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Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and
Andrea Marshall1, Douglas G Altman, Roger L Holder
1Centre for Statistics in Medicine, University of Oxford, Oxford, UK. andrea.marshall@warwick.ac.uk
BMC Medical Research Methodology
|July 30, 2009
Summary
Multiple imputation (MI) is key for handling missing data in prognostic models. New guidelines aim to improve how estimates are combined after MI, promoting wider use in studies.
Area of Science:
- Statistics
- Biostatistics
- Epidemiology
Background:
- Multiple imputation (MI) is a robust method for addressing missing covariate data in prognostic modeling.
- MI accounts for uncertainty in missing data by analyzing multiple imputed datasets.
- Combining estimates from imputed datasets requires methods that incorporate within- and between-imputation variability.
Purpose of the Study:
- To provide guidelines for combining estimates after multiple imputation in prognostic modeling studies.
- To identify current practices for combining estimates in the literature.
Main Methods:
- A literature review was conducted to assess current methods for combining estimates after MI.
- Guidelines were developed for appropriate estimation and combination techniques.
Main Results:
- Current literature inadequately reports methods for combining estimates after MI.
- Rubin's rules, without transformations, are the predominant method when any approach is stated.
- The normality assumption for Rubin's rules may not suit all prognostic model parameters.
Conclusions:
- Proposed simple guidelines can facilitate broader and more appropriate application of MI in prognostic modeling.
- Addressing limitations in combining estimates will enhance the utility of MI for handling missing data.
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