ouR data generation

How do you determine sample size when the goal of a study is not to conduct a null hypothesis test but to provide an estimate of multiple effect sizes? I needed to get a handle on this for a recent grant submission, which I’ve been writing about over the past month, here and here. (I provide a little more context for all of this in those earlier posts.) The statistical inference in the study will be based on the estimated posterior distributions from a Bayesian model, so it seems like we’d like those distributions to be as informative as possible. [Read More]

Factorial study designs present a number of analytic challenges, not least of which is how to best understand whether simultaneously applying multiple interventions is beneficial. Last time I presented a possible approach that focuses on estimating the variance of effect size estimates using a Bayesian model. The scenario I used there focused on a hypothetical study evaluating two interventions with four different levels each. This time around, I am considering a proposed study to reduce emergency department (ED) use for patients living with dementia that I am actually involved with. [Read More]

Way back in 2018, long before the pandemic, I described a soon-to-be implemented simstudy function genMultiFac that facilitates the generation of multi-factorial study data. I followed up that post with a description of how we can use these types of efficient designs to answer multiple questions in the context of a single study. Fast forward three years, and I am thinking about these designs again for a new grant application that proposes to study simultaneously three interventions aimed at reducing emergency department (ED) use for people living with dementia. [Read More]

In the previous post, I simulated data from a hypothetical RCT that had heterogeneous treatment effects across subgroups defined by three covariates. I presented two Bayesian models, a strongly pooled model and an unpooled version, that could be used to estimate all the subgroup effects in a single model. I compared the estimates to a set of linear regression models that were estimated for each subgroup separately. My goal in doing these comparisons is to see how often we might draw the wrong conclusion about subgroup effects when we conduct these types of analyses. [Read More]

I’m part of a team that recently submitted the results of a randomized clinical trial for publication in a journal. The overall findings of the study were inconclusive, and we certainly didn’t try to hide that fact in our paper. Of course, the story was a bit more complicated, as the RCT was conducted during various phases of the COVID-19 pandemic; the context in which the therapeutic treatment was provided changed over time. [Read More]

This is a relatively brief addendum to last week’s post, where I described how the rvar datatype implemented in the R package posterior makes it quite easy to perform posterior probability checks to assess goodness of fit. In the initial post, I generated data from a linear model and estimated parameters for a linear regression model, and, unsurprisingly, the model fit the data quite well. When I introduced a quadratic term into the data generating process and fit the same linear model (without a quadratic term), equally unsurprising, the model wasn’t a great fit. [Read More]

Say we’ve collected data and estimated parameters of a model that give structure to the data. An important question to ask is whether the model is a reasonable approximation of the true underlying data generating process. If we did a good job, we should be able to turn around and generate data from the model itself that looks similar to the data we started with. And if we didn’t do such a great job, the newly generated data will diverge from the original. [Read More]

The odds ratio (OR) – the effect size parameter estimated in logistic regression – is notoriously difficult to interpret. It is a ratio of two quantities (odds, under different conditions) that are themselves ratios of probabilities. I think it is pretty clear that a very large or small OR implies a strong treatment effect, but translating that effect into a clinical context can be challenging, particularly since ORs cannot be mapped to unique probabilities. [Read More]

Given my recent involvement with the design of a somewhat complex trial centered around a Bayesian data analysis, I am appreciating more and more that Bayesian approaches are a very real option for clinical trial design. A key element of any study design is sample size. While some would argue that sample size considerations are not critical to the Bayesian design (since Bayesian inference is agnostic to any pre-specified sample size and is not really affected by how frequently you look at the data along the way), it might be a bit of a challenge to submit a grant without telling the potential funders how many subjects you plan on recruiting (since that could have a rather big effect on the level of resources - financial and time - required. [Read More]

Health data systems are not always perfect, a point that was made quite obvious when a study I am involved with required a matched list of nursing home residents taken from one system with set results from PCR tests for COVID-19 drawn from another. Name spellings for the same person from the second list were not always consistent across different PCR tests, nor were they always consistent with the cohort we were interested in studying defined by the first list. [Read More]