Estimation and extrapolation of optimal treatment and testing strategies

被引：152

作者：

Robins, James ^{[1
,3
]}

Orellana, Liliana ^{[2
]}

Rotnitzky, Andrea ^{[3
,4
]}

机构：

[1] Harvard Univ, Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA

[2] Univ Buenos Aires, FCEyN, Buenos Aires, DF, Argentina

[3] Di Tella Univ, Dept Econ, Buenos Aires, DF, Argentina

[4] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA

来源：

STATISTICS IN MEDICINE | 2008年 / 27卷 / 23期

关键词：

dynamic regime; causal inference; marginal structural model; value of information;

D O I：

10.1002/sim.3301

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health-care system to determine the optimal treatment regime I or biologically similar subjects in a second health-care system when, for cultural. logistical. or financial reasons, the two health-care systems differ (and will continue to differ) in the frequency of, and reasons tor, both laboratory tests and physician visits. Finally. we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies. these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction. Copyright (C) 2008 John Wiley & Sons. Ltd.

引用

页码：4678 / 4721

页数：44

共 15 条

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[12]

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[13]

ROBINS JM, 2007, STAT SCI IN PRESS

[14]

ROBINS JM, 1999, STAT MODELS EPIDEMIO, P1

[15]

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