Publications by Keith Goldfeld
simstudy enhancement: specifying idiosyncratic follow-up times for longitudinal data
A researcher reached out to me a few weeks ago. They were trying to generate longitudinal data that included irregularly spaced follow-up periods. The default periods generated by the function addPeriods in the simstudy package are \(\{0, 1, 2, …, n – 1\}\), where there are \(n\) total periods. However, when follow-up periods required more spec...
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Perfectly balanced treatment arm distribution in a multifactorial CRT using stratified randomization
Over two years ago, I wrote a series of posts (starting here) that described possible analytic approaches for a proposed cluster-randomized trial with a factorial design. That proposal was recently funded by NIA/NIH, and now the Emergency departments leading the transformation of Alzheimer’s and dementia care (ED-LEAD) trial is just getting under...
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A three-arm trial using two-step randomization
Clinical Decision Support (CDS) tools are systems created to support clinical decision-making. Health care professionals using these tools can get guidance about diagnostic and treatment options when providing care to a patient. I’m currently involved with designing a trial focused on comparing a standard CDS tool with an enhanced version (CDS+)....
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Creating a nice looking Table 1 with standardized mean differences
I’m in the middle of a perfect storm, winding down three randomized clinical trials (RCTs), with patient recruitment long finished and data collection all wrapped up. This means a lot of data analysis, presentation prep, and paper writing (and not so much blogging). One common (and not so glamorous) thread cutting across all of these RCTs is the ...
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Finding logistic models to generate data with desired risk ratio, risk difference and AUC profiles
About two years ago, someone inquired whether simstudy had the functionality to generate data from a logistic model with a specific AUC. It did not, but now it does, thanks to a paper by Peter Austin that describes a nice algorithm to accomplish this. The paper actually describes a series of related algorithms for generating coefficients that targe...
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A demo of power estimation by simulation for a cluster randomized trial with a time-to-event outcome
A colleague reached out for help designing a cluster randomized trial to evaluate a clinical decision support tool for primary care physicians (PCPs), which aims to improve care for high-risk patients. The outcome will be a time-to-event measure, collected at the patient level. The unit of randomization will be the PCP, and one of the key design is...
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Generating variable cluster sizes to assess power in cluster randomize trials
In recent discussions with a number of collaborators at the NIA IMPACT Collaboratory about setting the sample size for a proposed cluster randomized trial, the question of variable cluster sizes has come up a number of times. Given a fixed overall sample size, it is generally better (in terms of statistical power) if the sample is equally distribut...
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Implementing a one-step GEE algorithm for very large cluster sizes in R
Very large data sets can present estimation problems for some statistical models, particularly ones that cannot avoid matrix inversion. For example, generalized estimating equations (GEE) models that are used when individual observations are correlated within groups can have severe computation challenges when the cluster sizes get too large. GEE ar...
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simstudy 0.6.0 released: more flexible correlation patterns
The new version (0.6.0) of simstudy is available for download from CRAN. In addition to some important bug fixes, I’ve added new functionality that should make data generation with correlated data a little more flexible. In the previous post, I described enhancements to the function genCorMat. As part of this release announcement, I’m describin...
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Flexible correlation generation: an update to genCorMat in simstudy
I’ve been slowly working on some updates to simstudy, focusing mostly on the functionality to generate correlation matrices (which can be used to simulate correlated data). Here, I’m briefly describing the function genCorMat, which has been updated to facilitate the generation of correlation matrices for clusters of different sizes and with pot...
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