Variable Selection for Qualitative Interactions in Personalized Medicine While Controlling the Family-Wise Error Rate
| Autor: | Susan A. Murphy, Ji Zhu, Lacey Gunter |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
Subset Analysis Feature selection Familywise error rate Machine learning computer.software_genre Article law.invention Lasso (statistics) Randomized controlled trial law Econometrics Humans Medicine Family Pharmacology (medical) Precision Medicine Observer Variation Pharmacology business.industry Clinical trial Evaluation Studies as Topic Personalized medicine Artificial intelligence Biostatistics business computer |
| Zdroj: | Journal of Biopharmaceutical Statistics. 21:1063-1078 |
| ISSN: | 1520-5711 1054-3406 |
| DOI: | 10.1080/10543406.2011.608052 |
| Popis: | For many years, subset analysis has been a popular topic for the biostatistics and clinical trials literature. In more recent years, the discussion has focused on finding subsets of genomes which play a role in the effect of treatment, often referred to as stratified or personalized medicine. Though highly sought after, methods for detecting subsets with altering treatment effects are limited and lacking in power. In this article we discuss variable selection for qualitative interactions with the aim to discover these critical patient subsets. We propose a new technique designed specifically to find these interaction variables among a large set of variables while still controlling for the number of false discoveries. We compare this new method against standard qualitative interaction tests using simulations and give an example of its use on data from a randomized controlled trial for the treatment of depression. |
| Databáze: | OpenAIRE |
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