoint Response Based on Lugano 2014 Response Categories | Combined Overall Timepoint Response(OVRLRESC) Records referenced from above table | Derive prior evaluable PET-CT response for carry-forward logic | Derive Combined Overall Timepoint Response | Derive AVAL (Numeric tumor response from AVALC values) | Other Endpoints

Common Clinical Trials Analyses | Analytical Functions for rtables | Clinical Trial Visualizations | Interactive Apps | Summary

tern Tabulation | tern Analyze Functions | Internals of tern Analyze Functions | Tabulation Examples | Demographic Table | Adverse Event Table | Statistics Functions | Response Table | Summary

teal application to use scatter plot matrix with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

Coming Soon

Analysis and Group Summary Function Review | General Analysis/Summary Function Structure | Arguments To Analysis Functions | afun Special Arguments: Marginal Counts | Marginal Column and Row Counts (.N_col and .N_row) | Other Marginal Count Special Argument | Further Topics On Creating Custom Analysis Functions

Coming Soon

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Coming Soon

Split Function Behavioral Building Blocks | Existing BBBs provided by rtables | Creating Post Processing BBBs | Illustrative And/Or Useful Examples | Placing Specific Facets First And/Or Last | Pre-sorting Or Pre-pruning Facets Based On Data Sparsity | Creating Preprocessing BBBs

Example | Using decider | Using crmPack | Comparison of parameter posteriors | Monte Carlo uncertainty of the differences | $$\operatorname{MCSE}(\hat\theta_{\text{decider}} -\hat\theta_{\text{crmPack}}) | Comparison of fit | Comparison of model code | decider | crmPack | Conclusion | References

Introduction | Getting Started | Content Information | Summary

Introduction | Demographic Table | Variations on the Demographic Table | Using Layouts | Adverse Events | Adverse Events By ID | Adverse Events By ID and By Grade | Response Table | Time to Event Analysis Table

Introduction | Data Pre-Processing | Creating Helper Functions: Cox Regression Model Calculations | tidy Method for summary.coxph Objects: tidy.summary.coxph | Function to Estimate Interaction Effects: h_coxreg_inter_effect | Function to Extract Effect Information: h_coxreg_extract_interaction | Creating a Helper Function: cached_model | Creating the Analysis Function: a_cox_summary | Selecting Parameters | Constructing the Table

Introduction | Getting Started | Nested Tables | NA Values | Custom Aggregation | Marginal Summaries | Table Decorations | Summary

Formats Precedence | Format Precedence and Inheritance Rules | Standard Format | Cell Format | Parent Table Format and Inheritance | NA Handling | Replacing NA Values at the Cell Level | Parent Table Replacement of NA Values and Inheritance Principles

Disclaimer | Creating an Example Table | Converting the Table to a Result Data Frame (ARD) | Customizing the Output | More Complex Outputs

Introduction | Overview | Data | Building a Table | Quick Start | Layout Instructions | Adding Column Structure | Adding Row Structure | Adding Group Information | Comparing with Other Tabulation Frameworks | Summary

Introduction | Getting Started | Detailed Table Structure | Insights on Value Format Structure | Applications | Summary

Introduction | Traditional Subsetting and modification with [ | Dealing with titles, foot notes, and top left information | Path Based Cell Value Accessing:

tern Formatting Functions Overview | Comparing tern & formatters Formats | Formatting Function Basics | Number of Decimal Places to Display | Value Thresholds | Creating Custom Formatting Functions | Summary

Variable Class Conversion | Including Missing Values in rtables | Missing Values in Numeric Variables

For Developers

Introduction | Required Packages | Programming Workflow | Read in Data | Initial Setup | Assign PARAMCD, PARAM, and PARAMN variables | Map AVAL and AVALC variables | Convert UPSIT Score to Percentile | Derive Criterion Flag Variables for UPSIT Percentile | Remaining Analysis Dataset Set-up | Example Scripts | References

Introduction | Required Packages | Programming Workflow | Read in Data | Initial PET Scan Analysis Dataset Set-up | Create PARAMCD, PARAM, and PARAMN variables | Map AVAL and AVALC variables | ADTPET (Tau PET Scan Analysis Dataset) | ADAPET (Amyloid PET Scan Analysis Dataset) | Convert amyloid SUVR to Centiloid | Derive Criterion Flag Variables for Amyloid Categories | Remaining PET Scan Analysis Dataset Set-up | Example Scripts | References

Introduction | Setting Your PubMed API Key | Load Required Libraries | Total Number of Articles Matching a Term | Number of Articles by Year | Plotting the Article Trend Over Time

Example 1: Recommend a dose for the next cohort | Setting up the data | Structure of the model class | Obtain the posterior | Use ggmcmc to diagnose | Plot the model fit | prior mean curve | Escalation rules | Recommended dose for the next cohort | Example 2: Run a simulation to evaluate operating characteristics | Set up safety window and DADesign to be completed | Set up true curves | Perform the simulations | Interpret the simulation results

Introduction | The dose grid | The dose-toxicity model | The increment rule | The dose selection rule | The cohort size | The stopping rule | Trial definition | Analysing a trial | The single patient run-in | The first full cohort | The second full cohort | The third full cohort | The fourth full cohort | The fifth full cohort | Summarising the trial results | Note | References

Defining the design | The dose grid | The dose toxicity model | The escalation rules | The maximum increment | The NextBest rule for recommending the best dose for the next cohort | The cohort size | The stopping rules | The overall trial design | References

Example

Introduction | Programming Workflow | Metadata | Reference Files for Parameters by Age | Reference Files for HEIGHT | Reference Files for Weight by Length/Height | Initial ADVS Set-up | Derive Additional Variables for Anthropometric indicators | Derived Variables for Parameters by Age | Age Unit Handling and Standardization | Understanding Age Unit Requirements | Common Age Unit Conversion Factors | Method: Manual Age Unit Conversion | Use the standardized age variables | Derived Variables for Weight by Length/Height | Derive Additional Parameters for Anthropometric indicators | Derived Parameters by Age | Derived Weight by Length/Height | Remaining ADVS Set-up | Example Scripts

