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Non‐ignorable missing covariate data in survival analysis: a case‐study of an International Breast Cancer Study Group trial
Author(s) -
Herring Amy H.,
Ibrahim Joseph G.,
Lipsitz Stuart R.
Publication year - 2004
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1046/j.1467-9876.2003.05168.x
Subject(s) - missing data , covariate , econometrics , observational study , statistics , proportional hazards model , computer science , mathematics
Summary. Non‐ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality‐of‐life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality‐of‐life data are likely to be non‐ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non‐ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.