Publications by Keith Goldfeld
Musings on missing data
I’ve been meaning to share an analysis I recently did to estimate the strength of the relationship between a young child’s ability to recognize emotions in others (e.g. teachers and fellow students) and her longer term academic success. The study itself is quite interesting (hopefully it will be published sometime soon), but I really wanted ...
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Even with randomization, mediation analysis can still be confounded
Randomization is super useful because it usually eliminates the risk that confounding will lead to a biased estimate of a treatment effect. However, this only goes so far. If you are conducting a meditation analysis in the hopes of understanding the underlying causal mechanism of a treatment, it is important to remember that the mediator has not ...
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What matters more in a cluster randomized trial: number or size?
I am involved with a trial of an intervention designed to prevent full-blown opioid use disorder for patients who may have an incipient opioid use problem. Given the nature of the intervention, it was clear the only feasible way to conduct this particular study is to randomize at the physician rather than the patient level. There was a concern th...
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Generating and modeling over-dispersed binomial data
A couple of weeks ago, I was inspired by a study to write about a classic design issue that arises in cluster randomized trials: should we focus on the number of clusters or the size of those clusters? This trial, which is concerned with preventing opioid use disorder for at-risk patients in primary care clinics, has also motivated this second po...
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simstudy update – stepped-wedge design treatment assignment
simstudy has just been updated (version 0.1.13 on CRAN), and includes one interesting addition (and a couple of bug fixes). I am working on a post (or two) about intra-cluster correlations (ICCs) and stepped-wedge study designs (which I’ve written about before), and I was getting tired of going through the convoluted process of generating data ...
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Don’t get too excited – it might just be regression to the mean
It is always exciting to find an interesting pattern in the data that seems to point to some important difference or relationship. A while ago, one of my colleagues shared a figure with me that looked something like this: It looks like something is going on. On average low scorers in the first period increased a bit in the second period, and hig...
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Planning a stepped-wedge trial? Make sure you know what you’re assuming about intra-cluster correlations …
A few weeks ago, I was at the annual meeting of the NIH Collaboratory, which is an innovative collection of collaboratory cores, demonstration projects, and NIH Institutes and Centers that is developing new models for implementing and supporting large-scale health services research. A study I am involved with – Primary Palliative Care for Emerg...
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More on those stepped-wedge design assumptions: varying intra-cluster correlations over time
In my last post, I wrote about within- and between-period intra-cluster correlations in the context of stepped-wedge cluster randomized study designs. These are quite important to understand when figuring out sample size requirements (and models for analysis, which I’ll be writing about soon.) Here, I’m extending the constant ICC assumption I...
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Estimating treatment effects (and ICCs) for stepped-wedge designs
In the last two posts, I introduced the notion of time-varying intra-cluster correlations in the context of stepped-wedge study designs. (See here and here). Though I generated lots of data for those posts, I didn’t fit any models to see if I could recover the estimates and any underlying assumptions. That’s what I am doing now. My focus here...
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Bayes models for estimation in stepped-wedge trials with non-trivial ICC patterns
Continuing a series of posts discussing the structure of intra-cluster correlations (ICC’s) in the context of a stepped-wedge trial, this latest edition is primarily interested in fitting Bayesian hierarchical models for more complex cases (though I do talk a bit more about the linear mixed effects models). The first two posts in the series foc...
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