Population metrics for suicide events: a causal inference approach
Bossarte, Robert, M.
Kane, Cathleen, P.
Tu, Xin, M.
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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 attempts 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 attempts within each of the risk level and then taking a weighted sum of the conditional probabilities. The metrics provide unbiased estimates of the risk of suicide attempts. Simulation studies and a real data example will be used to demonstrate the proposed metrics.
CitationHe, H., Lu, N., Stephens, B., Xia, Y., Bossarte, R. M., Kane, C. P., . . . Tu, X. M. (2019). Population metrics for suicide events: A causal inference approach. Statistical Methods in Medical Research, 28(2), 503-514. doi:10.1177/0962280217729843
effective sample size