Cluster Randomized Trials

A researcher recently approached me for advice on a cluster-randomized trial he is developing. He is interested in testing the effectiveness of two interventions and wondered whether a 2×2 factorial design might be the best approach.

As we discussed the interventions (I’ll call them $A$ and $B$), it became clear that $A$ was the primary focus. Intervention $B$ might enhance the effectiveness of $A$, but on its own, $B$ was not expected to have much impact. (It’s also possible that $A$ alone doesn’t work, but once $B$ is in place, the combination may reap benefits.) Given this, it didn’t seem worthwhile to randomize clinics or providers to receive B alone. We agreed that a three-arm cluster-randomized trial—with (1) control, (2) $A$ alone, and (3) $A + B$—would be a more efficient and relevant design.

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.

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 three-arm trial.

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 I can share with you. (There are some analytic solutions to this problem, but there doesn’t seem to a consensus about the best approach to use.)

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.

In a previous post, I described some ways one might go about analyzing data from a stepped-wedge, cluster-randomized trial using a generalized additive model (a GAM), focusing on continuous outcomes. I have spent the past few weeks developing a similar model for a binary outcome, and have started to explore model comparison and methods to evaluate goodness-of-fit. The following describes some of my thought process.

Recently I started a discussion about modeling secular trends using flexible models in the context of cluster randomized trials. I’ve been motivated by a trial I am involved with that is using a stepped-wedge study design. The initial post focused on more standard parallel designs; here, I want to extend the discussion explicitly to the stepped-wedge design.

A key challenge - maybe the key challenge - of a stepped wedge clinical trial design is the threat of confounding by time. This is a cross-over design where the unit of randomization is a group or cluster, where each cluster begins in the control state and transitions to the intervention. It is the transition point that is randomized. Since outcomes could be changing over time regardless of the intervention, it is important to model the time trends when conducting the efficacy analysis. The question is how we choose to model time, and I am going to suggest that we might want to use a very flexible model, such as a cubic spline or a generalized additive model (GAM).

In the previous post, I described an incipient effort that I am undertaking with two colleagues, Monica Taljaard and Fan Li, to better understand the implications for collecting baseline measurements on sample size requirements for stepped wedge cluster randomized trials. (The three of us are on the Design and Statistics Core of the NIA IMPACT Collaboratory.) In that post, I conducted a series of simulations that illustrated the design effects in parallel cluster randomized trials derived analytically in a paper by Teerenstra et al. In this post, I am extending those simulations to stepped wedge trials; the hope is that the design effects can be formally derived some point soon.

Is it possible to reduce the sample size requirements of a stepped wedge cluster randomized trial simply by collecting baseline information? In a trial with randomization at the individual level, it is generally the case that if we are able to measure an outcome for subjects at two time periods, first at baseline and then at follow-up, we can reduce the overall sample size. But does this extend to (a) cluster randomized trials generally, and to (b) stepped wedge designs more specifically?