Introduction | Programming Workflow | Read in Data | Pre-processing of Input Records | Select Overall Response Records and Set Parameter Details | Partial Date Imputation and Deriving ADT, ADTF, AVISIT etc | Derive AVALC and AVAL | Derive Confirmed Response Parameter | Handling Non-Evaluable Responses | Additional Variables | Definition of derive_confirmed_response Function | Check Number of NE Values Between Responses | Analysis Flag Derivation | Flag One Assessment at Each Analysis Visit (ANL01FL) | Exclude Assessments After New Anti-Cancer Therapy (ANL02FL) | Flag Assessments up to First PD (ANL03FL) | Select Source Assessments for Parameter derivations | Derive Progressive Disease Parameter | Define Events | Derive Response Parameter | Derive Clinical Benefit Parameter | Derive Best Confirmed Overall Response Parameter | Other Endpoints

Introduction | Required Packages | Programming Workflow | Read in Data | Merge TR with TU and Derive New Variables | Pre-processing of Input Records | Partial Date Imputation and Deriving ADT, ADTF, AVISIT etc | Unscheduled visits | Derive Parameters for Lesion Diameters (LDIAMn & NLDIAMn) | Derive Parameter for Sum of Diameter and Analysis Flag ANL01FL | Derive Baseline (ABLFL, BASE) | Derive NADIR | Derive Change from Baseline (CHG, PCHG) | Derive Additional Flag Variables | Derive PDFL | 1. Take the date of PD from ADRS | 2. Take the first date when the overall response is PD from RS | 3. Target lesion response of no CR at the current observation visit and but a CR response at the NADIR observation | 4. Calculate from source data | Derive Analysis Flags ANLzzFL | Derive Analysis Sequence Number (ASEQ) | Add ADSL Variables | Example Script

Definition | Implementation | Examples | Example 1 | Example 2 | Example 3 | Example 4 | References

NOTE | Control splitting with provided function (limited customization) | Custom Split Functions | Adding an Overall Column Only When The Split Would Already Define 2+ Facets | Leveraging .spl_context | What Is .spl_context? | Different Formats For Different Values Within A Row-Split | Simulating 'Baseline Comparison' In Row Space

The Old Way | Setting column counts to visible at Layout time | Modifying counts on an existing table | Hiding counts | Higher Level Column Counts | Setting Column Counts to Visible at Layout Time | Manipulating Column Counts In An Existing Table | Setting individual column counts | Setting Col Count Visibility | Advanced Settings

Introduction | Row Splitting

Controlling Facet Levels | Provided Functions | Controlling Combinations of Levels Across Multiple Variables | trim_levels_in_group | trim_levels_to_map | Combining Levels | add_overall_level | add_combo_levels | Fully Customizing Split (Facet) Behavior | An Example Custom Split Function | An Illustrative Example of A Custom Split Function | Overriding the Core Split Function | Design of Pre- and Post-Processing Functions For Use in make_split_fun

Rows and cell values alignments | Top-left Materials | Table Inset | Horizontal Separation | Section Dividers | Indent Modifier | Variable Label Visibility | Cell, Label, and Annotation Wrapping | Cell & Label Wrapping | Title & Footer Wrapping

Introduction | A Table Shell | A Brief Review Of rtables Layouts | Translating | Translating Column Structure | Translating Row Structure | Translating Cell Contents | Spanning Column Headers | Heterogeneous Column Structures (e.g., Risk Difference Columns) | Risk Difference Columns | Column-structure Aware Analysis Functions | Mixed Nesting Levels

Introduction | A Table In Need of Attention | Trimming | Trimming Rows | Trimming Columns | Pruning | Sorting | Sorting Fundamentals | Wildcards in Sort Paths | Sorting Within an Analysis Subtable | Writing Custom Pruning Criteria and Scoring Functions | Useful Functions and Accessors | Example Custom Scoring Functions | Sort by a character "score" | Sort by the Percent Difference in Counts Between Genders in Arm C

Introduction | Building A Table Row By Row | Tabulation With Row Structure | Calculating the Mean | Group Summaries | Pattern III

Titles and Non-Referential Footer Materials | Page-by splitting | Referential Footnotes | Adding Cell- and Analysis-row Referential Footnotes At Tabulation Time | Annotating an Existing Table with Referential Footnotes

Introduction | Creating Pediatrics ADaM Datasets | Support

Introduction | Covariance and Correlation Matrices | Transformation to Variance Parameters | Unstructured (us) | Homogeneous (ad) and Heterogeneous Ante-dependence (adh) | Homogeneous (toep) and Heterogeneous Toeplitz (toeph) | Homogeneous (ar1) and Heterogeneous (ar1h) Autoregressive | Homogeneous (cs) and Heterogeneous (csh) Compound Symmetry | Spatial Covariance Structure | Spatial exponential (sp_exp) | Spatial Gaussian (sp_gau) | References

Introduction | Required Packages | Visit variables (AVISIT, AVISITN, AWLO, AWHI, ...) | Period, Subperiod, and Phase Variables | A Note on Study Specific Code | The Period/Phase Reference Dataset | Example Creation of the Period/Phase Reference Dataset | Creating the Period/Phase Reference Dataset from EX | Creating the Period/Phase Reference Dataset from ADSL | Creating ADSL Period, Subperiod, or Phase Variables | Creating BDS and OCCDS Period, Subperiod, or Phase Variables | Treatment Variables (TRTxxP, TRTxxA, TRTP, TRTA, ...) | Creating ADSL Treatment Variables | Creating BDS and OCCDS Treatment Variables

Background | G-Computation Estimator | Covariance Estimator of $\hat{\theta}_t$ | Implementation

Prediction of conditional mean | Mathematical Derivations | Implementation of predict | Parametric Sampling for Prediction Interval | Prediction of Conditional Mean for New Subjects | Simulate response | Conditional Simulation | Marginal Simulation | Implementation of simulate | Relationship Between predict and simulate Results | predict options | simulate options | Comparison with SAS

Introduction | Dataset Contents | Required Packages | Programming Workflow | Initial set up of ADOE | Assigning PARAM/PARAMCD and AVISIT/AVISITN | Creating IOP pre to post-dose difference derived parameter | Further Derivations of Standard BDS Variables | Example Script

