A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data.

ABSTRACT: We develop and demonstrate methods to perform sensitivity analyses to assess sensitivity to plausible departures from missing at random in incomplete repeated binary outcome data. We use multiple imputation in the not at random fully conditional specification framework, which includes one or more sensitivity parameters (SPs) for each incomplete variable. The use of an online elicitation questionnaire is demonstrated to obtain expert opinion on the SPs, and highest prior density regions are used alongside opinion pooling methods to display credible regions for SPs. We demonstrate that substantive conclusions can be far more sensitive to departures from the missing at random assumption (MAR) when control and intervention nonresponders depart from MAR differently, and show that the correlatio

SUBMITTER: Tompsett D 

PROVIDER: S-EPMC7612109 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature