Using ordinal regression for advancing the understanding of distinct suicide outcomes

As recent advances in suicide research have underscored the importance of studying distinct suicide outcomes (i.e., suicidal thinking vs. behavior), there is a need to consider the theoretical meaningfulness of our statistical approach(es). As an alternative to more popular statistical methods, we introduce ordinal regression, detailing specific forms that are well-aligned to examine outcomes specific to suicide research.

Ordinal regression models allow for assessment of the influences of covariates on the experience of lower (i.e., suicidal ideation) to higher (i.e., suicidal planning) suicide risk outcomes.

As an empirical application, we fit a sequential ordinal regression model with 17 theoretically selected covariates and modeled category specific effects for each covariate.

Results detailed from depression and presence of nonsuicidal self-injury demonstrate the utility of ordinal regression in consideration of transitions across suicide outcomes. Ordinal regression models may be particularly informative in identifying risk factors unique to each suicide outcome, which has the potential to meaningfully inform theoretical models of suicide and suicide risk prediction.