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.

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Keywords causal inference, effect measures, meta-analysis, risk difference, risk ratio
Persistent URL dx.doi.org/10.1515/em-2016-0014, hdl.handle.net/1765/112990
Journal Epidemiologic Methods
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Citation
Huitfeldt, A. (Anders), Goldstein, A. (Andrew), & Swanson, S.A. (2018). The choice of effect measure for binary outcomes: Introducing counterfactual outcome state transition parameters. Epidemiologic Methods, 7(1). doi:10.1515/em-2016-0014