Publications by Keith Goldfeld
Considering sensitivity to unmeasured confounding: part 2
In part 1 of this 2-part series, I introduced the notion of sensitivity to unmeasured confounding in the context of an observational data analysis. I argued that an estimate of an association between an observed exposure \(D\) and outcome \(Y\) is sensitive to unmeasured confounding if we can conceive of a reasonable alternative data generating p...
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Correlated longitudinal data with varying time intervals
I was recently contacted to see if simstudy can create a data set of correlated outcomes that are measured over time, but at different intervals for each individual. The quick answer is there is no specific function to do this. However, if you are willing to assume an “exchangeable” correlation structure, where measurements far apart in time ...
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Using the uniform sum distribution to introduce probability
I’ve never taught an intro probability/statistics course. If I ever did, I would certainly want to bring the underlying wonder of the subject to life. I’ve always found it almost magical the way mathematical formulation can be mirrored by computer simulation, the way proof can be guided by observed data generation processes, and the way DGPs ...
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A example in causal inference designed to frustrate: an estimate pretty much guaranteed to be biased
I am putting together a brief lecture introducing causal inference for graduate students studying biostatistics. As part of this lecture, I thought it would be helpful to spend a little time describing directed acyclic graphs (DAGs), since they are an extremely helpful tool for communicating assumptions about the causal relationships underlying a...
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A case where prospective matching may limit bias in a randomized trial
Analysis is important, but study design is paramount. I am involved with the Diabetes Research, Education, and Action for Minorities (DREAM) Initiative, which is, among other things, estimating the effect of a group-based therapy program on weight loss for patients who have been identified as pre-diabetic (which means they have elevated HbA1c lev...
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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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