Development and Validation of a Dynamically Updated Prediction Model for Attrition From Marine Recruit Training.

Abstract

Dijksma, I, Hof, MHP, Lucas, C, and Stuiver, MM. Development and validation of a dynamically updated prediction model for attrition from Marine recruit training. J Strength Cond Res 36(9): 2523–2529, 2022—Whether fresh Marine recruits thrive and complete military training programs, or fail to complete, is dependent on numerous interwoven variables. This study aimed to derive a prediction model for dynamically updated estimation of conditional dropout probabilities for Marine recruit training. We undertook a landmarking analysis in a Cox proportional hazard model using longitudinal data from 744 recruits from existing databases of the Marine Training Center in the Netherlands. The model provides personalized estimates of dropout from Marine recruit training given a recruit's baseline characteristics and time-varying mental and physical health status, using 21 predictors. We defined nonoverlapping landmarks at each week and developed a supermodel by stacking the landmark data sets. The final supermodel contained all but one a priori selected baseline variables and time-varying health status to predict the hazard of attrition from Marine recruit training for each landmark as comprehensive as possible. The discriminative ability (c-index) of the prediction model was 0.78, 0.75, and 0.73 in week one, week 4 and week 12, respectively. We used 10-fold cross-validation to train and evaluate the model. We conclude that this prediction model may help to identify recruits at an increased risk of attrition from training throughout the Marine recruit training and warrants further validation and updates for other military settings.