The Calculation of a Confidence Interval on the Absolute Estimated Benefit for an Individual Patient

Background and Purpose-Stroke risk factors have been determined in large part through epidemiological studies in white cohorts; as a result, race-ethnic disparities in stroke incidence and mortality rates remained unexplained. The aim in the present study was to compare the prevalence, OR, and etiological fraction (EF) of stroke risk factors among white, blacks, and Caribbean Hispanics living in the same urban community of northern Manhattan. Methods-In this population-based incident case-control study, cases (nϭ688) of first ischemic stroke were prospectively matched 1:2 by age, sex, and race-ethnicity with community controls (nϭ1156). Risk factors were determined through in-person assessment. Conditional logistic regression was used to calculate adjusted ORs in each race-ethnic group. Prevalence and multivariate EFs were determined in each race-ethnic group.

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“…We used the bootstrap method to calculate CIs and demonstrate significance for each of the multivariate EFs. 26 …”

Section: Discussionmentioning

confidence: 99%

Race-Ethnic Disparities in the Impact of Stroke Risk Factors

Sacco

Boden‐Albala

Abel

et al. 2001

Stroke

343

259

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“…Evaluations of the use of warfarin for atrial fibrillation have recently been conducted by others, using distinctly different methodology [37,38]. Li et al [37] describe a method to estimate the absolute estimated benefit for individual patients.…”

Section: Discussionmentioning

confidence: 99%

“…Li et al [37] describe a method to estimate the absolute estimated benefit for individual patients. This approach uses a database on 35,000 individuals, to which a treatment-stratified Cox model is fitted, including the principal risk factors and treatment interaction terms.…”

Section: Discussionmentioning

confidence: 99%

“…Perhaps there is scope for an amalgamation of this and the NCB methodology, allowing a more detailed classification of risk factors for individuals (rather than our classification of individuals into one of six groups), and allowing the risk of fatal bleeds to vary also between individuals. Further, the implementation of such an amalgamated method in a Bayesian framework would allow the construction of prediction intervals for individuals; this might be simpler to implement than the Monte Carlo and bootstrap methodologies that Li et al [37] propose.…”

Section: Discussionmentioning

confidence: 99%

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A Bayesian approach to evaluating net clinical benefit allowed for parameter uncertainty

Sutton

Cooper

Abrams

et al. 2005

Journal of Clinical Epidemiology

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“…Boissel et al , defines a treatment benefit as the difference in the rate of the disease outcome between untreated and treated subjects. Using this definition and a Cox regression model of time to cardiovascular events with fixed regression coefficients, Li et al , computes confidence intervals for the individual benefit of some drugs based on the subject's prognostic factors. The prediction, though, does not take into account the potential variability of regression coefficients in the subject population.…”

Section: Introductionmentioning

confidence: 99%

Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed‐effects models

Díaz

2016

Statistics in Medicine

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We propose statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine. The method focuses on assessing and comparing individual effects of treatments rather than average effects, and can be used with continuous and discrete responses, including dichotomous and count responses. The method is based on new developments in generalized linear mixed-effects models, which are introduced in this article. To illustrate, analyses of data from the STAR*D clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are presented. The estimation of individual benefits is also explained.

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The Calculation of a Confidence Interval on the Absolute Estimated Benefit for an Individual Patient

Cited by 5 publications

References 37 publications

Race-Ethnic Disparities in the Impact of Stroke Risk Factors

Race-Ethnic Disparities in the Impact of Stroke Risk Factors

A Bayesian approach to evaluating net clinical benefit allowed for parameter uncertainty

Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed‐effects models

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