Publications by Keith Goldfeld
Posterior probability checking with rvars: a quick follow-up
This is a relatively brief addendum to last week’s post, where I described how the rvar datatype implemented in the R package posterior makes it quite easy to perform posterior probability checks to assess goodness of fit. In the initial post, I generated data from a linear model and estimated parameters for a linear regression model, and, unsu...
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Subgroup analysis using a Bayesian hierarchical model
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 pan...
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Drawing the wrong conclusion about subgroups: a comparison of Bayes and frequentist methods
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 line...
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Analyzing a factorial design by focusing on the variance of effect sizes
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 thre...
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A Bayesian analysis of a factorial design focusing on effect size estimates
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 ...
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Sample size requirements for a Bayesian factorial study design
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 earlie...
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simstudy update: adding flexibility to data generation
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 smoothe...
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The design effect of a cluster randomized trial with baseline measurements
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 red...
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Exploring design effects of stepped wedge designs with baseline measurements
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 Co...
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Controlling Type I error in RCTs with interim looks: a Bayesian perspective
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 gro...
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