Over the past few years, a number of folks have asked if simstudy accommodates customized distributions. There’s been interest in truncated, zero-inflated, or even more standard distributions that haven’t been implemented in simstudy. While I’ve come up with approaches for some of the specific cases, I was never able to develop a general solution that could provide broader flexibility. This shortcoming changes with the latest version of simstudy, now available on CRAN. Custom distributions can now be specified in defData and defDataAdd by setting the argument dist to “custom”. To introduce the ..read more

An investigator I frequently consult with seeks to estimate the effect of a palliative care treatment protocol for patients nearing end-stage disease, compared to a more standard, though potentially overly burdensome, therapeutic approach. Ideally, we would conduct a two-arm randomized clinical trial (RCT) to create comparable groups and obtain an unbiased estimate of the intervention effect. However, in this case, it may be considered unethical to randomize patients to a non-standard protocol. Alternatively, we could conduct an observational study, measuring outcomes for patients who choose o ..read more

The most traditional approach for analyzing binary outcome data is logistic regression, where the estimated parameters are interpreted as log odds ratios or, if exponentiated, as odds ratios (ORs). No one other than statisticians (and maybe not even statisticians) finds the odds ratio to be a very intuitive statistic, and many feel that a risk difference or risk ratio/relative risks (RRs) are much more interpretable. Indeed, there seems to be a strong belief that readers will, more often than not, interpret odds ratios as risk ratios. This turns out to be reasonable when an event is rare. Howe ..read more

One of my goals for the simstudy package is to make it as easy as possible to generate data from a wide range of data distributions. The recent update created the possibility of generating data from a customized distribution specified in a user-defined function. Last week, I added two functions, genDataDist and addDataDist, that allow data generation from an empirical distribution defined by a vector of integers. (See here for how to download latest development version.) This post provides a simple illustration of the new functionality. Here are the libraries needed, in case you want to follow ..read more

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 specificity, such as \(\{0, 90, 180, 365\}\) days from baseline, users had to manually add them. Originally, I had intended to incorporate this feature into the function, but unfortunately it slipped through the cracks. Thanks to the clear motivation provi ..read more

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 underway. Since the trial is in its early planning phase, I am starting to think about how we will do the randomization, and I’m sharing some of those thoughts (and code) here. A brief overview of ED-LEAD The ED-LEAD study is evaluating a set of three indep ..read more

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+). The main goal is to directly compare patient-level outcomes for those who have been exposed to the different versions of the CDS. However, we might also be interested in comparing the basic CDS with a control arm, which would suggest some type of thre ..read more

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 need to generate a Table 1, the comparison of baseline characteristics that convinces readers that randomization worked its magic (i.e., that study groups are indeed “comparable”). My primary goal here is to provide some R code to automate the generati ..read more

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 issues is settling on the number to randomize. Surprisingly, I’ve never been involved with a study that required a clustered survival analysis. So, this particular sample size calculation is new for me, which led to the development of simulations that ..read more

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 distributed across the different clusters; highly variable cluster sizes increase the standard errors of effect size estimates and reduce the ability to determine if an intervention or treatment is effective. When I started to prepare a quick simulation to de ..read more