The choice of effect measure for binary outcomes: Introducing counterfactual outcome state transition parameters

TL;DR

This paper introduces a new framework for selecting effect measures in binary outcome studies using counterfactual outcome state transition parameters linked to causal models, addressing limitations of traditional measures.

Contribution

It proposes a novel approach connecting effect measures to counterfactual causal models, providing insights into model specification and research generalization.

Findings

Counterfactual outcome state transition parameters are generally not identifiable without strong assumptions.

When these parameters are constant across populations, they have significant implications for analysis.

The framework aids in understanding when and how to choose effect measures based on causal assumptions.

Abstract

Standard measures of effect, including the risk ratio, the odds ratio, and the risk difference, are associated with a number of well-described shortcomings, and no consensus exists about the conditions under which investigators should choose one effect measure over another. In this paper, we introduce a new framework for reasoning about choice of effect measure by linking two separate versions of the risk ratio to a counterfactual causal model. In our approach, effects are defined in terms of "counterfactual outcome state transition parameters", that is, the proportion of those individuals who would not have been a case by the end of follow-up if untreated, who would have responded to treatment by becoming a case; and the proportion of those individuals who would have become a case by the end of follow-up if untreated who would have responded to treatment by not becoming a case. Although counterfactual outcome state transition parameters are generally not identified from the data without strong monotonicity assumptions, we show that when they stay constant between populations, there are important implications for model specification, meta-analysis, and research generalization.