Related Experiment Videos
Methods for the analysis of informatively censored longitudinal data
1Department of Biostatistics and Epidemiology, Cleveland Clinic Foundation, Ohio 44195.
Statistics in Medicine
|October 1, 1992
Summary
This study addresses informative censoring in longitudinal data, where early study termination relates to individual change rates. A new log-normal survival model approach is proposed to overcome bias in standard analyses.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Survival Analysis
Background:
- Standard linear random effects models fail with non-ignorably missing data.
- Informative censoring occurs when early termination probability links to true individual change rates.
- This bias affects likelihood-based analyses and least-squares slope averages.
Purpose of the Study:
- To review existing methods for analyzing informatively censored longitudinal data.
- To introduce a novel approach using a log-normal survival model.
- To discuss study design considerations for informative censoring.
Main Methods:
- Review of existing methodologies for informatively censored data.
- Development of a new approach based on a log-normal survival model.
- Utilizing the Expectation-Maximization (EM) algorithm for maximum likelihood estimation.
Main Results:
- The proposed log-normal survival model handles general unbalanced longitudinal data.
- The method effectively uses all available data, including single measurements.
- It offers a unified framework for estimating all model parameters.
Conclusions:
- The proposed log-normal survival model provides a robust solution for informative censoring in longitudinal studies.
- This approach mitigates bias inherent in standard analytical methods.
- It supports flexible data structures and comprehensive parameter estimation.