动态状态的边际均值模型。
Marginal Mean Models for Dynamic Regimes.
作者信息
Murphy S A, van der Laan M J, Robins J M
出版信息
J Am Stat Assoc. 2001 Dec 1;96(456):1410-1423. doi: 10.1198/016214501753382327.
Abstract
A dynamic treatment regime is a list of rules for how the level of treatment will be tailored through time to an individual's changing severity. In general, individuals who receive the highest level of treatment are the individuals with the greatest severity and need for treatment. Thus there is planned selection of the treatment dose. In addition to the planned selection mandated by the treatment rules, the use of staff judgment results in unplanned selection of the treatment level. Given observational longitudinal data or data in which there is unplanned selection, of the treatment level, the methodology proposed here allows the estimation of a mean response to a dynamic treatment regime under the assumption of sequential randomization.
摘要
动态治疗方案是一系列规则，规定了治疗水平如何随着时间根据个体病情严重程度的变化进行调整。一般来说，接受最高治疗水平的个体是病情最严重且最需要治疗的个体。因此，治疗剂量是经过规划选择的。除了治疗规则规定的规划选择外，工作人员的判断也会导致治疗水平的非规划选择。对于观察性纵向数据或存在治疗水平非规划选择的数据，本文提出的方法允许在序列随机化假设下估计对动态治疗方案的平均反应。
相似文献
1
Marginal Mean Models for Dynamic Regimes.
J Am Stat Assoc. 2001 Dec 1;96(456):1410-1423. doi: 10.1198/016214501753382327.
2
Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content.
Int J Biostat. 2010;6(2):Article 8.
3
Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes.
Obs Stud. 2023;9(4):25-48. doi: 10.1353/obs.2023.a906627.
4
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
5
Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part II: proofs of results.
Int J Biostat. 2010 Mar 3;6(2):Article 9. doi: 10.2202/1557-4679.1242.
6
Dynamic treatment regimes: practical design considerations.
Clin Trials. 2004 Feb;1(1):9-20. doi: 10.1191/1740774s04cn002oa.
7
Interpretable Dynamic Treatment Regimes.
J Am Stat Assoc. 2018;113(524):1541-1549. doi: 10.1080/01621459.2017.1345743. Epub 2018 Nov 14.
8
Causal effect models for realistic individualized treatment and intention to treat rules.
Int J Biostat. 2007;3(1):Article 3. doi: 10.2202/1557-4679.1022.
9
Estimation of optimal dynamic treatment regimes.
Clin Trials. 2014 Aug;11(4):400-407. doi: 10.1177/1740774514532570. Epub 2014 May 28.
10
Bayesian inference for optimal dynamic treatment regimes in practice.
Int J Biostat. 2023 May 17;19(2):309-331. doi: 10.1515/ijb-2022-0073. eCollection 2023 Nov 1.
引用本文的文献
1
Synergistic self-learning approach to establishing individualized treatment rules from multiple benefit outcomes in a calcium supplementation trial.
J R Stat Soc Ser C Appl Stat. 2025 Feb 14;74(4):925-945. doi: 10.1093/jrsssc/qlaf008. eCollection 2025 Nov.
2
A MARGINAL STRUCTURAL MODEL FOR PARTIAL COMPLIANCE IN SMARTS.
Ann Appl Stat. 2024 Jun;18(2):905-921. doi: 10.1214/21-aoas1586. Epub 2024 Apr 5.
3
Nonparametric assessment of regimen response curve estimators.
Biometrics. 2025 Apr 2;81(2). doi: 10.1093/biomtc/ujaf066.
4
The gap-closing estimand: A causal approach to study interventions that close disparities across social categories.
Sociol Methods Res. 2024 May;53(2):507-570. doi: 10.1177/00491241211055769. Epub 2022 Jan 13.
5
Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big Data.
J Gerontol A Biol Sci Med Sci. 2024 Dec 11;80(1). doi: 10.1093/gerona/glae269.
6
A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.
BMC Med Res Methodol. 2024 Sep 27;24(1):218. doi: 10.1186/s12874-024-02333-z.
7
Multiobjective tree-based reinforcement learning for estimating tolerant dynamic treatment regimes.
Biometrics. 2024 Jan 29;80(1). doi: 10.1093/biomtc/ujad017.
8
ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE.
Biometrika. 2023 Sep;110(3):645-662. doi: 10.1093/biomet/asac065. Epub 2022 Nov 24.
9
Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms.
Biostatistics. 2024 Jul 1;25(3):633-647. doi: 10.1093/biostatistics/kxad022.
10
Nonparametric Bayesian Q-learning for optimization of dynamic treatment regimes in the presence of partial compliance.
Stat Methods Med Res. 2023 Sep;32(9):1649-1663. doi: 10.1177/09622802231181223. Epub 2023 Jun 16.
本文引用的文献
1
Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.
Epidemiology. 2000 Sep;11(5):561-70. doi: 10.1097/00001648-200009000-00012.
2
Marginal structural models and causal inference in epidemiology.
Epidemiology. 2000 Sep;11(5):550-60. doi: 10.1097/00001648-200009000-00011.
3
Initial impact of the Fast Track prevention trial for conduct problems: II. Classroom effects. Conduct Problems Prevention Research Group.
J Consult Clin Psychol. 1999 Oct;67(5):648-57.
4
Initial impact of the Fast Track prevention trial for conduct problems: I. The high-risk sample. Conduct Problems Prevention Research Group.
J Consult Clin Psychol. 1999 Oct;67(5):631-47.
5
Rationale and design of a randomized clinical trial on prevention of stroke in isolated systolic hypertension. The Systolic Hypertension in the Elderly Program (SHEP) Cooperative Research Group.
J Clin Epidemiol. 1988;41(12):1197-208. doi: 10.1016/0895-4356(88)90024-8.
6
Systolic Hypertension in the Elderly Program (SHEP). Part 1: Rationale and design.
Hypertension. 1991 Mar;17(3 Suppl):II2-15. doi: 10.1161/01.hyp.17.3_suppl.ii2.
Zotero 插件