This site is a compendium of R code meant to highlight the various uses of simulation to aid in the understanding of probability, statistics, and study design. I will frequently draw on examples using my R package simstudy. Occasionally, I will opine on other topics related to causal inference, evidence, and research more generally.

Be careful not to control for a post-exposure covariate

A researcher was presenting an analysis of the impact various types of childhood trauma might have on subsequent substance abuse in adulthood. Obviously, a very interesting and challenging research question. The statistical model included adjustments for several factors that are plausible confounders of the relationship between trauma and substance use, such as childhood poverty. However, the model also include a measurement for poverty in adulthood - believing it was somehow confounding the relationship of trauma and substance use. [Read More]

Should we be concerned about incidence - prevalence bias?

Recently, we were planning a study to evaluate the effect of an intervention on outcomes for very sick patients who show up in the emergency department. My collaborator had concerns about a phenomenon that she had observed in other studies that might affect the results - patients measured earlier in the study tend to be sicker than those measured later in the study. This might not be a problem, but in the context of a stepped-wedge study design (see this for a discussion that touches this type of study design), this could definitely generate biased estimates: when the intervention occurs later in the study (as it does in a stepped-wedge design), the “exposed” and “unexposed” populations could differ, and in turn so could the outcomes. [Read More]

Using simulation for power analysis: an example based on a stepped wedge study design

Simulation can be super helpful for estimating power or sample size requirements when the study design is complex. This approach has some advantages over an analytic one (i.e. one based on a formula), particularly the flexibility it affords in setting up the specific assumptions in the planned study, such as time trends, patterns of missingness, or effects of different levels of clustering. A downside is certainly the complexity of writing the code as well as the computation time, which can be a bit painful. [Read More]

simstudy update: two new functions that generate correlated observations from non-normal distributions

In an earlier post, I described in a fair amount of detail an algorithm to generate correlated binary or Poisson data. I mentioned that I would be updating simstudy with functions that would make generating these kind of data relatively painless. Well, I have managed to do that, and the updated package (version 0.1.3) is available for download from CRAN. There are now two additional functions to facilitate the generation of correlated data from binomial, poisson, gamma, and uniform distributions: genCorGen and addCorGen. [Read More]

Balancing on multiple factors when the sample is too small to stratify

Ideally, a study that uses randomization provides a balance of characteristics that might be associated with the outcome being studied. This way, we can be more confident that any differences in outcomes between the groups are due to the group assignments and not to differences in characteristics. Unfortunately, randomization does not guarantee balance, especially with smaller sample sizes. If we want to be certain that groups are balanced with respect to a particular characteristic, we need to do something like stratified randomization. [Read More]

Copulas and correlated data generation: getting beyond the normal distribution

Using the simstudy package, it’s possible to generate correlated data from a normal distribution using the function genCorData. I’ve wanted to extend the functionality so that we can generate correlated data from other sorts of distributions; I thought it would be a good idea to begin with binary and Poisson distributed data, since those come up so frequently in my work. simstudy can already accommodate more general correlated data, but only in the context of a random effects data generation process. [Read More]

When marginal and conditional logistic model estimates diverge

Say we have an intervention that is assigned at a group or cluster level but the outcome is measured at an individual level (e.g. students in different schools, eyes on different individuals). And, say this outcome is binary; that is, something happens, or it doesn’t. (This is important, because none of this is true if the outcome is continuous and close to normally distributed.) If we want to measure the effect of the intervention - perhaps the risk difference, risk ratio, or odds ratio - it can really matter if we are interested in the marginal effect or the conditional effect, because they likely won’t be the same. [Read More]

It can be easy to explore data generating mechanisms with the simstudy package

I learned statistics and probability by simulating data. Sure, I did the occasional proof, but I never believed the results until I saw it in a simulation. I guess I have it backwards, but I that’s just the way I am. And now that I am a so-called professional, I continue to use simulation to understand models, to do sample size estimates and power calculations, and of course to teach. [Read More]

Everyone knows that loops in R are to be avoided, but vectorization is not always possible

It goes without saying that there are always many ways to solve a problem in R, but clearly some ways are better (for example, faster) than others. Recently, I found myself in a situation where I could not find a way to avoid using a loop, and I was immediately concerned, knowing that I would want this code to be flexible enough to run with a very large number of observations, possibly over many observations. [Read More]