ex4_belly

Survival objects reverse-engineered datasets from published Kaplan-Meier
curves.
Individual trials are de-identified since the data are only
approximations of the actual data.
Data are intended to evaluate methods and designs for trials where
non-proportional hazards may be anticipated for outcome data.

datasets

Provides some basic routines for simulating a
clinical trial. The primary intent is to provide some tools to
generate trial simulations for trials with time to event outcomes.
Piecewise exponential failure rates and piecewise constant
enrollment rates are the underlying mechanism used to simulate
a broad range of scenarios such as those presented in
Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>.
However, the basic generation of data is done using pipes to allow
maximum flexibility for users to meet different needs.

Yujie Zhao

simtrial

Clinical Trial Simulation

Keaven Anderson

John Blischak

Nan Xiao

Yilong Zhang

Jianxiao Yang

Lili Ling

Xintong Li

Ruixue Wang

Yi Cui

Ping Yang

Yalin Zhu

Heng Zhou

Amin Shirazi

Cole Manschot

Larry Leon

Merck & Co., Inc., Rahway, NJ, USA and its affiliates 

ex4_belly function

Data frame with 4 variables:<ul>
<li><code>id</code>: Sequential numbering of unique identifiers.</li>
<li><code>month</code>: Time-to-event.</li>
<li><code>event</code>: 1 for event, 0 for censored.</li>
<li><code>trt</code>: 1 for experimental, 0 for control.</li>
</ul>

Format

Time-to-event data example 4 for non-proportional hazards working group — ex4_belly

Time-to-event data example 4 for non-proportional hazards working group

ex4_belly: Time-to-event data example 4 for non-proportional hazards working group

Description

Usage

Arguments

Format

References

See Also

Examples