Abstract:
:Pattern-mixture models (PMM) and selection models (SM) are alternative approaches for statistical analysis when faced with incomplete data and a nonignorable missing-data mechanism. Both models make empirically unverifiable assumptions and need additional constraints to identify the parameters. Here, we first introduce intuitive parameterizations to identify PMM for different types of outcome with distribution in the exponential family; then we translate these to their equivalent SM approach. This provides a unified framework for performing sensitivity analysis under either setting. These new parameterizations are transparent, easy-to-use, and provide dual interpretation from both the PMM and SM perspectives. A Bayesian approach is used to perform sensitivity analysis, deriving inferences using informative prior distributions on the sensitivity parameters. These models can be fitted using software that implements Gibbs sampling.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Kaciroti NA,Raghunathan Tdoi
10.1002/sim.6302subject
Has Abstractpub_date
2014-11-30 00:00:00pages
4841-57issue
27eissn
0277-6715issn
1097-0258journal_volume
33pub_type
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