When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data

Cain, Lauren E.; Robins, James M.; Lanoy, Émilie; Logan, Roger; Costagliola, Dominique; Hernán, Miguel A.

doi:10.2202/1557-4679.1212

Cited by 188 publications

(266 citation statements)

References 24 publications

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“…Propensity score methods could then be used to identify groups of patients whose probability of transfusion was close to 1 (or close to zero) and exclude them from analyses 77 . Second, observational analyses might mimic RCTs of liberal versus restrictive transfusion strategies, by following patients from the start of surgery and censoring those who depart from one or other strategy 78 . Such an approach was used to address the effect on mortality of commencing antiretroviral therapy at different CD4 count thresholds, in HIV-1 infected patients 79 .…”

Section: Discussionmentioning

confidence: 99%

Indications for red blood cell transfusion in cardiac surgery: a systematic review and meta-analysis

Patel

Avlonitis

Jones

et al. 2015

The Lancet Haematology

110

106

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(2015). Indications for red blood cell transfusion in cardiac surgery: A systematic review and meta-analysis. Lancet Haematology, 2(12), e543-e553. DOI: 10.1016/S2352-3026 (15) University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available:

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Section: Discussionmentioning

confidence: 99%

Indications for red blood cell transfusion in cardiac surgery: a systematic review and meta-analysis

Patel

Avlonitis

Jones

et al. 2015

The Lancet Haematology

110

106

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“…AIs are also known by a variety of different names. In the statistical literature, they are commonly referred to as dynamic treatment regimes [17,19,[25][26][27][28][29][30][31][32][33][34][35][36][37] (or, more appropriately, regimens). In the mental health and substance use literatures, they are more commonly known as adaptive treatment strategies [38,39].…”

Section: Adaptive Interventionsmentioning

confidence: 99%

Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research

Almirall

Nahum‐Shani

Sherwood

et al. 2014

Behav. Med. Pract. Policy Res.

349

274

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The management of many health disorders often entails a sequential, individualized approach whereby treatment is adapted and readapted over time in response to the specific needs and evolving status of the individual. Adaptive interventions provide one way to operationalize the strategies (e.g., continue, augment, switch, step-down) leading to individualized sequences of treatment. Often, a wide variety of critical questions must be answered when developing a highquality adaptive intervention. Yet, there is often insufficient empirical evidence or theoretical basis to address these questions. The Sequential Multiple Assignment Randomized Trial (SMART)-a type of research design-was developed explicitly for the purpose of building optimal adaptive interventions by providing answers to such questions. Despite increasing popularity, SMARTs remain relatively new to intervention scientists. This manuscript provides an introduction to adaptive interventions and SMARTs. We discuss SMART design considerations, including common primary and secondary aims. For illustration, we discuss the development of an adaptive intervention for optimizing weight loss among adult individuals who are overweight. KEYWORDS

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“…ÞÞ is a function of t only, and this component thus may be ignored in the definition of the IPW weights because the numerator of the weights for IPW estimation of a dynamic MSM for discrete-time hazards pooled over time may be any function of both t and rules d 28,37 . The remaining components of the right-censoring mechanism were estimated based on two separate logistic models for (i) rightcensoring because of disenrollment from the health plan P A dis 2 t ð Þ ¼ 0jA end…”

Section: Appendix F Estimation Of the Ipw Weights In The Dm Studymentioning

confidence: 99%

Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes

Neugebauer

Fireman

Roy

et al. 2012

Pharmacoepidemiology and Drug

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Purpose Chronic disease care typically involves treatment decisions that are frequently adjusted to the patient's evolving clinical course (e.g., hemoglobin A1c monitoring and treatment intensification in diabetes patients). Thus, in comparative effectiveness and safety research (CER), it often is less clinically relevant to contrast the health effects of static treatment decisions than to compare the effectiveness of competing medical guidelines, that is, adaptive treatment strategies that map the patient's unfolding clinical course to subsequent treatment decisions. With longitudinal observational studies, treatment decisions at any point in time may be influenced by clinical factors that also are risk factors for the outcome of interest. Such time-dependent confounders cannot be properly handled with standard statistical approaches, because such confounders may be influenced by previous treatment decisions and may thus lie on causal pathways between the very outcomes and early treatment decisions whose effects are under study. Under explicit assumptions, we motivate the application of inverse probability weighting estimation to fit dynamic marginal structural models (MSMs) in observational studies to address pragmatic CER questions and properly adjust for time-dependent confounding and informative loss to follow-up. Methods We review the principles behind this modeling approach and describe its application in an observational study of type 2 diabetes patients to investigate the comparative effectiveness of four adaptive treatment intensification strategies for glucose control on subsequent development or progression of urinary albumin excretion. Results Results indicate a protective effect of more aggressive treatment intensification strategies in patients already on two or more oral agents or basal insulin. These conclusions are concordant with recent randomized trials. Conclusions Inverse probability weighting estimation to fit dynamic MSM is a viable and appealing alternative to inadequate standard modeling approaches in many CER problems where time-dependent confounding and informative loss to follow-up are expected. Copyright

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When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data

Cited by 188 publications

References 24 publications

Indications for red blood cell transfusion in cardiac surgery: a systematic review and meta-analysis

Indications for red blood cell transfusion in cardiac surgery: a systematic review and meta-analysis

Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research

Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes

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