Using Multiple Imputation to Classify Potential Outcomes Subgroups

TL;DR

This paper introduces a method to classify patient subgroups based on how medical tests influence treatment decisions, using multiple imputation to handle missing data and improve causal inference accuracy.

Contribution

It proposes a novel subgroup classification approach that captures test influence on treatment, integrating multiple imputation for missing data and causal inference in medical testing analysis.

Findings

Explicit causal assumptions improve estimate precision

Bias occurs if potential outcomes independence is violated

Applied to breast cancer genomic testing and chemotherapy decisions

Abstract

With medical tests becoming increasingly available, concerns about over-testing and over-treatment dramatically increase. Hence, it is important to understand the influence of testing on treatment selection in general practice. Most statistical methods focus on average effects of testing on treatment decisions. However, this may be ill-advised, particularly for patient subgroups that tend not to benefit from such tests. Furthermore, missing data are common, representing large and often unaddressed threats to the validity of statistical methods. Finally, it is desirable to conduct analyses that can be interpreted causally. We propose to classify patients into four potential outcomes subgroups, defined by whether or not a patient's treatment selection is changed by the test result and by the direction of how the test result changes treatment selection. This subgroup classification naturally captures the differential influence of medical testing on treatment selections for different patients, which can suggest targets to improve the utilization of medical tests. We can then examine patient characteristics associated with patient potential outcomes subgroup memberships. We used multiple imputation methods to simultaneously impute the missing potential outcomes as well as regular missing values. This approach can also provide estimates of many traditional causal quantities. We find that explicitly incorporating causal inference assumptions into the multiple imputation process can improve the precision for some causal estimates of interest. We also find that bias can occur when the potential outcomes conditional independence assumption is violated; sensitivity analyses are proposed to assess the impact of this violation. We applied the proposed methods to examine the influence of 21-gene assay, the most commonly used genomic test, on chemotherapy selection among breast cancer patients.