Publications by Keith Goldfeld
Consider a permutation test for a small pilot study
Recently I wrote about the challenges of trying to learn too much from a small pilot study, even if it is a randomized controlled trial. There are limitations on how much you can learn about a treatment effect given the small sample size and relatively high variability of the estimate. However, the temptation for researchers is usually just too g...
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Simulating multiple RCTs to simulate a meta-analysis
I am currently involved with an RCT that is struggling to recruit eligible patients (by no means an unusual problem), increasing the risk that findings might be inconclusive. A possible solution to this conundrum is to find similar, ongoing trials with the aim of pooling data in a single analysis, to conduct a meta-analysis of sorts. In an ideal ...
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A Bayesian model for a simulated meta-analysis
This is essentially an addendum to the previous post where I simulated data from multiple RCTs to explore an analytic method to pool data across different studies. In that post, I used the nlme package to conduct a meta-analysis based on individual level data of 12 studies. Here, I am presenting an alternative hierarchical modeling approach that ...
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A hurdle model for COVID-19 infections in nursing homes
Late last year, I added a mixture distribution to the simstudy package, largely motivated to accommodate zero-inflated Poisson or negative binomial distributions. (I really thought I had added this two years ago – but time is moving so slowly these days.) These distributions are useful when modeling count data, but we anticipate observing more ...
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Generating data from a truncated distribution
A researcher reached out to me the other day to see if the simstudy package provides a quick and easy way to generate data from a truncated distribution. Other than the noZeroPoisson distribution option (which is a very specific truncated distribution), there is no way to do this directly. You can always generate data from the full distribution a...
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Diagnosing and dealing with degenerate estimation in a Bayesian meta-analysis
The federal government recently granted emergency approval for the use of antibody rich blood plasma when treating hospitalized COVID-19 patients. This announcement is unfortunate, because we really don’t know if this promising treatment works. The best way to determine this, of course, is to conduct an experiment, though this approval makes th...
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Generating probabilities for ordinal categorical data
Over the past couple of months, I’ve been describing various aspects of the simulations that we’ve been doing to get ready for a meta-analysis of convalescent plasma treatment for hospitalized patients with COVID-19, most recently here. As I continue to do that, I want to provide motivation and code for a small but important part of the data ...
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Permuted block randomization using simstudy
Along with preparing power analyses and statistical analysis plans (SAPs), generating study randomization lists is something a practicing biostatistician is occasionally asked to do. While not a particularly interesting activity, it offers the opportunity to tackle a small programming challenge. The title is a little misleading because you should...
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simstudy just got a little more dynamic: version 0.2.1
simstudy version 0.2.1 has just been submitted to CRAN. Along with this release, the big news is that I’ve been joined by Jacob Wujciak-Jens as a co-author of the package. He initially reached out to me from Germany with some suggestions for improvements, we had a little back and forth, and now here we are. He has substantially reworked the und...
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A refined brute force method to inform simulation of ordinal response data
Francisco, a researcher from Spain, reached out to me with a challenge. He is interested in exploring various models that estimate correlation across multiple responses to survey questions. This is the context: He doesn’t have access to actual data, so to explore analytic methods he needs to simulate responses. It would be ideal if the simulat...
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