Blog Archives

Generating and modeling over-dispersed binomial data

May 13, 2019
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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...

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What matters more in a cluster randomized trial: number or size?

April 29, 2019
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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 that...

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Even with randomization, mediation analysis can still be confounded

April 15, 2019
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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 been randomized,...

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Musings on missing data

April 1, 2019
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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 to write about it here...

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A case where prospective matching may limit bias in a randomized trial

March 11, 2019
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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 levels). The original plan was to randomize...

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A example in causal inference designed to frustrate: an estimate pretty much guaranteed to be biased

February 25, 2019
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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 researcher’s data. The strength of DAGs is that...

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Using the uniform sum distribution to introduce probability

February 4, 2019
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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 can confirm analytic...

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Correlated longitudinal data with varying time intervals

January 21, 2019
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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 are just...

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Considering sensitivity to unmeasured confounding: part 2

January 9, 2019
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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 process (DGP) that includes...

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Considering sensitivity to unmeasured confounding: part 1

January 1, 2019
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Considering sensitivity to unmeasured confounding: part 1

Principled causal inference methods can be used to compare the effects of different exposures or treatments we have observed in non-experimental settings. These methods, which include matching (with or without propensity scores), inverse probability weighting, and various g-methods, help us create comparable groups to simulate a randomized experiment. All of these approaches rely on a key assumption of no...

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