| Title |
Cox regression with missing covariate data using a modified partial likelihood method
|
|---|---|
| Published in |
Lifetime Data Analysis, October 2015
|
| DOI | 10.1007/s10985-015-9351-y |
| Pubmed ID | |
| Authors |
Torben Martinussen, Klaus K. Holst, Thomas H. Scheike |
| Abstract |
Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard function with the idea being to profile out this function before carrying out the estimation of the parameter of interest. In this step one uses a Breslow type estimator to estimate the cumulative baseline hazard function. We focus on the situation where the observed covariates are categorical which allows us to calculate estimators without having to assume anything about the distribution of the covariates. We show that the proposed estimator is consistent and asymptotically normal, and derive a consistent estimator of the variance-covariance matrix that does not involve any choice of a perturbation parameter. Moderate sample size performance of the estimators is investigated via simulation and by application to a real data example. |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 29% |
| Librarian | 1 | 14% |
| Researcher | 1 | 14% |
| Student > Master | 1 | 14% |
| Unknown | 2 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 2 | 29% |
| Agricultural and Biological Sciences | 1 | 14% |
| Social Sciences | 1 | 14% |
| Engineering | 1 | 14% |
| Unknown | 2 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 October 2016.
All research outputs
#15,390,684
of 22,896,955 outputs
Outputs from Lifetime Data Analysis
#55
of 120 outputs
Outputs of similar age
#166,051
of 283,376 outputs
Outputs of similar age from Lifetime Data Analysis
#1
of 2 outputs
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