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Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions
- Source :
- Statistics in Medicine, 2, 33, 219-237
- Publication Year :
- 2013
- Publisher :
- Wiley, 2013.
-
Abstract
- When two alternative treatments (A and B) are available, some subgroup of patients may display a better outcome with treatment A than with B, whereas for another subgroup, the reverse may be true. If this is the case, a qualitative (i.e., disordinal) treatment–subgroup interaction is present. Such interactions imply that some subgroups of patients should be treated differently and are therefore most relevant for personalized medicine. In case of data from randomized clinical trials with many patient characteristics that could interact with treatment in a complex way, a suitable statistical approach to detect qualitative treatment–subgroup interactions is not yet available. As a way out, in the present paper, we propose a new method for this purpose, called QUalitative INteraction Trees (QUINT). QUINT results in a binary tree that subdivides the patients into terminal nodes on the basis of patient characteristics; these nodes are further assigned to one of three classes: a first for which A is better than B, a second for which B is better than A, and an optional third for which type of treatment makes no difference. Results of QUINT on simulated data showed satisfactory performance, with regard to optimization and recovery. Results of an application to real data suggested that, compared with other approaches, QUINT provided a more pronounced picture of the qualitative interactions that are present in the data. Copyright © 2013 John Wiley & Sons, Ltd.
- Subjects :
- Adult
Statistics and Probability
Operations research
Epidemiology
Patient characteristics
Breast Neoplasms
Subgroup analysis
LS - Life Style
computer.software_genre
Outcome (game theory)
law.invention
Behavioural Changes
Randomized controlled trial
Moderator
law
Humans
Medicine
Qualitative interaction
Computer Simulation
Randomized Controlled Trials as Topic
Binary tree
business.industry
Decision Trees
Middle Aged
Moderation
Health
Treatment efficacy
Data Interpretation, Statistical
Simulated data
Female
ELSS - Earth, Life and Social Sciences
Personalized medicine
Artificial intelligence
Healthy for Life
business
Healthy Living
computer
Partitioning
Algorithms
Natural language processing
Subjects
Details
- ISSN :
- 02776715
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- Statistics in Medicine
- Accession number :
- edsair.doi.dedup.....dbccf794a9d94d79e441b9d6fb6594ff