This site is a compendium of R code meant to highlight the various uses of simulation to aid in the understanding of probability, statistics, and study design. I frequently draw on examples using my R package simstudy. Occasionally, I opine on other topics related to causal inference, evidence, and research more generally.

Flexible simulation in simstudy with customized distribution functions

Posted on August 30, 2022

Really, the only problem with the simstudy package (😄) is that there is a hard limit to the possible probability distributions that are available (the current count is 15 - see here for a complete description). However, it turns out that there is more flexibility than first meets the eye, and we can easily accommodate a limitless number as long as you are willing to provide some extra code. [Read More]
R  simulation 

Simulating data from a non-linear function by specifying a handful of points

Posted on August 9, 2022

Trying to simulate data with non-linear relationships can be frustrating, since there is not always an obvious mathematical expression that will give you the shape you are looking for. I’ve come up with a relatively simple solution for somewhat complex scenarios that only requires the specification of a few points that lie on or near the desired curve. (Clearly, if the relationships are straightforward, such as relationships that can easily be represented by quadratic or cubic polynomials, there is no need to go through all this trouble. [Read More]
R  simulation  GAM 

simstudy updated to version 0.5.0

Posted on July 20, 2022

A new version of simstudy is available on CRAN. There are two major enhancements and several new features. In the “major” category, I would include (1) changes to survival data generation that accommodate hazard ratios that can change over time, as well as competing risks, and (2) the addition of functions to allow users to sample from existing data sets with replacement to generate “synthetic” data will real life distribution properties. [Read More]
R  simstudy  simulation 

To impute or not: the case of an RCT with baseline and follow-up measurements

Posted on April 12, 2022

Under normal conditions, conducting a randomized clinical trial is challenging. Throw in a pandemic and things like site selection, patient recruitment and patient follow-up can be particularly vexing. In any study, subjects need to be retained long enough so that outcomes can be measured; during a period when there are so many potential disruptions, this can become quite difficult. This issue of loss to follow-up recently came up during a conversation among a group of researchers who were troubleshooting challenges they are all experiencing in their ongoing trials. [Read More]
R  Missing data 

Simulating time-to-event outcomes with non-proportional hazards

Posted on March 29, 2022

As I mentioned last time, I am working on an update of simstudy that will make generating survival/time-to-event data a bit more flexible. I previously presented the functionality related to competing risks, and this time I’ll describe generating survival data that has time-dependent hazard ratios. (As I mentioned last time, if you want to try this at home, you will need the development version of simstudy that you can install using devtools::install_github(“kgoldfeld/simstudy”). [Read More]
R  survival analysis 

Adding competing risks in survival data generation

Posted on March 15, 2022

I am working on an update of simstudy that will make generating survival/time-to-event data a bit more flexible. There are two biggish enhancements. The first facilitates generation of competing events, and the second allows for the possibility of generating survival data that has time-dependent hazard ratios. This post focuses on the first enhancement, and a follow up will provide examples of the second. (If you want to try this at home, you will need the development version of simstudy, which you can install using devtools::install_github(“kgoldfeld/simstudy”). [Read More]
R  survival analysis 

Follow-up: simstudy function for generating parameters for survival distribution

Posted on February 22, 2022

In the previous post I described how to determine the parameter values for generating a Weibull survival curve that reflects a desired distribution defined by two points along the curve. I went ahead and implemented these ideas in the development version of simstudy 0.4.0.9000, expanding the idea to allow for any number of points rather than just two. This post provides a brief overview of the approach, the code, and a simple example using the parameters to generate simulated data. [Read More]
R  survival analysis 

Simulating survival outcomes: setting the parameters for the desired distribution

Posted on February 8, 2022

The package simstudy has some functions that facilitate generating survival data using an underlying Weibull distribution. Originally, I added this to the package because I thought it would be interesting to try to do, and I figured it would be useful for me someday (and hopefully some others, as well). Well, now I am working on a project that involves evaluating at least two survival-type processes that are occurring simultaneously. [Read More]
R  simulation  survival analysis 

simstudy update: ordinal data generation that violates proportionality

Posted on January 25, 2022

Version 0.4.0 of simstudy is now available on CRAN and GitHub. This update includes two enhancements (and at least one major bug fix). genOrdCat now includes an argument to generate ordinal data without an assumption of cumulative proportional odds. And two new functions defRepeat and defRepeatAdd make it a bit easier to define multiple variables that share the same distribution assumptions. Ordinal data In simstudy, it is relatively easy to specify multinomial distributions that characterize categorical data. [Read More]
R  simstudy 

Including uncertainty when comparing response rates across clusters

Posted on January 18, 2022

Since this is a holiday weekend here in the US, I thought I would write up something relatively short and simple since I am supposed to be relaxing. A few weeks ago, someone presented me with some data that showed response rates to a survey that was conducted at about 30 different locations. The team that collected the data was interested in understanding if there were some sites that had response rates that might have been too low. [Read More]
R  Bayesian model