Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c}
LITTLE
Missing data, due to nonresponse or incomplete records, may lead to information biases. To account for the potential effects of missingness and data missing at random or with no known pattern, we will use multiple imputation techniques. Multiple imputation datasets of any missing data will be made under a joint model for the variable in question and a missingness indicator conditional on the fully observed data. Estimates obtained from the multiply imputed datasets will be combined using Rubin’s Rule (Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons.)