Introduction | Dataset Contents | Required Packages | Programming Workflow | Reading In Data and Setting Up Lookup Tables | Original Items (param_lookup_original) | Transformed Records (param_lookup_transformed) | Composite Records (param_lookup_composite) | Initial Set Up and Mapping the Raw Records | Deriving the Transformed Parameters | Deriving the Composite Parameters | Composite Scores by Category | Overall Composite Scores | Further Derivations of Standard BDS Variables | Example Script

Overview | CRAN | Check if the Package Exists on CRAN | Parse Package Archive HTML from CRAN database | Extract Version Information from the Archive Page | Get All Versions and the Latest Version | Get CRAN package URL source code | Internal R package | Bioconductor package | Github repository | Get R package source code

Introduction | Programming Workflow | Read in Data | Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADY, ADTF, ATMF) | Compute duration for a record | Create 1:1 mapping records | Create Summary Records | Assign PARAMCD, PARAMN, etc. from Reference tables | Derive Categorization Variables (AVALCATy) | Assign ASEQ | Add the ADSL variables | Example Script

Introduction | Programming PK NCA (ADPC/ADNCA) Analysis Data | ADPC Programming Workflow | Read in Data | Expand Dosing Records | Find First Dose | Find Reference Dose Dates Corresponding to PK Records | Combine PC and EX Records and Derive Relative Time Variables | Derive Analysis Variables | Create DTYPE for Imputed records | Create Duplicated Records for Analysis | Combine ADPC data with Duplicated Records | Calculate Change from Baseline and Assign ASEQ | Add Additional Baseline Variables | Add the ADSL variables | Programming Population PK (ADPPK) Analysis Data | ADPPK Programming Workflow | Find First Dose ADPPK | Find Previous Dose | Combine PC and EX Records for ADPPK | Derive Analysis Variables and Dependent Variable DV | Add ASEQ and Remove Temporary Variables | Derive Numeric Covariates | Derive Additional Covariates from VS and LB | Combine Covariates with ADPPK Data | Example Scripts

Introduction | Required Packages | Programming Workflow | Read in Data | Flagging Elevated Values (CRITy, CRITyFL) | Subsetting by LBTESTCD and Joining by Potential Events | How to Create New Parameters and Rows | Conclusion | Example Script

Introduction | Installation | Basic Workflow | 1. Create a Figure | 2. Pick a Layout | 3. Wrap with gridify | 4. Add Text to Cells | 5. Print or Assign | Example with a Table | Saving Your Output | More Resources | A Note on Security and Searchability | Outputs Cannot Be Hand-Edited | PDF Text is Searchable | Conclusion

Introduction | Example Usage | Key Transparency Benefits | Conclusion

Introduction | Examples with Figures | Examples with ggplot2 Figures | Precedence of Operators | Simple Layout Example | Complex Layout Example | Pharma Layout Examples | Pharma Layout Base Example | Pharma Layout Letter Example | Pharma Layout A4 Example | Example with a Base R Figure | Examples with Tables | Example with a flextable | Example with a gt Table | Example with an rtable via flextable conversion | Using the show() Methods | Graphical Parameters | Global and Individual Graphical Parameters Example | Background Colour Example | set_cell() Arguments | Maximum Characters | Position and Rotation | Altering Scales and Adjusting Dimensions | Adjusting the Height of Rows using Global Options | How These Options Work | Examples using Global Options | Vertically Anchoring the Object | Export to PDF, PNG, TIFF, and JPEG | Conclusion

Introduction | Required Packages | Programming Workflow | Read in Data | Derive Parameters (CNSR, ADT, STARTDT) | Pre-Defined Time-to-Event Source Objects | Single Event | Add Additional Information for Events and Censoring (EVNTDESC, SRCVAR, ...) | Handling Subjects Without Assessment | Deriving a Series of Time-to-Event Parameters | Further Scenarios | Derive Analysis Value (AVAL) | Derive Analysis Sequence Number (ASEQ) | Add ADSL Variables

Dependencies Tree | Basic Usage | License Extraction | Check for Conflicting Dependency Versions

Generate the risk report in a specific path | See the ggplot2 package risk report in the web browser | The report includes different types of traceability matrices:

Get package version from CRAN | Get package version from Bioconductor

CRAN download | GitHub commit activity | Bioconductor download

Introduction | Load the Package | Example 1: Use Default Configuration | Example 2: Use Custom Configuration (Strict Code Coverage) | Summary

Package Risk Assessment Summary | Metadata | Documentation Metrics | Dependencies | Test Coverage | CRAN Check Results | Popularity Metrics | CRAN Downloads | GitHub Statistics | Reverse Dependencies (Extended) | Authorship | Hosting Information | Suggested Imports Review | Advanced feature | Traceability Matrix

Setup environment | Run traceability matrices and risk assessment | Structure of traceability matrices | Get coverage summary by risk level | High risk test coverage | Medium risk test coverage | Low risk test coverage | View function type summary | Defunct Functions | Imported functions | Re-exported functions | Experimental functions

Introduction | 1. Assessing a Local Source Package | Step 1: Build your package | Step 2: Upload the package (optional) | Step 3: Run the assessment | 2. Assessing via renv.lock | 3. Assessing a CRAN or Bioconductor Package | Assess the latest version from CRAN | Or a specific version | Summary

Getting Started with xportr | Preparing your Specification Files | xportr_type() | xportr_length() | xportr_order() | xportr_format() | xportr_label() | xportr_write()

Introduction | Step 1 - Run package coverage for a package | What the Report Contains | STF vs NSTF Reporting | STF (Standard Testing Frameworks) | NSTF (Non‑Standard Testing Frameworks) | Multi‑Framework Reports

Important Note | Introduction | Programming Workflow | Read in Data | Pre-processing of Input Records | Select Overall Response Records and Set Parameter Details | Partial Date Imputation and Deriving ADT, ADTF, AVISIT etc | Derive AVALC and AVAL | Flag Worst Assessment at Each Date (ANL01FL) | Flag Assessments up to First PD (ANL02FL) | Derive Progressive Disease Parameter | Derive Response Parameter | Derive Clinical Benefit Parameter | Derive Best Overall Response Parameter | Derive Best Overall Response of CR/PR Parameter | Derive Response Parameters requiring Confirmation | Derive Parameters using Independent Review Facility (IRF)/Blinded Independent Central Review (BICR) responses | Derive Death Parameter | Derive Last Disease Assessment Parameters | Derive Measurable Disease at Baseline Parameter | Assign ASEQ | Add ADSL variables | Example Script

