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.

Can ChatGPT help construct non-trivial statistical models? An example with Bayesian "random" splines

Posted on October 8, 2024

I’ve been curious to see how helpful ChatGPT can be for implementing relatively complicated models in R. About two years ago, I described a model for estimating a treatment effect in a cluster-randomized stepped wedge trial. We used a generalized additive model (GAM) with site-specific splines to account for general time trends, implemented using the mgcv package. I’ve been interested in exploring a Bayesian version of this model, but hadn’t found the time to try - until I happened to pose this simple question to ChatGPT: [Read More]
R  Bayesian model  ChatGPT 

An IV study design to estimate an effect size when randomization is not ethical

Posted on September 3, 2024

An investigator I frequently consult with seeks to estimate the effect of a palliative care treatment protocol for patients nearing end-stage disease, compared to a more standard, though potentially overly burdensome, therapeutic approach. Ideally, we would conduct a two-arm randomized clinical trial (RCT) to create comparable groups and obtain an unbiased estimate of the intervention effect. However, in this case, it may be considered unethical to randomize patients to a non-standard protocol. [Read More]
R  instrumental variable  simulation  causal inference 

Generating binary data by specifying the relative risk, with simulations

Posted on July 2, 2024

The most traditional approach for analyzing binary outcome data is logistic regression, where the estimated parameters are interpreted as log odds ratios or, if exponentiated, as odds ratios (ORs). No one other than statisticians (and maybe not even statisticians) finds the odds ratio to be a very intuitive statistic, and many feel that a risk difference or risk ratio/relative risks (RRs) are much more interpretable. Indeed, there seems to be a strong belief that readers will, more often than not, interpret odds ratios as risk ratios. [Read More]
R  simulation  logistic regression  poisson regression  simstudy 

simstudy: another way to generate data from a non-standard density

Posted on June 4, 2024

One of my goals for the simstudy package is to make it as easy as possible to generate data from a wide range of data distributions. The recent update created the possibility of generating data from a customized distribution specified in a user-defined function. Last week, I added two functions, genDataDist and addDataDist, that allow data generation from an empirical distribution defined by a vector of integers. (See here for how to download latest development version. [Read More]
R  simstudy 

simstudy 0.8.0: customized distributions

Posted on May 21, 2024

Over the past few years, a number of folks have asked if simstudy accommodates customized distributions. There’s been interest in truncated, zero-inflated, or even more standard distributions that haven’t been implemented in simstudy. While I’ve come up with approaches for some of the specific cases, I was never able to develop a general solution that could provide broader flexibility. This shortcoming changes with the latest version of simstudy, now available on CRAN. [Read More]
R  simstudy 

simstudy enhancement: specifying idiosyncratic follow-up times for longitudinal data

Posted on April 16, 2024

A researcher reached out to me a few weeks ago. They were trying to generate longitudinal data that included irregularly spaced follow-up periods. The default periods generated by the function addPeriods in the simstudy package are \(\{0, 1, 2, ..., n - 1\}\), where there are \(n\) total periods. However, when follow-up periods required more specificity, such as \(\{0, 90, 180, 365\}\) days from baseline, users had to manually add them. [Read More]
R  Longitudinal data 

Perfectly balanced treatment arm distribution in a multifactorial CRT using stratified randomization

Posted on February 20, 2024

Over two years ago, I wrote a series of posts (starting here) that described possible analytic approaches for a proposed cluster-randomized trial with a factorial design. That proposal was recently funded by NIA/NIH, and now the Emergency departments leading the transformation of Alzheimer’s and dementia care (ED-LEAD) trial is just getting underway. Since the trial is in its early planning phase, I am starting to think about how we will do the randomization, and I’m sharing some of those thoughts (and code) here. [Read More]
R  Cluster randomized trials 

A three-arm trial using two-step randomization

Posted on December 19, 2023

Clinical Decision Support (CDS) tools are systems created to support clinical decision-making. Health care professionals using these tools can get guidance about diagnostic and treatment options when providing care to a patient. I’m currently involved with designing a trial focused on comparing a standard CDS tool with an enhanced version (CDS+). The main goal is to directly compare patient-level outcomes for those who have been exposed to the different versions of the CDS. [Read More]
R  Cluster randomized trials 

Creating a nice looking Table 1 with standardized mean differences

Posted on September 26, 2023

I’m in the middle of a perfect storm, winding down three randomized clinical trials (RCTs), with patient recruitment long finished and data collection all wrapped up. This means a lot of data analysis, presentation prep, and paper writing (and not so much blogging). One common (and not so glamorous) thread cutting across all of these RCTs is the need to generate a Table 1, the comparison of baseline characteristics that convinces readers that randomization worked its magic (i. [Read More]
R 

Finding logistic models to generate data with desired risk ratio, risk difference and AUC profiles

Posted on June 20, 2023

About two years ago, someone inquired whether simstudy had the functionality to generate data from a logistic model with a specific AUC. It did not, but now it does, thanks to a paper by Peter Austin that describes a nice algorithm to accomplish this. The paper actually describes a series of related algorithms for generating coefficients that target specific prevalence rates, risk ratios, and risk differences, in addition the AUC. [Read More]
R  simstudy  logistic regression