B117 - Methods to deal with missing data in ALSPAC - 01/06/2003
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.