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 improv...

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 contin...

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...

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 ...

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 ...

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 ...

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, ...

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. ...

Continuing the discussion on cumulative odds models I started last time, I want to investigate a solution I always assumed would help mitigate a failure to meet the proportional odds assumption. I’ve believed if there is a large number of categories and the relative cumulative odds between two groups ...

In two Covid-19-related trials I’m involved with, the primary or key secondary outcome is the status of a patient at 14 days based on a World Health Organization ordered rating scale. In this particular ordinal scale, there are 11 categories ranging from 0 (uninfected) to 10 (death). In between, a patient can ...

Continuing with the theme of exploring small issues that come up in trial design, I recently used simulation to assess the impact of stratifying (or not) in the context of a multi-site Covid-19 trial with a binary outcome. The investigators are concerned that baseline health status will affect the probability ...

As a biostatistician, I like to be involved in the design of a study as early as possible. I always like to say that I hope one of the first conversations an investigator has is with me, so that I can help clarify the research questions before getting into the ...

Last time I provided some simulations that suggested that there might not be any efficiency-related benefits to using unbalanced randomization when the outcome is binary. This is a quick follow-up to provide a counter-example where the outcome in a two-group comparison is continuous. If the groups have different amounts of ...

Of course, we’re all thinking about one thing these days, so it seems particularly inconsequential to be writing about anything that doesn’t contribute to solving or addressing in some meaningful way this pandemic crisis. But, I find that working provides a balm from reading and hearing all day ...

In my last post, I made the point that p-values should not necessarily be considered sufficient evidence (or evidence at all) in drawing conclusions about associations we are interested in exploring. When it comes to contingency tables that represent the outcomes for two categorical variables, it isn’t so obvious ...

I frequently find myself in discussions with collaborators about the merits of reporting p-values, particularly in the context of pilot studies or exploratory analysis. Over the past several years, the American Statistical Association has made several strong statements about the need to consider approaches that measure the strength of evidence ...

I am always saying that simulation can help illuminate interesting statistical concepts or ideas. The design effect that underlies much of clustered analysis is could benefit from a little exploration through simulation. I’ve written about clustered-related methods so much on this blog that I won’t provide links - ...

I am currently wrestling with how to analyze data from a stepped-wedge designed cluster randomized trial. A few factors make this analysis particularly interesting. First, we want to allow for the possibility that between-period site-level correlation will decrease (or decay) over time. Second, there is possibly additional clustering at the ...

The ROC (receiver operating characteristic) curve visually depicts the ability of a measure or classification model to distinguish two groups. The area under the ROC (AUC), quantifies the extent of that ability. My goal here is to describe as simply as possible a process that serves as a foundation for ...