Introduction | Programming Workflow | Read in Data | Pre-processing of Input Records | Select Overall Response Records and Set Parameter Details | Partial Date Imputation and Deriving ADT, ADTF, AVISIT etc | Derive AVALC and AVAL | Flag Worst Assessment at Each Date (ANL01FL) | Flag Assessments up to First PD (ANL02FL) | Select Source Assessments for Parameter derivations | Define Events | Derive Progressive Disease Parameter | Derive Response Parameter | Derive Clinical Benefit Parameter | Derive Best Overall Response Parameter | Derive Best Overall Response of CR/PR Parameter | Derive Response Parameters requiring Confirmation | Derive Non-standard Parameters | Adding a Criterion for Confirmed Clinical Benefit | Considering Non-Standard Response Values | Derive Parameters using Independent Review Facility (IRF)/Blinded Independent Central Review (BICR) responses | Derive Death Parameter | Derive Last Disease Assessment Parameters | Derive Measurable Disease at Baseline Parameter | Assign ASEQ | Add ADSL variables | Example Script

Motivation | Setting an option | Options used in a teal application | teal.bs_theme (bslib::bs_theme object) | teal.load_nest_code (character) | teal.threshold_slider_vs_checkboxgroup (numeric) | teal.basic_table_args (basic_table_args object) | teal.ggplot2_args (ggplot2_args object) | teal.plot_dpi (integer value 24 or larger) | teal.log_layout (character) | teal.log_level (character) | teal.show_js_log (logical) | teal.lockfile.mode (character) | teal.sidebar.position (character) | teal.sidebar.width (numeric) | teal.reporter.nav_buttons (character) | teal.reporter.rmd_output (character) | teal.reporter.rmd_yaml_args (character) | teal.reporter.global_knitr (list) | teal.reporter.max_request_size (numeric) | teal.show_src (logical) | teal.enable_deep_linking (logical) | teal.snapshot_manager.enable (logical) | Deprecated options | teal_logging | teal_show_js_log | teal.ui.class (character(1))

Introduction | Outputs that can be decorated | Decorating ElementaryTable | Decorating ggplot | Decorating grob | Decorating datatables | Decorating patchwork

What this package is for | Choose a dataset | Choose dataset and columns | Add a value filter (levels or ranges) | Define choices and selections | Defaults | Static choices | tidyselect helpers | Functions | Use of multiple to select more than one columns at once | Choices and Selection Summary | Trying these patterns inside teal | Writing your own module | See also

Introduction | Minimal Shiny app | Notes

Introduction | Step-by-step Guidance to Create a Package Extension | Lessons Learned

Introduction | Required Packages | Example Data | Original Items | Transformed Items | Scales and Scores | Time to Deterioration/Improvement | Time to Confirmed/Definitive Deterioration/Improvement | Worst/Best Answer | Completion

Example of using tern.rbmi | The Data | Creating intercurrent event data | Draws | Define key variables | Impute | Analyze | Pool | Create output

Introduction | An Illustrative Example | Row Paths | Column Paths | Uniqueness of Paths | Wildcards In Paths | Operating On Tables With Paths | Column Counts | Section Dividers | Sorting Within An rtables Table | Extracting And Modifying Values | Footnotes | Table Structure And Technical Details | Row Structure | Label And Group Summary Rows | Column Structure

1 Retrieved dropout models in a nutshell | 2 Data simulation using function simulate_data() | 3 Estimators based on retrieved dropout models | 4 Implementation of the defined retrieved dropout models in rbmi | 4.1 Basic MAR model | 4.2 Retrieved dropout model 1 (RD1) | 4.3 Retrieved dropout model 2 (RD2) | 4.4 Brief summary of results | References

Introduction | How to Transform outputs? | Requirements and Limitations | Building Output Transformations (Decorators) | Server | UI | Variable Names as Arguments | Using Output Transformations (Decorators) | Single Transformation (Decoration) | Module | Application | Transforming Multiple Outputs (Decorators) | Transformators | Convenience | Reducing Boilerplate | Multiple Transformations

Introduction | How to use the @caption, @info and @code tags together in a "block" | How to sequence your blocks | Data sections

Introduction | Usage | teal.bs_theme R option | Bootstrap version and themes | Default Bootstrap theme | Reset the Bootstrap theme | Theme not updated | Custom teal CSS | Bootstrap NULL vs Bootstrap 3 | Regular shiny::fluidPage | Interactive theming guide | Set overall app theme | Customize a bootswatch theme | Apply the customized theme | Try the above app in shinylive | Sources

Built-in Summaries | Notes About Built-in's | Custom Summaries

Population Data in the Denominator | Denominator Grouping | Count layers | Shift layers | Controlling the Denominator Filter | Missing Counts | Missing Subjects | Adding a 'Total' Row

Basic Templates | Templates With Parameters | Viewing and Removing Templates

A Basic Example | Filling Missing Groups Using Factors | Where to go from here

Introduction | Default Logging Setup of teal.logger | Customizing the Log Level | Customizing the Log Layout | Customizing the Log Destination | Logging in Other teal.X Packages | Example of Usage

Data Upload | Table Generator | Population Explorer | Individual Explorer | Recap & what's next?

Introduction | Derivations | Creating ADaM Datasets | Support

Introduction | Creating Oncology ADaM Datasets | Support

Introduction | Derivations | Input and Output | Starting a Script | Support

Generating minimal data to test teal.modules.clinical | Setup & Helper Functions | ADSL | ADAE | ADAETTE | ADCM | ADEG | ADEX | ADLB | ADMH | ADQS | ADRS | ADTTE | ADVS | Generate Data

Introduction | Required Packages | Characterization of Derivations | Overview of Derivations | Source Data | Single Source Dataset | Multiple Source Datasets | Methods | Selection | Options for Selection | Sort Order | New Values | Summary | Computed

Weighted Least Square (WLS) Empirical Covariance | Difference of Implementations | Proof of Identity | Proof for Covariance Estimator | Proof for Degrees of Freedom | Special Considerations in Implementations | Pseudo Inverse of a Matrix | Avoiding the Crossproduct of the G Matrix | References

