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A martingale residual diagnostic for longitudinal and recurrent event data
Entisar Elgmati1, Daniel Farewell, Robin Henderson
1Department of Mathematics and Statistics, Newcastle University, Newcastle, UK.
Lifetime Data Analysis
|August 25, 2009
Summary
We propose a new method to assess event history models, even with measurement error. This approach for longitudinal and recurrent event data offers a more robust fit assessment than standard techniques.
Area of Science:
- Statistics
- Biostatistics
- Epidemiology
Background:
- Assessing the fit of event history models is crucial for reliable data analysis.
- Traditional methods, like plotting standardized martingale residuals, have limitations, especially with measurement error.
Purpose of the Study:
- To develop and validate an alternative procedure for assessing event history model fit.
- To create a method robust to measurement error and applicable to diverse data types (longitudinal, recurrent events).
Main Methods:
- Developed an alternative procedure based on the covariance of martingale residuals over time.
- Proposed that the covariance between residuals at t0 and t > t0 should be time-independent.
- Created a test statistic from increments in estimated covariances to detect model misspecification.
Main Results:
- The proposed method is valid in the presence of measurement error.
- The approach is applicable to both longitudinal and recurrent event data.
- Investigated the properties of the test statistic under various model misspecification scenarios.
Conclusions:
- The novel covariance-based method provides a more reliable assessment of event history model fit.
- This technique enhances the analysis of studies involving measurement error and complex event data.
- Demonstrated utility through applications in infant diarrhea studies and schizophrenia treatment research.
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