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.

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

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.
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## A three-arm trial using two-step randomization

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.
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## Creating a nice looking Table 1 with standardized mean differences

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

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.
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## A demo of power estimation by simulation for a cluster randomized trial with a time-to-event outcome

A colleague reached out for help designing a cluster randomized trial to evaluate a clinical decision support tool for primary care physicians (PCPs), which aims to improve care for high-risk patients. The outcome will be a time-to-event measure, collected at the patient level. The unit of randomization will be the PCP, and one of the key design issues is settling on the number to randomize.
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## Generating variable cluster sizes to assess power in cluster randomized trials

In recent discussions with a number of collaborators at the NIA IMPACT Collaboratory about setting the sample size for a proposed cluster randomized trial, the question of variable cluster sizes has come up a number of times. Given a fixed overall sample size, it is generally better (in terms of statistical power) if the sample is equally distributed across the different clusters; highly variable cluster sizes increase the standard errors of effect size estimates and reduce the ability to determine if an intervention or treatment is effective.
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## Implementing a one-step GEE algorithm for very large cluster sizes in R

Very large data sets can present estimation problems for some statistical models, particularly ones that cannot avoid matrix inversion. For example, generalized estimating equations (GEE) models that are used when individual observations are correlated within groups can have severe computation challenges when the cluster sizes get too large. GEE are often used when repeated measures for an individual are collected over time; the individual is considered the cluster in this analysis.
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## simstudy 0.6.0 released: more flexible correlation patterns

The new version (0.6.0) of simstudy is available for download from CRAN. In addition to some important bug fixes, I’ve added new functionality that should make data generation with correlated data a little more flexible. In the previous post, I described enhancements to the function genCorMat. As part of this release announcement, I’m describing blockExchangeMat and blockDecayMat, two new functions that can be used to generate correlation matrices when there is a temporal element to the data generation.
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## Flexible correlation generation: an update to genCorMat in simstudy

I’ve been slowly working on some updates to simstudy, focusing mostly on the functionality to generate correlation matrices (which can be used to simulate correlated data). Here, I’m briefly describing the function genCorMat, which has been updated to facilitate the generation of correlation matrices for clusters of different sizes and with potentially different correlation coefficients.
I’ll briefly describe what the existing function can currently do, and then give an idea about what the enhancements will provide.
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