Journal article
Complete imputation of missing repeated categorical data: one‐sample applications
Statistics in medicine, Vol.21(2), pp.203-217
01/30/2002
DOI: 10.1002/sim.982
PMID: 11782060
Abstract
Longitudinal studies with repeated measures are often subject to non‐response. Methods currently employed to alleviate the difficulties caused by missing data are typically unsatisfactory, especially when the cause of the missingness is related to the outcomes. We present an approach for incomplete categorical data in the repeated measures setting that allows missing data to depend on other observed outcomes for a study subject. The proposed methodology also allows a broader examination of study findings through interpretation of results in the framework of the set of all possible test statistics that might have been observed had no data been missing. The proposed approach consists of the following general steps. First, we generate all possible sets of missing values and form a set of possible complete data sets. We then weight each data set according to clearly defined assumptions and apply an appropriate statistical test procedure to each data set, combining the results to give an overall indication of significance. We make use of the EM algorithm and a Bayesian prior in this approach. While not restricted to the one‐sample case, the proposed methodology is illustrated for one‐sample data and compared to the common complete‐case and available‐case analysis methods. Copyright © 2002 John Wiley & Sons, Ltd.
Details
- Title: Subtitle
- Complete imputation of missing repeated categorical data: one‐sample applications
- Creators
- Colin P WestJeffrey D Dawson
- Resource Type
- Journal article
- Publication Details
- Statistics in medicine, Vol.21(2), pp.203-217
- DOI
- 10.1002/sim.982
- PMID
- 11782060
- NLM abbreviation
- Stat Med
- ISSN
- 0277-6715
- eISSN
- 1097-0258
- Publisher
- John Wiley & Sons, Ltd; Chichester, UK
- Number of pages
- 15
- Grant note
- Merck MD/PhD Biostatistics Fellowship
- Language
- English
- Date published
- 01/30/2002
- Academic Unit
- Public Health Administration; Biostatistics
- Record Identifier
- 9983997351202771
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