Causal inference for N-of-1 trials

TL;DR

This paper formalizes causal inference for N-of-1 trials, defining a target effect called U-CATE, and discusses estimation methods under various assumptions, with practical application to acne symptom data.

Contribution

It introduces a formal causal inference framework for N-of-1 trials, defining U-CATE and exploring estimation strategies under complex conditions.

Findings

A simple mean difference estimates U-CATE in basic settings.

Time-varying g-formula identifies U-CATE with complex data.

Different assumptions lead to different analytical strategies.

Abstract

The aim of personalized medicine is to tailor treatment decisions to individuals' characteristics. N-of-1 trials are within-person crossover trials that hold the promise of targeting individual-specific effects. While the idea behind N-of-1 trials might seem simple, analyzing and interpreting N-of-1 trials is not straightforward. Here we ground N-of-1 trials in a formal causal inference framework and formalize intuitive claims from the N-of-1 trials literature. We focus on causal inference from a single N-of-1 trial and define a conditional average treatment effect (CATE) that represents a target in this setting, which we call the U-CATE. We discuss assumptions sufficient for identification and estimation of the U-CATE under different causal models where the treatment schedule is assigned at baseline. A simple mean difference is an unbiased, asymptotically normal estimator of the U-CATE in simple settings. We also consider settings where carryover effects, trends over time, time-varying common causes of the outcome, and outcome-outcome effects are present. In these more complex settings, we show that a time-varying g-formula identifies the U-CATE under explicit assumptions. Finally, we analyze data from N-of-1 trials about acne symptoms and show how different assumptions about the data generating process can lead to different analytical strategies.