title: Reparameterizing the pattern mixture model for sensitivity analyses under informative dropout.
abstract: Pattern mixture models are frequently used to analyze longitudinal data where missingness is induced by dropout. For measured responses, it is typical to model the complete data as a mixture of multivariate normal distributions, where mixing is done over the dropout distribution. Fully parameterized pattern mixture models are not identified by incomplete data; Little (1993, Journal of the American Statistical Association 88, 125-134) has characterized several identifying restrictions that can be used for model fitting. We propose a reparameterization of the pattern mixture model that allows investigation of sensitivity to assumptions about nonidentified parameters in both the mean and variance, allows consideration of a wide range of nonignorable missing-data mechanisms, and has intuitive appeal for eliciting plausible missing-data mechanisms. The parameterization makes clear an advantage of pattern mixture models over parametric selection models, namely that the missing-data mechanism can be varied without affecting the marginal distribution of the observed data. To illustrate the utility of the new parameterization, we analyze data from a recent clinical trial of growth hormone for maintaining muscle strength in the elderly. Dropout occurs at a high rate and is potentially informative. We undertake a detailed sensitivity analysis to understand the impact of missing-data assumptions on the inference about the effects of growth hormone on muscle strength.
authors: Department of Statistics, Iowa State University, 102G Snedecor Hall, Ames, Iowa 50011, USA. mdaniels@iastate.edu
authors: M J
authors: MJ
authors: Daniels
authors: J W
authors: JW
authors: Hogan
mesh_terms: D000368:Aged; D001699:Biometry / Q000379:methods*; D019382:Human Growth Hormone / Q000627:therapeutic use; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D024510:Muscle Development; D018482:Muscle, Skeletal / Q000187:drug effects / Q000254:growth & development / Q000502:physiology; D010352:Patient Dropouts; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D019382:Human Growth Hormone