Introduction | Package Structures | data | data-raw | design | SAS | TMB | inst | man | NAMESPACE | NEWS.md | R | README | simulations | src | chol_cache.h | covariance.h | derivatives.h | empirical.cpp | exports.cpp | jacobian.cpp | kr_comp.cpp | Makevars | mmrm.cpp | predict.cpp | test files | tmb.cpp and tmb_includes.h | utils.h | tests | vignettes | Other files | _pkgdown.yml | .gitignore | .lintr | .pre-commit-config.yaml | .Rbuildignore

Overview of TLG Programming | Prep environment | titles and footnotes | Column metadata | Process data | Generate results | Output results | Listing programming | Graph programming | Metadata method | By processing

Introduction | Basic tbl data frame | Formatting table | Add new row | Add indentation | Enable bold font | Formatting functions

Symbols | Superscripts and Subscripts | Inline RTF Line Breaks

Introduction | Counts: statlist = statlist("n") | Big N row, counts and percentages: statlist = statlist(c("N", "n (x.x%)")) | Using factor to order category summary | Counts, denominators, and percentages: statlist = statlist(c("n/N (x.x%)")) | By processing: statlist = statlist(c("N", "n (x.x%)")) | Padding factor levels on and off | Denominator Dataframe | By processing and denominators: statlist = statlist(c("n (x.x)"), denoms_by = "TRT01AN") | Available statlist formats for frequency summary | Nested Frequency Analysis

Introduction | Customizing Univariate Statistics | Decimal Precision | Data Driven Precision

Introduction to Type I/II/III Hypothesis Testing | Contained Effect | Type II Hypothesis Testing | Type III Hypothesis Testing | Hypothesis Testing in SAS | Special Considerations | Reference Levels | Example of Reference Levels | Why Model Covariate Order Changes My Testing in SAS? | Why mmrm Gives More Covariates Than SAS? | Excluding columns due to collinearity | Intercept-free models | Intercept-free Models with stats::model.matrix() | Intercept-free Models with PROC MIXED | Type-II Contrast Matrices in Intercept-free Models

Introduction

Introduction | Who Are Your Numbers And What Do They Do? | There's Counting And Then There's Counting... | Denominators | "Yeah But What If" ... Conditionality In afuns | Conditionality Based On Column Facet | Conditionality Based On Row Facet | Why Not Both? | A Final Note On afun Complexity

Scope and Audience | Chapters

Introduction | Figures | Multiple Figure Delivery with a for-loop and sprintf | Tables | For PDF/DOCX | Splitting By Rows for PDF | Using the paginate_table() Helper Function (Recommended) | Export To PDF, JPG and PNG | Multi-page outputs. | 1. Export a single image | 2. Export multiple images | Extending The Multi-Page Example | Long Table in HTML

Introduction | Reporter Modules | Module Overview

Introduction | Programming Workflow | Read in Data | Pre-processing of Input Dataset | Merge FACE with EX | Merge Required ADSL Variables Needed for Analysis | Derive Fever Records from VS Domain | Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADTF, ATMF, ADY) | Derive Period Variables (e.g. APxxSDT, APxxEDT, ...) | Derive Direct Mapping Variables | Derive AN01FL | Derive Severity Records for Administration Site Events | Derive Maximum Records | Assign PARAMCD, PARAM, PARAMN, PARCAT1 | Derive Maximum Severity Flag | Derive Event Occurrence Flag | Post-processing of the Dataset | Add ADSL variables | Example Script

Introduction | Programming Flow | Read in Data | Derive Period, Subperiod, and Phase Variables (e.g. APxxSDT, APxxEDT, ...) | Derive Treatment Variables (TRT0xP, TRT0xA) | Derive/Impute Numeric Treatment Date/Time and Duration (TRTSDTM, TRTEDTM, TRTDURD) | Population Flags (e.g. SAFFL) | Derive Vaccination Date Variables | Create Period Variables (Study Specific) | Derive Other Variables | Add Labels and Attributes | Example Script

Introduction | Layout Basics | Example Layout | Nrow and Ncol | Heights and Widths | Adjusting Heights automatically | Margin argument | Background colour | Adding the Object argument | Adding the Scales argument | Adding cells | Row and Column positions of Cells | Maximum Number of Characters per Line for a Cell | Default Graphical Parameters | Default cell text | Adding a Watermark

Filter Panel API | Getting and setting filter states | 1. Setting the filter state | 2. Updating filter states. *Works only in the shiny reactive context. | 3. Getting the filter state | 4. Removing filter states | 5. Clearing the filter state | Controlling settings of the filter panel | Filter panel as a module

Introduction | The Basics | NSE Principle | Replace an object name | What about dataframe names? | In a nutshell | rtables | Direct use of substitute | substitute in a function | Chain expressions in a pipeline | Including pre-processing

Basic AUTOS Configuration | Working Example: Single Script Library | Loading and Using Auto-Sourced Functions | Multiple Script Libraries | Using Multiple Libraries | Understanding Function Conflicts and the Overwrite Parameter | Quick Example of Function Conflicts | Environment-Specific Auto-Sourcing | How Auto-Sourcing Works | Technical Details of Conflict Handling | Benefits of Auto-Sourcing | Common Use Cases | Project Utilities | Environment-Specific Functions | Team Libraries | Managing Function Conflicts with the Overwrite Parameter | Default Behavior: Overwrite = TRUE | Conservative Behavior: Overwrite = FALSE | Understanding Conflict Detection | Function Metadata Tracking | How Metadata Tracking Works | Accessing Function Metadata | Benefits of Metadata Tracking | 1. Debugging Function Issues | 2. Audit Trail | Metadata and the detach_autos() Function | Best Practices

Understanding Path Configuration | Configuration Structure Levels | Level 1: Execution Environment | Level 2: Paths and Autos | Level 3: Specific Configuration | Working Example | Loading and Using the Configuration | Accessing Your Configured Paths | How It Works | Benefits | Next Steps

Advanced Path Resolution | The Problem: Moving Data | Solution: Multiple Path Locations | Working Example Setup | Understanding the Configuration | Using read_path() for Smart File Location | Path Search Order | Controlling Search Order with envsetup_environ | Environment-Specific Path Resolution | Production Path Resolution | Practical Usage Pattern | Benefits of Dynamic Paths | Common Patterns | Development-First Search | Production-Only Search | Next Steps

