Create Baseline Characteristic Table | Build a metadata | Analysis preparation | Format the numbers | Output as RTF | Create Baseline Characteristic Table for Subgroup Analysis | Format output

Step 1: Define design parameters | Step 2: Run sim_gs_n() | Step 3: Summarize simulations | References

Overview | Randomization | Enrollment | Time-to-event and time-to-dropout | Generating a trial | Cutting data for analysis | Generating a counting process dataset | Logrank and weighted logrank testing | Simplification for 2-arm trials

Introduction | Defining the test | Executing for a single dataset | Generating test statistics with sim_fixed_n() | Generating data with sim_pw_surv() | Using survival data in another format | Simulation | References

Step 1: Simulate time-to-event data | Scenario a) The simplest scenario | Scenario b) Differential dropout rates | Scenario c) Stratified designs | Scenario d) Multi-arm designs | Step 2: Cut data | Step 3: Run tests | Step 4: Perform the above single simulation repeatedly | Step 5: Summarize simulations | References

Overview | Scenario definitions | A scenario that generates a discrepancy

Introduction | RMST vs. logrank | Estimation of RMST in a single arm at a single time point | $$\hat{S}(t_i) | \prod_{j=1}^{i}\left(1-\frac{d_{j}}{Y_{j}}\right)$$Based on the definition and formula above, $\text{RMST}(\tau)$ can be estimated by$$\widehat{\text{RMST}}(\tau) | \int_{0}^{\tau} \hat{S}(t) d t | The standard error of $\widehat | \widehat{\text{Var}}(\widehat{\text{RMST}}(\tau)) | \sqrt | \int_{t_i}^{\tau} \hat{S}(t) d t | Estimation of RMST differences in 2 arms at a single time point | References

Step 1: Define design parameters | Step 2: Run sim_fixed_n() | Step 3: Summarize simulations

Overview | Hover label in function prepare_boxly(): | Hover label in function boxly(): | Example 1: Add hover label of outlier | Example 2: Choose hover label of box | Example 3: Cutomize label of outlier and hover label of box

Overview | Packages used | Enrollment rates | Failure and dropout rates | Fixed design | Group sequential design | Simple efficacy bound definition | Two-sided testing | Symmetric 2-sided bounds | Asymmetric 2-sided bounds | References

Build metadata | Metadata for baseline characteristic table | A metadata of the AE subgroup specific analysis | Customization (Optional) | Build a reactable | Baseline characteristic table + Participants With Drug-Related AE | Baseline characteristic table + Participants With Serious AE

Create Exposure Duration Table | Build a metadata | Analysis preparation | Format output | Output as RTF

Create Exposure Duration Histogram | 1. Build metadata | 2. Analysis preparation | prepare_exp_duration() | extend_exp_duration() | plotly_exp_duration()

Build a metadata | Interactive Disposition

Overview | Highlighted features | Workflow | Input

Overview | Build metadata | Generate an AE specific subgroup table

Overview | Define metadata | Prepare a mock table

Overview | Define metadata | Select columns | Add a column for risk difference inference | Add a column for average event duration | Add a column for event frequency

Overview | Define metadata | Select columns

Overview | Define metadata | Filter rows | Sort rows | Filter and sort rows

Overview | Step 1: Define metadata | Step 2: Generate the AE listing table

Overview | Generate an AE summary table | Step 1: Define metadata | Step 2: Generate the RTF table

Overview | Generate an AE-specific table | Step 1: Define metadata | Step 2: Generate the static AE specific table

Overview | Highlighted features | Workflow | Step 1: Define metadata | Step 2: Prepare the analysis data | Step 3: Format the results | Step 4: Create the table

Overview | Step 1: Define metadata | Step 2: Generate an interactive AE listing table

Overview | Define metadata | Define multiple analysis plans in one object | Build metadata once | AE output 1: Static AE summary table | AE output 2: Static AE-specific table | AE output 3: Interactive AE listing table | AE output 4: Interactive AE forest plot | Summary

Overview | Generate an AE summary table | Step 1: Define metadata | Step 2: Generate the GT table

Overview | Procedure to generate an AE specific table in gt format | Step 1: Define metadata | Step 2: Generate the AE specific table

EAER formula explanation | EAER formula | EAER for different types of AEs | Any AE | Serious AEs | Drug-related AEs | Calculate EAERs

General Use | Step 1: build your metadata | Step 2: adjusting column widths | Multiple, Long Treatment Labels Example | Step 1: build example metadata

