A New RCM Approach to Survival Analysis: The Conditional-Incidence-Rate Model

Abstract

This article introduces a new causal analytic method for survival analysis that retains the framework of Rubin’s causal model as an alternative to the marginal structural model (MSM). The major limitation of the MSM is a systematic bias in the effects of past treatments when the method is applied to the hazard rate analysis of nonrepeatable events in the presence of unobserved heterogeneity. This systematic bias is demonstrated in the article. The method introduced here assumes a semiparametric conditional-incidence-rate model and provides consistent estimates of the effects of present and past treatments on the conditional cumulative-incidence rate in the analysis of nonrepeatable events in the presence of unobserved heterogeneity. Unlike the MSM, which requires a sequential and cumulative use of the inverse-probability-of-treatment weighting many times for data with many time points, the new method uses the inverse-probability-of-treatment weighing only twice sequentially for estimation of the present and past treatment effects at each time of entry into treatment, and not cumulatively across different treatment entry times. Analysis of the conditional-incidence rate can also provide a more efficient parameter estimate for the treatment effect than the hazard rate model in cases where a majority of sample persons experience the event and thereby cease to be members of the risk set of the hazard rate during the period of observation. An application to an analysis of sexual initiation demonstrates that leaving home promotes sexual initiation, especially premarital sexual initiation, because it greatly increases the rate of premarital sexual initiation during the year after leaving home.