Multiple Environment Configuration | The Challenge | Environment-Specific Configuration | Working Example | Loading Different Environments | Default Environment | QA Environment | Production Environment | Configuration Inheritance | Environment Selection Strategies | 1. Environment Variable | 2. Programmatic Selection | Best Practices | Next Steps

Getting Started | Code Maintenance | Update Cached Data

License | Introduction | Required Packages | Programming Workflow | Read in Data | Derive Core ADVS Variables | Original Items | Derive the four Subscales | Remaining ADCOEQ Set-up | Example Scripts

Introduction | Required Packages | Programming Workflow | Read in Data | Derive Core ADVS Variables | Create PARAMCD, PARAM, PARAMN, PARCAT1 variables | Derive Parameters for Metabolic indicators | Derive BMI | Derive waist hip-ratio | Derive Variables for Metabolic indicators | Derive Categorization Variables (AVALCATy, BASECATy) | Derive Criterion Variables (CRITy, CRITyFL, CRITyFN) | Remaining ADVS Set-up | Example Scripts

Introduction | Required Packages | Programming Workflow | Read in Data | Define Lookup Tables | Derive Core ADLB Variables | Derive Visit Information | Derive Metabolic Parameters | Add variables required for derivation | Derive HOMA-IR index | Derive FLI score | Remaining ADLB Set-up | Example Scripts

Dear Developers | Why have a separate development package? | How to add new tools to {admiraldev}? | When to add a function to {admiraldev}? | Scenario One: {admiral} core | Scenario Two: {admiral} extension

Overview | Step 1: Write your template | Step 2: Create a softlink in inst/templates/ | Step 3: Verify with list_all_templates() | Step 4: Generate with use_template()

Introduction | Datasets | FEV Data | BCVA Data | Model Implementations | Ante-dependence (heterogeneous) | PROC GLIMMIX | mmrm | Ante-dependence (homogeneous) | Auto-regressive (heterogeneous) | gls | Auto-regressive (homogeneous) | glmmTMB | Compound symmetry (heterogeneous) | Compound symmetry (homogeneous) | Spatial exponential | Toeplitz (heterogeneous) | Toeplitz (homogeneous) | Unstructured | lmer | Benchmarking | Convergence Times | Marginal Treatment Effect Estimates Comparison | Impact of Missing Data on Convergence Rates | Session Information

data_extract_spec | Step 1/4 - Preparing the Data | Step 2/4 - Creating a data_extract_spec Object | Step 3/4 - Creating the shiny UI and Server Modules | Step 4/4 - Creating the shiny App | Try it out in Shinylive

1: Safely set autos

Generating a log | axecute() | logrx addin: | Log attributes

Advanced Customizations | Column Widths | Pagination | Horizontal separators (note section_div = )

log.rx.lint | log.rx.approved

Introduction | Required Packages | Call Derivation | Restrict Derivation | Slice Derivation | Combining Higher Order Functions

Common R CMD Check Issues | Check Fails Only on One Version | Package Dependencies | Global Variables | Undocumented Function Parameter | Outdated Documentation

Introduction | Data Extraction and Data Merging

Introduction | Creating data in-app | Modification of data in-app | WARNING | Extending existing teal_data_modules

Introduction | Example

Introduction | Information for users of the filter panel in applications | Information for R developers using the filter panel

Introduction | Main features | Example application | Try it out in Shinylive

teal application to use association plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use bivariate plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use cross table with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to display data table with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to analyze and report outliers with various datasets types. | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use regression plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use response plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use scatter plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

Additional topics | Loading libraries and data

Example - Single-Table Module

Introduction | Programming Workflow | Read in Data | Combine IS with SUPPIS | Derive Timing Variables | Derive ADT and ADY Variables | Parameters Derivation | Derive PARCAT1 and CUTOFFx Variables | Derive AVAL, AVALU and DTYPE Variables | Derive BASE Variables | Derive CHG and R2BASE Variables | Derive CRITx Variables | Derive TRTP/A Variables | Derive PPS Record Level Flag Variable | Add ADSL Variables | Example Script

Introduction | Asymptotic Covariance | Empirical Covariance | Jackknife Covariance | Bias-Reduced Covariance | Kenward-Roger Covariance

Introduction | Referential Footnotes Workaround | Summary

Introduction | Programming workflow | Read in data | Create oak_id_vars | Read in CT | Map Topic Variable | Map Rest of the Variables | Repeat Map Topic and Map Rest | Create SDTM derived variables | Add Labels and Attributes

Introduction | Raw data | Programming workflow | Read in data | Create oak_id_vars | Read in CT | Map Topic Variable | Map Rest of the Variables | assign_no_ct | assign_ct | assign_datetime | hardcode_ct and condition_add | hardcode_no_ct and condition_add | condition_add involving target domain | condition_add involving raw dataset and target domain | Repeat Map Topic and Map Rest | Create SDTM derived variables | Add Labels and Attributes

Introduction | Main features | A simple application

Reproducibility of teal_data objects | Verification | Verification status | Verification process | verified | unverified | Retrieving code | Tracking object dependencies

Introduction | Programming Flow | Read in Data | Create DCUT Dataset | Postprocess DCUT

Introduction | Example

Introduction | Example

Main Concept of Data cut process | Data cut approaches for different SDTM | Technical approach within | Data Handling Rules | Validation | Starting a Script

Introduction | Programming Flow | Read in Data | Create DCUT Dataset | Preprocess Datasets | Specify Cut Types | Patient Cut | Date Cut | DM Cut | Apply Cut | Output Final List of Cut Datasets

Introduction | Programming Flow | Read in Data | Create DCUT Dataset | Preprocess Datasets | Process data cut

Variable Listing | Missing Variables | Deprecated/Superseded

Variable Listing | Missing Variables | Deprecated/Superseded

Introduction | Default Formatting in as_listing | Column-Wise Formatting in as_listing | Adding Formatted Columns to a Listing via add_listing_col | Summary

Introduction | Descriptive Statistics | Methodology | Statistical model | Estimation | Inference | Covariance structure | Model fitting | Setup | Model fitting via tern.mmrm | Model fitting manually in R | Model fitting via SAS | Diagnostics | Residual analysis | Outlook