Step 1: build your metadata | Step 2: customize the AE specific columns

Step 1: build your metadata | Step 2: toggle risk difference columns

Create Disposition Table | Example data | Build a metadata | Analysis preparation | Format output | RTF tables

Step 1: build your metadata | Step 2: display only SOC

Step 1: build your metadata | Step 2: customize the decimals

Step 1: build your metadata | Step 2: customize limits for AE proportion/difference plotting

Step 1: build your metadata | Step 2: customize the legend below the column of AE proportions differences

Step 1: build your metadata | Step 2: personalized color choices

Step 1: build your metadata | Step 2: customize the AE listing columns

Overview | Background | The sequential run | Setting up a parallel backend | Execution in parallel | A nested parallel example

Create Treatment Compliance Table | Example data | Build a metadata | Analysis preparation | Format output | Additional statistics | RTF tables

Overview | Results data table | Simulated trial dataset generation | Dataset manipulations for analysis | Flow for simulating group sequential: one scenario algorithm

Introduction | Simulating a delayed effect example | Generalizing the Magirr-Burman test | Freidlin and Korn strong null hypothesis example | References

Introduction | Obtaining a suitable dataset | Fit survival curves to the relevant endpoints | Parametric distributions | Royston-Parmar splines models | Combine the best fits | Comparing likelihood values for the three model structures | Comparing the implied (restricted) mean durations | Visual inspection of model fits | References

Introduction | Notation | Background mortality survival and hazard functions | What does constraining for background mortality mean? | Approach 1 - Constraining only the survival function | Approach 2 - Constraining the hazard function | Approach 3 - Modeling excess hazard in the original dataset | Application to 3-state State Transition Models (STMs) | Application to Partitioned Survival Models (PSM) | Illustration of calculations | Set-up | Creating the life table | Obtaining and fitting distributions to patient-level data | Making the projections | Comparing the results | Possible extensions | References

Overview | Design | Multiplicity diagram for hypothesis testing | Group sequential designs for each hypothesis | H1: OS, Subgroup | H2: OS, All | H3: PFS, Subgroup | H4: PFS, All | H5 and H6: ORR | Design list | Spending plan and spending time | Results entry at time of analysis | Timing of analyses and resulting event counts and spending times | Nominal p-values for each analysis | Testing hypotheses | Compute sequential p-values for each hypothesis | Evaluate hypothesis rejection using gMCPLite | Verification of hypotheses rejected | Multiplicity graph sequence from gMCPLite | Comparison of sequential p-values to multiplicity graphs | Bounds at final $\alpha$ allocated for group sequential tests | Session information | References

Introduction | The basic graph layout | Specifying hypothesis names and $\alpha$-allocation | Text formatting and location, ellipse size | Using colors and legends | Specifying the transition matrix between hypotheses | References

Overview | Statistical methods | Unstratified analysis of M&N method | Confidence interval | p-value and Z-statistic | Stratified analysis of M&N method | Example | Load package | Data simulation | Unstratified analysis | Stratified analysis | References

Collect ADaM mapping | Collect population | Collect population records | Collect observation record | Collect specifications

Overview | Use cases | Mental model | Example: adverse events analysis

Overview | Build metadata | Step 1: input the population and observation datasets | Step 2: create statistical analysis plans | Step 3: feed in the analysis plan to the existing metadata | Step 4: define the key words in the above metadata | Step 4.1: define the key words in population & observation | Step 4.2 define the key words in the analysis plans | Step 5: build the metadata | Create A&R grid

Overview of the example we used | Prepare the metadata | Step 1: Define the metadata | Step 2: Define the analysis plan | Step 3: Define the keywords | Step 4: Build and review the metadata | Add components of mockup table | Create functions | Row label | Get treatment grouping order | Get the title/endnotes of TLF | Logic to create mockup table | Display the generated mockup tables

Overview | Example 1: All Participants as Treated (apat) | Base R | Tidy evaluation | Example 2: Serious adverse events (ser)

Introduction | Example workflow | Pack one package | Unpack one package | Pack multiple packages | Unpack multiple packages | Helper functions

Merge file collections | Prune file collections | Sanitize file collections

File specification | File collection | File specification templates | File specification usage patterns | Default file specification | Automatic file specification

Filename and extension | File format | File structure | Field names and values | Package | File | Format | Content

SPC quick guide | Instalation | Functions | EvaluateRules() | SetParameters() | CalculateLimits() | CalculateZoneBorders() | NelsonRules() | Rule1()-Rule8() | For more information about parameters read the package manual.