- Bayesian selection model jointly addresses rarity of suicidal behaviour and nonmonotone, nonignorable missingness in time-varying binary outcomes and predictors.
- Model combines mixed-effects complementary log-log regression for rare events with a missingness model, and probit mixed-effects model for the predictor with its missingness model.
- Empirical application and simulation study demonstrate parameter recovery and reveal bias when outcome and missingness sensitivity parameters are ignored.
Psychometrika. 2026 Jun 8:1-35. doi: 10.1017/psy.2026.10120. Online ahead of print.
ABSTRACT
This study investigates the relationship between daily interpersonal stress (binary, time-varying) and suicidal behavior (binary, time-varying) using 90 days of daily diary data from 106 adolescents assessed immediately after discharge from acute psychiatric treatment. It addresses two key complexities: the rarity of suicidal events and non-monotone, non-ignorable missingness in both the outcome and the predictor. Because existing methods often fail to accommodate these complexities, leading to biased estimates, a Bayesian selection model is specified. The model integrates a mixed-effects complementary log-log regression for rare events with a missingness model that accounts for non-monotone, non-ignorable missingness in the outcome. A probit mixed-effects model is used for the time-varying predictor, along with a corresponding missingness model for its non-monotone, non-ignorable missingness. Empirical results support the applicability of the specified model to longitudinal studies involving rare events and complex missing-data structures. Furthermore, a simulation study demonstrates parameter recovery and highlights bias in focal parameters when sensitivity parameters in the outcome and missingness models are ignored.
PMID:42253042 | DOI:10.1017/psy.2026.10120
Share Evidence Blueprint
Save to Google Notes
Search Google Scholar
Save as PDF
⭐ My Revision List
