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.
Skeptical Bayesian priors might help minimize skepticism about subgroup analyses
Posted on January 4, 2022
Over the past couple of years, I have been working with an amazing group of investigators as part of the CONTAIN trial to study whether COVID-19 convalescent plasma (CCP) can improve the clinical status of patients hospitalized with COVID-19 and requiring noninvasive supplemental oxygen. This was a multi-site study in the US that randomized 941 patients to either CCP or a saline solution placebo. The overall findings suggest that CCP did not benefit the patients who received it, but if you drill down a little deeper, the story may be more complicated than that.
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Controlling Type I error in RCTs with interim looks: a Bayesian perspective
Posted on December 21, 2021
Recently, a colleague submitted a paper describing the results of a Bayesian adaptive trial where the research team estimated the probability of effectiveness at various points during the trial. This trial was designed to stop as soon as the probability of effectiveness exceeded a pre-specified threshold. The journal rejected the paper on the grounds that these repeated interim looks inflated the Type I error rate, and increased the chances that any conclusions drawn from the study could have been misleading.
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Exploring design effects of stepped wedge designs with baseline measurements
Posted on December 7, 2021
In the previous post, I described an incipient effort that I am undertaking with two colleagues, Monica Taljaard and Fan Li, to better understand the implications for collecting baseline measurements on sample size requirements for stepped wedge cluster randomized trials. (The three of us are on the Design and Statistics Core of the NIA IMPACT Collaboratory.) In that post, I conducted a series of simulations that illustrated the design effects in parallel cluster randomized trials derived analytically in a paper by Teerenstra et al.
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The design effect of a cluster randomized trial with baseline measurements
Posted on November 23, 2021
Is it possible to reduce the sample size requirements of a stepped wedge cluster randomized trial simply by collecting baseline information? In a trial with randomization at the individual level, it is generally the case that if we are able to measure an outcome for subjects at two time periods, first at baseline and then at follow-up, we can reduce the overall sample size. But does this extend to (a) cluster randomized trials generally, and to (b) stepped wedge designs more specifically?
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simstudy update: adding flexibility to data generation
Posted on November 9, 2021
A new version of simstudy (0.3.0) is now available on CRAN and on the package website. Along with some less exciting bug fixes, we have added capabilities to a few existing features: double-dot variable reference, treatment assignment, and categorical data definition. These simple additions should make the data generation process a little smoother and more flexible.
Using non-scalar double-dot variable reference Double-dot notation was introduced in the last version of simstudy to allow data definitions to be more dynamic.
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Sample size requirements for a Bayesian factorial study design
Posted on October 26, 2021
How do you determine sample size when the goal of a study is not to conduct a null hypothesis test but to provide an estimate of multiple effect sizes? I needed to get a handle on this for a recent grant submission, which I’ve been writing about over the past month, here and here. (I provide a little more context for all of this in those earlier posts.) The statistical inference in the study will be based on the estimated posterior distributions from a Bayesian model, so it seems like we’d like those distributions to be as informative as possible.
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A Bayesian analysis of a factorial design focusing on effect size estimates
Posted on October 12, 2021
Factorial study designs present a number of analytic challenges, not least of which is how to best understand whether simultaneously applying multiple interventions is beneficial. Last time I presented a possible approach that focuses on estimating the variance of effect size estimates using a Bayesian model. The scenario I used there focused on a hypothetical study evaluating two interventions with four different levels each. This time around, I am considering a proposed study to reduce emergency department (ED) use for patients living with dementia that I am actually involved with.
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Analyzing a factorial design by focusing on the variance of effect sizes
Posted on September 28, 2021
Way back in 2018, long before the pandemic, I described a soon-to-be implemented simstudy function genMultiFac that facilitates the generation of multi-factorial study data. I followed up that post with a description of how we can use these types of efficient designs to answer multiple questions in the context of a single study.
Fast forward three years, and I am thinking about these designs again for a new grant application that proposes to study simultaneously three interventions aimed at reducing emergency department (ED) use for people living with dementia.
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Drawing the wrong conclusion about subgroups: a comparison of Bayes and frequentist methods
Posted on September 14, 2021
In the previous post, I simulated data from a hypothetical RCT that had heterogeneous treatment effects across subgroups defined by three covariates. I presented two Bayesian models, a strongly pooled model and an unpooled version, that could be used to estimate all the subgroup effects in a single model. I compared the estimates to a set of linear regression models that were estimated for each subgroup separately.
My goal in doing these comparisons is to see how often we might draw the wrong conclusion about subgroup effects when we conduct these types of analyses.
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Subgroup analysis using a Bayesian hierarchical model
Posted on August 31, 2021
I’m part of a team that recently submitted the results of a randomized clinical trial for publication in a journal. The overall findings of the study were inconclusive, and we certainly didn’t try to hide that fact in our paper. Of course, the story was a bit more complicated, as the RCT was conducted during various phases of the COVID-19 pandemic; the context in which the therapeutic treatment was provided changed over time.
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