Application of weighted adjustments of dropout rates in sensitivity analysis of medical repeated measurements data and implementation with SAS

Abstract

Objective: To explore the application of weighted adjustments of dropout rates in sensitivity analysis of medically repeated measurements data and the implementation with SAS 9.4 software. Methods: By compiling SAS codes, mixed-effects models for repeated measures were used to conduct the covariance analysis of multivariable repeated measurements data. Meanwhile, the overall dropout rate and the dropout rates of each group were used to make weighted adjustments by applying pattern-mixture models, which was considered to be a sensitivity analysis to validate the stability of results. Results: The dropout rates of placebo group, low-dose and high-dose groups were 8.77%, 11.79% and 16.15%, respectively, the differences were significant (P=0.025). The results of mixed-effects models for repeated measures showed the differences of curative effect indicators changes from baselines of between high-dose, low-dose groups and placebo group were significant (P=0.008 and P=0.002). The results of pattern-mixture models considering weighted adjustments of the respective groups' dropout rates were consistent with those of mixed-effects models for repeated measures. Conclusions: The pattern-mixture models considering weighted adjustments of dropout rates can be used in the sensitivity analysis of repeated measurements data. The SAS codes can provide a practical basis for the popularization and application of weighted adjustments of dropout rates in the sensitivity analysis of repeated measurements data.