Considering sensitivity to unmeasured confounding: part 1

Principled causal inference methods can be used to compare the effects of different exposures or treatments we have observed in non-experimental settings. These methods, which include matching (with or without propensity scores), inverse probability weighting, and various g-methods, help us create comparable groups to simulate a randomized experiment. All of these approaches rely on a key assumption of no unmeasured confounding. The problem is, short of subject matter knowledge, there is no way to test this assumption empirically. [Read More]

Parallel processing to add a little zip to power simulations (and other replication studies)

It’s always nice to be able to speed things up a bit. My first blog post ever described an approach using Rcpp to make huge improvements in a particularly intensive computational process. Here, I want to show how simple it is to speed things up by using the R package parallel and its function mclapply. I’ve been using this function more and more, so I want to explicitly demonstrate it in case any one is wondering. [Read More]

Horses for courses, or to each model its own (causal effect)

In my previous post, I described a (relatively) simple way to simulate observational data in order to compare different methods to estimate the causal effect of some exposure or treatment on an outcome. The underlying data generating process (DGP) included a possibly unmeasured confounder and an instrumental variable. (If you haven’t already, you should probably take a quick look.) A key point in considering causal effect estimation is that the average causal effect depends on the individuals included in the average. [Read More]

Generating data to explore the myriad causal effects that can be estimated in observational data analysis

I’ve been inspired by two recent talks describing the challenges of using instrumental variable (IV) methods. IV methods are used to estimate the causal effects of an exposure or intervention when there is unmeasured confounding. This estimated causal effect is very specific: the complier average causal effect (CACE). But, the CACE is just one of several possible causal estimands that we might be interested in. For example, there’s the average causal effect (ACE) that represents a population average (not just based the subset of compliers). [Read More]

Causal mediation estimation measures the unobservable

I put together a series of demos for a group of epidemiology students who are studying causal mediation analysis. Since mediation analysis is not always so clear or intuitive, I thought, of course, that going through some examples of simulating data for this process could clarify things a bit. Quite often we are interested in understanding the relationship between an exposure or intervention on an outcome. Does exposure \(A\) (could be randomized or not) have an effect on outcome \(Y\)? [Read More]

Cross-over study design with a major constraint

Every new study presents its own challenges. (I would have to say that one of the great things about being a biostatistician is the immense variety of research questions that I get to wrestle with.) Recently, I was approached by a group of researchers who wanted to evaluate an intervention. Actually, they had two, but the second one was a minor tweak added to the first. They were trying to figure out how to design the study to answer two questions: (1) is intervention \(A\) better than doing nothing and (2) is \(A^+\), the slightly augmented version of \(A\), better than just \(A\)? [Read More]

In regression, we assume noise is independent of all measured predictors. What happens if it isn't?

A number of key assumptions underlie the linear regression model - among them linearity and normally distributed noise (error) terms with constant variance In this post, I consider an additional assumption: the unobserved noise is uncorrelated with any covariates or predictors in the model. In this simple model: \[Y_i = \beta_0 + \beta_1X_i + e_i,\] \(Y_i\) has both a structural and stochastic (random) component. The structural component is the linear relationship of \(Y\) with \(X\). [Read More]

simstudy update: improved correlated binary outcomes

An updated version of the simstudy package (0.1.10) is now available on CRAN. The impetus for this release was a series of requests about generating correlated binary outcomes. In the last post, I described a beta-binomial data generating process that uses the recently added beta distribution. In addition to that update, I’ve added functionality to genCorGen and addCorGen, functions which generate correlated data from non-Gaussian or normally distributed data such as Poisson, Gamma, and binary data. [Read More]

Binary, beta, beta-binomial

I’ve been working on updates for the simstudy package. In the past few weeks, a couple of folks independently reached out to me about generating correlated binary data. One user was not impressed by the copula algorithm that is already implemented. I’ve added an option to use an algorithm developed by Emrich and Piedmonte in 1991, and will be incorporating that option soon in the functions genCorGen and addCorGen. I’ll write about that change some point soon. [Read More]

The power of stepped-wedge designs

Just before heading out on vacation last month, I put up a post that purported to compare stepped-wedge study designs with more traditional cluster randomized trials. Either because I rushed or was just lazy, I didn’t exactly do what I set out to do. I did confirm that a multi-site randomized clinical trial can be more efficient than a cluster randomized trial when there is variability across clusters. (I compared randomizing within a cluster with randomization by cluster. [Read More]