LASSO-Based Survival Prediction Modeling with Multiply Imputed Data: A Case Study in Tuberculosis Mortality Prediction

Using health administrative datasets for developing prediction models is always challenging due to missing values in key predictors. Multiple imputation has been recommended to deal with missing predictor values. However, predicting survival outcomes using regularized regression, for example, Cox-LASSO, faces limitations as these methods are incompatible with pooling model outputs from multiple imputed data using Rubin’s rule. In this study, we explored the performance of three statistical methods in developing prediction models with Cox-LASSO on multiply imputed data: prediction average, performance average, and stacked. We considered two hyperparameter selection techniques: minimum-lambda that gives the minimum cross-validated prediction error and 1SE-lambda that selects more parsimonious models. We also conducted plasmode simulations with varying the events per parameter. The stacked approach provided the most robust predictions in our case study of predicting tuberculosis mortality and simulations, producing a time-dependent c-statistic of 0.93 and a well-calibrated calibration plot. The 1SE-lambda technique resulted in underfitting of the models in most scenarios, both in case study and simulation. Our findings advocate the stacked method with minimum-lambda as an effective technique for combining LASSO-based prediction outputs from multiply imputed data. We shared reproducible R codes for future researchers to facilitate the adoption of these methodologies in their research.