A Pilot Design for Observational Studies: Using Abundant Data Thoughtfully

TL;DR

This paper introduces a pilot design approach for observational studies that uses initial data collection to inform and improve study design, enhancing the accuracy of treatment effect estimates and sensitivity analyses.

Contribution

It proposes a novel pilot design methodology that leverages early observational data to optimize study design and analysis strategies in data-rich, control-poor settings.

Findings

Pilot design reduces within-set heterogeneity.

Improves treatment effect estimation accuracy.

Enhances sensitivity analysis of unobserved confounding.

Abstract

Observational studies often benefit from an abundance of observational units. This can lead to studies that -- while challenged by issues of internal validity -- have inferences derived from sample sizes substantially larger than randomized controlled trials. But is the information provided by an observational unit best used in the analysis phase? We propose the use of `pilot design,' in which observations are expended in the design phase of the study, and the post-treatment information from these observations is used to improve study design. In modern observational studies, which are data rich but control poor, pilot designs can be used to gain information about the structure of post-treatment variation. This information can then be used to improve instrumental variable designs, propensity score matching, doubly-robust estimation, and other observational study designs. We illustrate one version of a pilot design, which aims to reduce within-set heterogeneity and improve performance in sensitivity analyses. This version of a pilot design expends observational units during the design phase to fit a prognostic model, avoiding concerns of overfitting. Additionally, it enables the construction of `Assignment-Control (AC) plots,' which visualize the relationship between propensity and prognostic scores. We first show some examples of these plots, then we demonstrate in a simulation setting how this alternative use of the observations can lead to gains in terms of both treatment effect estimation and sensitivity analyses of unobserved confounding.