Simulation Settings | Define Two Arms | Define a Trial | Define Trial Milestones and Action Functions | Interim decision | Stage 1 analysis | Final analysis | Execute a Trial | Execute Trial Simulation

Define Trial Milestones | Custom Action Functions | Perform statistical analyses on data snapshot | Save intermediate results | Example 1: saving hazard ratio and p-value | Example 2: passing complex objects between milestones | Example 3: defining global data of a trial | Example 4: carrying a testing procedure object across milestones | Alter a trial via adaptation

Simulation Settings | Define Three Arms | Define a Trial | Define Trial Milestones and Action Functions | Execute a Trial | Execute Trial Simulation

Simulation Settings | Transition Hazards of PFS and OS | Define Treatment Arms | Define a Trial | Define Milestone and Action for Final Analysis

Statistical setting | What alpha means | Conditional power | A specified hazard ratio | The interim trend | The null effect | What happens near the interim | Inverting conditional power to obtain an event number | Why ordinary bisection is insufficient | Integer search algorithm | An example with three target crossings | Worked TrialSimulator example | Conditional power under different assumptions | Event-number reassessment and the cap | A promising-zone policy | Implementing the policy in an action function | Assumptions and design checks | A complete promising-zone simulation | Multiple treatment arms | References

Where crossover() is called | The earliest crossover time: delay and the opening time | How the eligible population is determined | How the package monitors the triplet's output | Developing the triplet | A worked example | Scenarios | Relationship to add_regimen() | Inspecting the switches

Simulation Settings | Define Endpoints in Four Arms | Define a Trial | Define Milestone and Action | Execute a Trial

Define endpoints with random number generators | Baseline endpoints

Simulation Settings | Define Data Generator of fev1 | Define fev1 Endpoints for Each Arm | Define a Trial | Define Milestones and Associated Actions | Execute a Trial | Appendix: Codes of Helper Functions

Simulation Settings | Define Placebo and High Dose Arms | Define a Trial | Define Milestones and Actions | Execute a Trial | Appendix: Code of Helper Function

Example | Analyze Time-to-Event Endpoint | Analyze Continuous Endpoint | Analyze Binary Endpoint

A Common Structure | Why regimen uses three functions? | The Role of what() | The Role of when() | The Role of how() | Implement Dynamic Treatment Switching | Example 1: Crossover after progression | Example 2: Crossover for non-responsers | Example 3: Crossover when condition deteriorates | Example 4: Crossover multiple times

Model | $$\Pr(X > x, Y > y) | $$\Pr(P > t) | \Pr(X > t, Y > t) | \exp\left[-\left{(\lambda_X^\theta + \lambda_Y^\theta)t^\theta\right}^{1/\theta}\right] | Choosing the Copula Parameter | $$\tau(P,Y) | $$\tau(P,Y)-\tau(X,Y) | Example | Practical Guidance | Appendix: Kendall's Tau for Observed PFS and OS | $$S_{X,Y}(x,y) | \Pr(X>x, Y>y) | $$\Pr(A>a, B>b) | $$S_{P,Y}(Y,Y) | $$E | $$L_R(s) | \frac{1}{\theta}\int_0^\infty e^{-(1+s)r}(r+\theta-1),dr | $$\int_c^1L_R\left{\left(\frac{1-w}{d}\right)^{1/\theta}\right}dw | $$\frac{d}{dt}\left(\frac{t^\theta}{1+t}\right) | $$d\left[\frac{t^\theta}{1+t}\right]_0^1 | c\frac | 4E{S_{P,Y}(P,Y)}-1 | \frac | 1-\frac{1}{\theta}\left(\frac{\lambda_X^\theta}{\lambda_P^\theta}\right)

Define Arm with Mulitple Sets of Endpoints | Add Inclusion Criteria for the Arm | Simulate Data Explicitly (Not Recommended)

Reparameterization | Example | Further Discussion

Example: Simulating Longitudinal Blood Pressure Endpoints | Defining Endpoints for an Arm | Defining Endpoints for Another Arm | Creating Treatment Arms and Adding Endpoints

A trial has been running for 6 months | At least 520 patients have been enrolled | At least 400 patients have been enrolled, and each has received a minimum of 3 weeks of treatment | At least 340 PFS events are observed | Other examples

Define a univariate endpoint with random number generators from stats | Define a univariate endpoint with custom random number generators | Define multiple endpoints | An example of a custom random number generator for correlated endpoints

Flexible Input | Example 1: Integrating Population Characteristics