Journal
BIOMETRICS
Volume 78, Issue 3, Pages 908-921Publisher
WILEY
DOI: 10.1111/biom.13472
Keywords
cross‐ sectional incidence estimation; EM algorithm; HIV; AIDS; interval‐ censored covariates; joint modeling
Funding
- NIAID of the National Institutes of Health (NIH) [R01-AI095068]
- Division of Intramural Research, NIAID
Ask authors/readers for more resources
The study introduces a method for generalized linear regression with interval-censored covariates, which indirectly infers the distribution of the covariate of interest compared to other conventional approaches. The proposed method shows less bias but slight increases in standard error when compared to midpoint analysis and uniform imputation methods.
A method for generalized linear regression with interval-censored covariates is described, extending previous approaches. A scenario is considered in which an interval-censored covariate of interest is defined as a function of other variables. Instead of directly modeling the distribution of the interval-censored covariate of interest, the distributions of the variables which determine that covariate are modeled, and the distribution of the covariate of interest is inferred indirectly. This approach leads to an estimation procedure using the Expectation-Maximization (EM) algorithm. The performance of this approach is compared to two alternative approaches, one in which the censoring interval midpoints are used as estimates of the censored covariate values, and another in which the censored values are multiply imputed using uniform distributions over the censoring intervals. A simulation framework is constructed to assess these methods' accuracies across a range of scenarios. The proposed approach is found to have less bias than midpoint analysis and uniform imputation, at the cost of small increases in standard error.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available