A standard concern with long term longitudinal studies is that of attrition over time. Together with initial non-response this typically leads to biased model estimates unless a suitable form of adjustment is carried out. The standard approach to this has been to compute weights based upon the propensity to respond and to drop out and then carry out weighted analyses to compensate for response bias. In the present paper [the authors] argue that this approach is statistically inefficient, because it drops incomplete data records, is inflexible, and in practice gives rise to undue...  [+] Show more

A standard concern with long term longitudinal studies is that of attrition over time. Together with initial non-response this typically leads to biased model estimates unless a suitable form of adjustment is carried out. The standard approach to this has been to compute weights based upon the propensity to respond and to drop out and then carry out weighted analyses to compensate for response bias. In the present paper [the authors] argue that this approach is statistically inefficient, because it drops incomplete data records, is inflexible, and in practice gives rise to undue complexity involving a proliferation of weighting systems for different analyses. Instead [the authors] set out an alternative approach that makes use of recently developed imputation procedures for handling missing data and show how this can be used to improve the quality of the statistical analysis. An example analysis is given using the Longitudinal Study of Australian Youth (LSAY).

Published abstract.