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Social Scientists and Biostatisticians often employ conventional parametric survival or mixture cure models (e.g., Weibull, Exponential) to analyze outcome variables in survival data that focus on the time until an event occurred or “failed” (Box-Steffensmeier & Zorn, 1999; Lee, Chakraborty, & Sun, 2017; Maller & Zhou, 1996). An important assumption underlying these models is that researchers record the date year, month or day in which an event or observation of interest failed (i.e., “terminated”) accurately. Yet events that are recorded as having failed at a given point in time can be inaccurately measured (Bagozzi, Joo, Kim, & Mukherjee, 2019; Clark, Bradburn, Love, & Altman, 2003; Schober & Vetter, 2018). Inaccurate measurement of this sort leads to a subset of misclassified failure cases in survival data in which some subjects are recorded as having failed or experienced the event of interest even though they in actuality “live on” past their recorded-failure point.

BayesMFSurv: An R Package to Estimate Bayesian Split-Population Survival Models With (and Without) Misclassified Failure Events