Nonignorable Missingness in Matched Case–Control Data Analyses

$48.00 plus tax (Refund Policy)

Download / Buy Article:

Abstract:

Summary. 

Matched case–control data analysis is often challenged by a missing covariate problem, the mishandling of which could cause bias or inefficiency. Satten and Carroll (2000, Biometrics56, 384–388) and other authors have proposed methods to handle missing covariates when the probability of missingness depends on the observed data, i.e., when data are missing at random. In this article, we propose a conditional likelihood method to handle the case when the probability of missingness depends on the unobserved covariate, i.e., when data are nonignorably missing. When the missing covariate is binary, the proposed method can be implemented using standard software. Using the Northern Manhattan Stroke Study data, we illustrate the method and discuss how sensitivity analysis can be conducted.

Keywords: Matched case–control study; Missing covariates; Nonignorable missingness

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.0006-341X.2004.00174.x

Publication date: June 1, 2004

Related content

Tools

Favourites

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more