These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
2. A sparse additive model for treatment effect-modifier selection. Park H; Petkova E; Tarpey T; Ogden RT Biostatistics; 2022 Apr; 23(2):412-429. PubMed ID: 32808656 [TBL] [Abstract][Full Text] [Related]
3. A constrained single-index regression for estimating interactions between a treatment and covariates. Park H; Petkova E; Tarpey T; Ogden RT Biometrics; 2021 Jun; 77(2):506-518. PubMed ID: 32573759 [TBL] [Abstract][Full Text] [Related]
4. Robust regression for optimal individualized treatment rules. Xiao W; Zhang HH; Lu W Stat Med; 2019 May; 38(11):2059-2073. PubMed ID: 30740747 [TBL] [Abstract][Full Text] [Related]
5. Causal effect models for realistic individualized treatment and intention to treat rules. van der Laan MJ; Petersen ML Int J Biostat; 2007; 3(1):Article 3. PubMed ID: 19122793 [TBL] [Abstract][Full Text] [Related]
6. Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules. Butler EL; Laber EB; Davis SM; Kosorok MR Biometrics; 2018 Mar; 74(1):18-26. PubMed ID: 28742260 [TBL] [Abstract][Full Text] [Related]
7. A single-index model with a surface-link for optimizing individualized dose rules. Park H; Petkova E; Tarpey T; Ogden RT J Comput Graph Stat; 2022; 31(2):553-562. PubMed ID: 35873662 [TBL] [Abstract][Full Text] [Related]
8. Testing interaction between treatment and high-dimensional covariates in randomized clinical trials. Callegaro A; Spiessens B; Dizier B; Montoya FU; van Houwelingen HC Biom J; 2017 Jul; 59(4):672-684. PubMed ID: 27763683 [TBL] [Abstract][Full Text] [Related]
9. Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees. Logan BR; Sparapani R; McCulloch RE; Laud PW Stat Methods Med Res; 2019 Apr; 28(4):1079-1093. PubMed ID: 29254443 [TBL] [Abstract][Full Text] [Related]
10. Individualized treatment rules: generating candidate clinical trials. Petersen ML; Deeks SG; van der Laan MJ Stat Med; 2007 Nov; 26(25):4578-601. PubMed ID: 17450501 [TBL] [Abstract][Full Text] [Related]
12. Statistical learning of origin-specific statically optimal individualized treatment rules. van der Laan MJ; Petersen ML Int J Biostat; 2007; 3(1):Article 6. PubMed ID: 19122792 [TBL] [Abstract][Full Text] [Related]
13. Nonparametric machine learning for precision medicine with longitudinal clinical trials and Bayesian additive regression trees with mixed models. Spanbauer C; Sparapani R Stat Med; 2021 May; 40(11):2665-2691. PubMed ID: 33751659 [TBL] [Abstract][Full Text] [Related]
14. Analyzing sequentially randomized trials based on causal effect models for realistic individualized treatment rules. Bembom O; van der Laan MJ Stat Med; 2008 Aug; 27(19):3689-716. PubMed ID: 18407580 [TBL] [Abstract][Full Text] [Related]
15. Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine. Belhechmi S; Le Teuff G; De Bin R; Rotolo F; Michiels S BMC Bioinformatics; 2023 Mar; 24(1):96. PubMed ID: 36927444 [TBL] [Abstract][Full Text] [Related]
17. Measuring the performance of prediction models to personalize treatment choice. Efthimiou O; Hoogland J; Debray TPA; Seo M; Furukawa TA; Egger M; White IR Stat Med; 2023 Apr; 42(8):1188-1206. PubMed ID: 36700492 [TBL] [Abstract][Full Text] [Related]
18. Treatment decisions based on scalar and functional baseline covariates. Ciarleglio A; Petkova E; Ogden RT; Tarpey T Biometrics; 2015 Dec; 71(4):884-94. PubMed ID: 26111145 [TBL] [Abstract][Full Text] [Related]