Analyses of data from longitudinal studies are often complicated by the presence of missing values, caused

by participant dropout or non-response. Failure to allow for this can lead to both biased and inefficient

statistical analyses. Analyses ignoring problems caused by missing data are common. Statistical research has

generated better ways to deal with these problems, but the methods are technically challenging. The

proposed research will focus on the application of multiple imputation (MI) - the most flexible available

method - in longitudinal studies. We will demonstrate the potential of MI to reduce bias and increase

precision in analyses of data from the ALSPAC birth cohort study and the ART-CC and ART-LINC HIV

cohort collaborations. We will also clarify the circumstances in which analyses allowing for missing data are

likely to have advantages over simpler methods. We will develop a framework for simulations that allow

evaluation of characteristics of imputation procedures, and use both simulations and analyses of longitudinal

data to examine how to deal with the model complexity that characterises application of these procedures.

We will adapt existing software to improve model diagnostics that may alert the user to problems in

imputation procedures and to facilitate sensitivity analyses that examine robustness of results to data that are

missing not at random MNAR). We will work with members of the CONSORT and STROBE groups, and

with journal editors, to provide guidelines on reporting analyses that deal with missing data.