Bayesian models for subgroup analysis in clinical trials

Jones, Hayley E; Ohlssen, David; Neuenschwander, Beat; Racine, Amy; Branson, Michael

doi:10.1177/1740774510396933

Cited by 91 publications

(80 citation statements)

References 41 publications

(77 reference statements)

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“…Additional discussions regarding the application of hierarchical modeling to subgroup analysis may be found in Lipsky 13 and Jones. 40 …”

Section: Resultsmentioning

confidence: 99%

Bayesian Hierarchical Modeling and the Integration of Heterogeneous Information on the Effectiveness of Cardiovascular Therapies

Kwok

Lewis²

2011

Circ: Cardiovascular Quality and Outcomes

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Abstract-When making therapeutic decisions for an individual patient or formulating treatment guidelines on a population level, it is often necessary to utilize information arising from different study designs, settings, or treatments. In clinical practice, heterogeneous information is frequently synthesized qualitatively, whereas in comparative effectiveness research and guideline development, it is imperative that heterogeneous data are integrated quantitatively and in a manner that accurately captures the true uncertainty in the results. Bayesian hierarchical modeling is a technique that utilizes all available information from multiple sources and naturally yields a revised estimate of the treatment effect associated with each source. A hierarchical model consists of multiple levels (ie, a hierarchy) of probability distributions that represent relationships between information arising within single populations or trials, as well as relationships between information arising from different populations or trials. We describe the structure of Bayesian hierarchical models and discuss their advantages over simpler models when multiple information sources are relevant. Key Words: multilevel analysis Ⅲ comparative effectiveness research Ⅲ practice guidelines H ealth care providers must often decide whether to recommend a therapy for a patient for whom directly relevant treatment outcome data are limited or absent. For example, there may be limited experience with the therapy, the disease state may be rare, or the patient may be a member of a population that is underrepresented in available clinical studies. Similarly, those setting health care policy, authoring treatment guidelines, or making coverage determinations must also often make decisions regarding the effectiveness of treatments when directly relevant information is limited. An intuitive approach, frequently used by practicing clinicians, is to consider evidence from related populations and therapies, although the similarity to the patient or population of interest may vary significantly. For example, efficacy data for other drugs in the same class may be available, or the therapy may have been evaluated in populations with different comorbidities, demographic characteristics, or disease severities.Whether at the individual patient or population level, this type of decision-making requires the synthesis of information from heterogeneous sources. Many common statistical methods assume either homogeneity (eg, fixed-effects meta-analysis) or complete independence across populations and are thus ill-suited for the quantitative synthesis of heterogeneous data. Hierarchical, or multilevel, modeling is a statistical method that can be used to quantitatively and coherently combine heterogeneous information. A hierarchical model consists of multiple levels (ie, a hierarchy) of probability distributions that represent relationships between information arising within single populations or trials, as well as relationships between information arising from different popul...

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“…Additional discussions regarding the application of hierarchical modeling to subgroup analysis may be found in Lipsky 13 and Jones. 40 …”

Section: Resultsmentioning

confidence: 99%

Bayesian Hierarchical Modeling and the Integration of Heterogeneous Information on the Effectiveness of Cardiovascular Therapies

Kwok

Lewis²

2011

Circ: Cardiovascular Quality and Outcomes

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“…Indeed, several authors including Jones et al. (2011) suggest using the sampling distribution in (3) as a reasonable approximation.…”

Section: Bayesian Methods For Subgroup Analysismentioning

confidence: 99%

“…4, we introduce several Bayesian models suggested in Jones et al. (2011) that can be used in subgroup analysis and describe several of their prominent features. Throughout this section, the Bayesian methods for subgroup analysis are illustrated through their use in analyzing the SOLVD data, and we compare and contrast the results of both the frequentist and Bayesian approaches.…”

Section: Introductionmentioning

confidence: 99%

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research

Henderson

Louis

Wang

et al. 2016

Health Serv Outcomes Res Method

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Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion software beanz can facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE. Topics highlighted include shrinkage estimation, model choice, sensitivity analysis, and posterior predictive checking. A case study is presented in which we demonstrate the use of the methods discussed.

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“…The EB offers a natural way to combine both extremes (Efron, 1996). Closely related approaches are the full Bayesian approach in the setting of linear models (Dixon and Simon, 1991; Jones et al, 2011) and EB approach assuming a normal prior (Davis and Leffingwell, 1990; Louis, 1984). To our knowledge, this is the first attempt of applying EB approach without the need to assume a parametric family as the prior distribution in the setting of treatment effect estimation in sub-populations.…”

Section: Introductionmentioning

confidence: 99%

Estimation of treatment effect in a subpopulation: An empirical Bayes approach

Shen

Jeong

2015

Journal of Biopharmaceutical Statistics

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It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in sub-populations. In particular, due to limited sample size for sub-populations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often non-informative. We propose an empirical Bayes approach to combine both information embedded in a target sub-population and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented.

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Bayesian models for subgroup analysis in clinical trials

Cited by 91 publications

References 41 publications

Bayesian Hierarchical Modeling and the Integration of Heterogeneous Information on the Effectiveness of Cardiovascular Therapies

Bayesian Hierarchical Modeling and the Integration of Heterogeneous Information on the Effectiveness of Cardiovascular Therapies

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research

Estimation of treatment effect in a subpopulation: An empirical Bayes approach

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