Standards Metadata | Study Definition Metadata | Study SDTM Mappings Metadata (specifications)

Introduction | Programming Flow | Read in Data | Pre-processing of Input Dataset | Create Reference Dataset for Periods | Derivation of Analysis Dates | Join with the Periods Reference Dataset and Derive Relative Day in Period | Creation of Analysis Version for GRADING Variable (Either TOXGR or SEV) | Creation of Analysis Sequence Number | Final Step : Get All the Remaining Variables from ADSL | Example Script

Core Concept | List of Algorithms | Sub-algorithms

Introduction | Creating Conditioned Data Frames | Simple Example | Usage in SDTM Domain Derivations | Example with Concomitant Medications (CM) Domain

Introduction | Multiple inputs | Format variations | Multiple alternative formats | Parsing of date or time components | Allowing alternative date or time values | Changing reserved format characters

Setup | Relational srcref data | Linking covr traces to package object srcrefs | Linking unit tests to evaluated covr traces | Relational documentation data | Summary

Setup | Preparing Graph Data | Create Edges of Our Test Path | Plotting Our Test Paths

What are srcref objects? | Extracting relevant traceability srcrefs | Setup | Functions for extracting srcrefs | Extracting package namespace object srcrefs | Extracting test srcrefs | Extracting trace srcrefs | Summary

Acknowledgments | Installation | BioConductor | GitHub | Introduction | Importing Data | Importing a SummarizedExperiment | Importing an ExpressionSet | Importing a Matrix | Annotations | Connection to Database | Querying and Saving Annotations | Quality Control Flags | Automatic Gene and Sample Flagging | Manual Sample Flagging | Accessing Flags | Filtering Data | Based on Default QC Flags | Based on Custom Variables | Normalizing Counts | Descriptive Plots | Simple Plots | Top Genes | Heatmap of Genes among Samples | Correlation between Samples | Principal Components | PCA of Samples | Correlation with Sample Variables | QC Report Template | Differential Expression | Summary | Session Info

Introduction | Programming Workflow | Read in Data | Pre-processing of Input Records | Select Overall Response Records and Set Parameter Details | Partial Date Imputation and Deriving ADT, ADTF, AVISIT etc | Derive AVALC and AVAL | Flag Worst Assessment at Each Date (ANL01FL) | Flag Assessments up to First iCPD (ANL02FL) | Select Source Assessments for Parameter derivations | Define Events | Handling Different Input Response Values | Derive Confirmed and Unconfirmed Progressive Disease Parameter | Derive Response Parameter | Derive Clinical Benefit Parameter | Derive Best Overall Response Parameter | Derive Response Parameters requiring Confirmation | Other Endpoints

Introduction | Programming Workflow | Read in Data | Single Source | Multiple Sources - Prerequisite Steps | Prerequisite | Multiple Sources - Derive New Anti-Cancer Start Date and Merge with ADSL | Using the New Anti-Cancer Therapy Date | Derive Date Parameter

Introduction | Common Clinical Trials Analyses | Interactive Apps | Data Requirements | Summary

Introduction | Basics of Pagination | Alternative Methods to Specify Page Size | 1. pg_width and pg_height | 2. lpp and cpp | 3. colwidths | Pagination with Exporters | export_as_txt | export_as_rtf | Pagination by Parameter | Combining Pagination by Parameter with Regular Pagination | Combining Pagination by Parameter with export_as_txt | Summary

Introduction | Building a Listing | Key Columns | Titles and Footers | Summary

Introduction | Ad-hoc module example | UI function | Server function | App function | Testing it

Motivation | File name - character | XPT | FDA | NMPA | PMDA | File name - length | Variable name | Variable length | Label character | Label length | Values character | Values length | Format | Type | File size

Introduction | The Formatting Map Structure | Calling reformat | Example | Rule Attributes

A Simple Example | Distinct Versus Event Counts | Nested Count Summaries

Build a tplyr_meta object | Building your own metadata table | Anti-joins | Appending Existing Tplyr Metadata | Metadata Without Tplyr | Tying it Together

Format Strings | General Formatting | Controlling Formatting | Lowercase 'x' | Uppercase 'X' | Auto-precision

Generating the Metadata | Extracting The Input Source | Extracting a Result Cell's Metadata | Anti Joins

String wrapping | Row Masking | Collapsing Row Labels | Leading Spaces in HTML Files | Conditional Formatting | Extracting a Format Group | Highly Customized Sort Variables | External Data Formatting

Table Parameters | Additional Treatment Groups | Population Data | Data Completion | Where to Go From Here

How Tplyr Works | The tplyr_table() Object | The tplyr_layer() Object | Adding Layers to a Table | A Note Before We Go Deeper | Constructing Layers | String Formatting in Tplyr | Layer Types | Descriptive Statistic Layers | Count Layers | Shift Layers | Where to go from here? | References

Introduction | Initialization | Class Description | FilteredData | FilteredDataset | FilterStates | FilterState | FilterStateExpr | teal_slice | Making a reproducible filter call | Overview | Example | Filter panel modules

1 Introduction | 2 Data and model specification | 3 Reference-based conditional mean imputation - frequentist inference | 3.1 Draws | 3.2 Impute | 3.3 Analyse | 3.4 Pool | 4 Reference-based conditional mean imputation - information-anchored inference | 4.1 Draws | 4.2 Imputation step including calculation of delta-adjustment | 4.3 Analyse | 4.4 Pool | References

formatters | Formatting values | Format labels | Table Rendering Framework

Introduction | Scenario - PFS and OS as Co-primary Endpoints | Type I Error - Simulation Under $H_0$ | Sample size and power calculation - simulation under $H_1$ | References

Common usage | Common customizations | Extraction of model features | Lower level functions | Hypothesis testing | Tidymodels | Acknowledgments | References

Using the package | Data checks | Relationship to other tools and data standards | Package maintenance and release schedule | Contributing

Introduction | Cor(PFS, OS): Statistical Background | Example: Computing Cor(PFS, OS) directly | Estimating Cor(PFS, OS) from data | Example: Estimating Cor(PFS, OS) from data | References

Introduction | Simulation Specifications | Application | Output Data | References

Introduction | Overall survival calculation | Implementation

Introduction | Structure of the Query Dataset | Variables | Required Content | Input | Output | Example | Query Dataset (ds_query) | Adverse Event Dataset (ae) | Output Dataset | Optional Content | Output Variables

