Population metrics for suicide events: A causal inference approach. (February 2019)

• Record Type: Journal Article
• Authors: He, Hua; Lu, Naiji; Stephens, Brady; Xia, Yinglin; Bossarte, Robert M; Kane, Cathleen P; Tang, Wan; Tu, Xin M
• Is Part Of: Statistical methods in medical research. Volume 28:Number 2(2019)
• Page Start: 503
• Page End: 514
• Publication Date: 2019-02
• Subjects: Causal inference -- effective sample size -- metrics -- multiple events -- population heterogeneity -- potential outcome; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217729843
• Languages: English
• ISSNs: 0962-2802

Abstract

Large-scale public health prevention initiatives and interventions are a very important component to current public health strategies. But evaluating effects of such large-scale prevention/intervention faces a lot of challenges due to confounding effects and heterogeneity of study population. In this paper, we will develop metrics to assess the risk for suicide events based on causal inference framework when the study population is heterogeneous. The proposed metrics deal with the confounding effect by first estimating the risk of suicide events within each of the risk levels, number of prior attempts, and then taking a weighted sum of the conditional probabilities. The metrics provide unbiased estimates of the risk of suicide events. Simulation studies and a real data example will be used to demonstrate the proposed metrics.