Journal article
Validating effectiveness of subgroup identification for longitudinal data
Statistics in medicine, Vol.37(1), pp.98-106
01/15/2018
DOI: 10.1002/sim.7500
PMID: 28948635
Abstract
In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness; however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over time. With the individual treatment effects and characteristics of the patients, various classification algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. In this paper, we further propose a simple validation approach which not only determines if the subgroups used are appropriate and beneficial but also compares methods to predict individual treatment effects. This entire procedure is readily implemented by existing packages in statistical software. The effectiveness of the proposed method is confirmed with simulation studies and analysis of data from the Women Entering Care study on depression.
Details
- Title: Subtitle
- Validating effectiveness of subgroup identification for longitudinal data
- Creators
- Nichole Andrews - Western Michigan UniversityHyunkeun Cho - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Statistics in medicine, Vol.37(1), pp.98-106
- DOI
- 10.1002/sim.7500
- PMID
- 28948635
- ISSN
- 0277-6715
- eISSN
- 1097-0258
- Number of pages
- 9
- Language
- English
- Date published
- 01/15/2018
- Academic Unit
- Biostatistics
- Record Identifier
- 9983997311702771
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