Introduction | TLG-functions | preprocessing | postprocessing | Example AET02 | chevron_tlg object customization | Custom chevron_tlg object creation

Introduction | Using a chevron-defined object | With a modified chevron object

General definition | MMRM special case | Example | Differences compared to SAS | References

Abstract | The basic linear mixed-effects model | Extending the basic linear mixed-effects model | The MMRM as a special case | Missing data | References

Background | Working Collaboratively | GitHub | Package Dependencies | Existing Checks | Good Practices | Example Check | Additional Considerations

Setting Up | Documentation | Metadata | Running a Check | Running Many Checks | Writing Out Results | Making a Customizable Script

Introduction | ADSL | ADLB | Other Basic Data Structures | Required Variables | TRTORD | AVISITCD | AVISITCDN | AVALU | LBSTRESC | LOQFL | BASE2 | CHG2 | PCHG2 | Additional Variables | AVALL2, BASEL2, BASE2L2

PARAMCD | Pull down menus | AVISIT | The Data Constraint Filter | Data Point Brushing | Log2 - Points to Consider | Right Hand Data Filter Panel | Visualization Specifics

Introduction | Set up packages and data | Install packages | Generate the data | Fitting the models | Bootstrap the estimated parameters | Exporting to Excel | The Excel model | Exported data tab | Extrapolations tab

Introduction | Set up packages and data | Install packages | Generate the data | Fitting the models | Bootstrap the estimated parameters | Converting to STEM format | Exporting to Excel | The Excel model | Exported data tab | Stat. Parameters tab | Extrapolations tab

Introduction | Set up packages and data | Install packages | Generate the data | Fitting the models | Bootstrap the estimated parameters | Exporting to Excel | The Excel model | Results ignoring correlation | Results accounting for correlation

Introduction | Basics | Contribution Model

Overview | Simple Example

Plot Themes | Risk Table Themes

Introduction | Main Features | A Simple Example

Analysis Model | Example of use | References

User Guide: Dynamic Borrowing with psborrow | Install the package | Simulate covariates | Survival | Bayesian analysis | Posterior summary statistics | Package Options

Introduction | Read in the data | Fitting models | Converting to a STEM format | The output of convSTEM | stem_param | stem_cov | stem_modsum | stem_boot | Details on conversions | Conversion of parameters | Conversion of Exponential models | Seperate models | Common shape models | Excel formula | Conversion of Weibull models | Conversion of Gompertz models | Conversion of Log-logistic models | Conversion of Log-normal models | Conversion of Generalized Gamma models | Conversion of Gamma models | Conversion of AIC and BIC

Introduction | Set up packages and data | Install packages | Read in the data | Exploratory analysis | Fitting the models | Estimate survival from the models and plot the curves | Adressing uncertainty - Bootstrapping | Estimating quantities from the sample | Outputing parameters to excel | References

Introduction | Set up packages and data | Install packages | Read in the data | Bootstrapping the endpoints | Fitting the models to the original data | Ignoring correlation between endpoints | Considering correlation between endpoints | Plotting impact of accounting for correlations between endpoints

Introduction | Properties of the distributions | Special cases | Initial diagnostics | Exponential | Separate models | Common shape models | Weibull | Independent shape models | Gompertz | Log-logistic | Log-normal | Gamma | Generalized gamma | Generalized F | References

1 Introduction | 2 Data simulation using function simulate_data() | 3 Handling of observed post-ICE data in rbmi under reference-based imputation | 4 Efficiently changing reference-based imputation strategies | 5 Imputation under MAR with time-varying covariates | 6 Custom imputation strategies | 7 Custom analysis functions | 8 Sensitivity analyses: Delta adjustments and tipping point analyses | 8.1 Simple delta adjustments and tipping point analyses | 8.2 More flexible delta-adjustments using the dlag and delta arguments of delta_template() | References

1 Introduction | 2 The Data | 3 Draws | 4 Impute | 5 Analyse | 6 Pool | 7 Code

1 Scope of this document | 2 Introduction to estimands and estimation methods | 2.1 Estimands | 2.2 Alignment between the estimand and the estimation method | 3 Statistical methodology | 3.1 Overview of the imputation procedure | 3.2 Setting, notation, and missing data assumptions | 3.3 The base imputation model | 3.4 Imputation step | 3.5 \(\delta\)-adjustment | 3.6 Analysis step | 3.7 Pooling step for inference of (approximate) Bayesian MI and Rubin’s rules | 3.8 Bootstrap and jackknife inference for conditional mean imputation | 3.9 Pooling step for inference of the bootstrapped MI methods | 3.10 Comparison between the implemented approaches | 4 Mapping of statistical methods to rbmi functions | 5 Comparison to other software implementations | References

Creating A New Validation Packet | Tutorial | Add Requirements | Change Log | Testing | Test Cases | Test Code | Authoring Validation Reports | Running a Validation Report | Validation Mode: Running on Source

Introduction | Simple Dataset | Adding Multiple Variables | Variable Dependencies | Configurable Arguments | Injecting Missing Data | Scaffolding | Compare dplyr

Introduction and Motivations | Recipes | Table Recipes | Scaffold Tables and Scaffolding Join Recipes | Constructing Missingness Recipes | Constructing Data with Recipes | Individual Table

Creating A New R Package | Tutorial | Add Requirements | Package Development | Change Log | Testing | Test Cases | Test Code | Authoring Validation Reports | Running a Validation Report | Validation Mode: Running on Source | Validation Mode: Generating validated bundle for distribution | Validation Mode: Validating and installing package | Validation Mode: Re-validating an installed package

Introduction | A Small Note | Understanding Document Orientation Within pharmaRTF | Multi-level Column Headers | Minimizing Cell Padding | Setting Spacing Between Column Headers and Titles | Split Titles | Special Title/Footnote Formats | PAGE_FORMAT: | DATE_FORMAT: | FILE_PATH: | Further Reading

Notes | Introduction | A Small Note | Huxtable Basics | Changing Table Shape | Column Headers | Cell Formatting | Cell Merging | Some Final Notes | Further Reading

Version 0.1.1 updates | What is pharmaRTF? | A Small Note | The Basics | Further Reading