title: Reweighting estimators for Cox regression with missing covariate data: analysis of insulin resistance and risk of stroke in the Northern Manhattan Study.
abstract: Incomplete covariates often obscure analysis results from a Cox regression. In an analysis of the Northern Manhattan Study (NOMAS) to determine the influence of insulin resistance on the incidence of stroke in nondiabetic individuals, insulin level is unknown for 34.1% of the subjects. The available data suggest that the missingness mechanism depends on outcome variables, which may generate biases in estimating the parameters of interest if only using the complete observations. This article aimed to introduce practical strategies to analyze the NOMAS data and present sensitivity analyses by using the reweighting method in standard statistical packages. When the data set structure is in counting process style, the reweighting estimates can be obtained by built-in procedures with variance estimated by the jackknife method. Simulation results indicate that the jackknife variance estimate provides reasonable coverage probability in moderate sample sizes. We subsequently conducted sensitivity analyses for the NOMAS data, showing that the risk estimates are robust to a variety of missingness mechanisms. At the end of this article, we present the core SAS and R programs used in the analysis.
authors.affiliation: Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA. Qiang.Xu@fda.hhs.gov
authors.forename: Qiang
authors.initials: Q
authors.lastname: Xu
authors.forename: Myunghee Cho
authors.initials: MC
authors.lastname: Paik
authors.forename: Tatjana
authors.initials: T
authors.lastname: Rundek
authors.forename: Mitchell S V
authors.initials: MS
authors.lastname: Elkind
authors.forename: Ralph L
authors.initials: RL
authors.lastname: Sacco
mesh_terms: D000367:Age Factors; D000428:Alcohol Drinking / Q000009:adverse effects; D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D005260:Female; D006801:Humans; D015994:Incidence; D007333:Insulin Resistance; D053208:Kaplan-Meier Estimate; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D009519:New York City / Q000453:epidemiology; D044382:Population Groups / Q000706:statistics & numerical data; D011336:Probability; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D018401:Sample Size; D012737:Sex Factors; D012907:Smoking / Q000009:adverse effects; D020521:Stroke / Q000453:epidemiology*