| Title: | Efficient targeted learning of heterogeneous treatment effects for multiple subgroups |
| Journal: | Biometrics |
| Published: | 10 Dec 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36416173/ |
| DOI: | https://doi.org/10.1111/biom.13800 |
| URL: | https://arxiv.org/pdf/2211.14671 |
Publication 10805
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this paper, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimer's disease risk.</p>
6 Authors
- Waverly Wei
- Maya Petersen
- Mark J van der Laan
- Zeyu Zheng
- Chong Wu
- Jingshen Wang
1 Application
| Application ID | Title |
|---|---|
| 48240 | Integrative analysis of UK Biobank and other genetic and genomic datasets for complex disease detection and prevention |
Enabling scientific discoveries that improve human health