Integrated Data Analysis for Assessing Treatment Effect through Combining Information from All Sources

Quan, Hui; Zhang, Bingzhi; Chuang‐Stein, Christy; Jones, Byron

doi:10.1080/19466315.2016.1197150

Cited by 5 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods have been shown to increase statistical power, reduce sample size requirements, and minimize time and costs. Among the areas of application for these methods are pediatric medicine, 1,2 rare diseases, [3][4][5] and oncology. 6 One main application of information borrowing is the design and analysis of basket clinical trials.…”

Section: Introductionmentioning

confidence: 99%

“…Schoenfeld et al 1 used a nearly‐identical approach in borrowing strength from adult to pediatric trials. Usage of empirical Bayesian method and network meta‐analysis in the context of borrowing information from historical control data was covered in Quan et al, 5 Pocock, 13 and more recently Lewis et al, 14 discuss the conditions that external studies must meet to be considered for inclusion in models that allow borrowing of information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Alone, together: On the benefits of Bayesian borrowing in a meta‐analytic setting

Harari¹,

Soltanifar²,

Verhoek³

et al. 2023

Pharmaceutical Statistics

View full text Add to dashboard Cite

It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any historical studies, naturally leads to empirical Bayes meta‐analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alone, together: On the benefits of Bayesian borrowing in a meta‐analytic setting

Harari¹,

Soltanifar²,

Verhoek³

et al. 2023

Pharmaceutical Statistics

View full text Add to dashboard Cite

show abstract

“…In addition to the analysis issues accompanying the use of auxiliary data, regulators worry that some researchers might "cherry-pick" the historical data, including only information favorable to the company's interests. Quan et al (2017) offer a recent practically-focused review the places the borrowing of strength from historical data in the broader context of integrated data analysis, with particular emphasis on the use of network meta-analysis and empirical Bayes to estimate the effects of various treatments.…”

Section: Introductionmentioning

confidence: 99%

Borrowing From Historical Control Data in Cancer Drug Development: A Cautionary Tale and Practical Guidelines

Lewis¹,

Sarkar

Zhu

et al. 2019

Statistics in Biopharmaceutical Research

View full text Add to dashboard Cite

Some clinical trialists, especially those working in rare or pediatric disease, have suggested borrowing information from similar but already-completed clinical trials. This paper begins with a case study in which relying solely on historical control information would have erroneously resulted in concluding a significant treatment effect. We then attempt to catalog situations where borrowing historical information may or may not be advisable using a series of carefully designed simulation studies. We use an MCMC-driven Bayesian hierarchical parametric survival modeling approach to analyze data from a sponsor's colorectal cancer study. We also apply these same models to simulated data comparing the effective historical sample size, bias, 95% credible interval widths, and empirical coverage probabilities across the simulated cases. We find that even after accounting for variations in study design, baseline characteristics, and standard-of-care improvement, our approach consistently identifies Bayesianly significant differences between the historical and concurrent controls under a range of priors on the degree of historical data borrowing. Our simulation studies are far from exhaustive, but inform the design of future trials. When the historical and current controls are not dissimilar, Bayesian methods can still moderate borrowing to a more appropriate level by adjusting for important covariates and adopting sensible priors.

show abstract

A simple method for correcting for the Will Rogers phenomenon with biometrical applications

Stander

2020

Biometrical J

View full text Add to dashboard Cite

In its basic form, the Will Rogers phenomenon takes place when an increase in the average value of each of two sets is achieved by moving an element from one set to another. This leads to the conclusion that there has been an improvement, when in fact essentially nothing has changed. Extended versions of this phenomenon can occur in epidemiological studies, rendering their results unreliable. After describing epidemiological and clinical studies that have been affected by the Will Rogers phenomenon, this paper presents a simple method to correct for it. The method involves introducing a transition matrix between the two sets and taking probability weighted expectations. Two real‐world biometrical examples, based on migration economics and breast cancer epidemiology, are given and improvements against a naïve analysis are demonstrated. In the cancer epidemiology example, we take account of estimation uncertainty. We also discuss briefly some limitations associated with our method.

show abstract

Integrated Data Analysis for Assessing Treatment Effect through Combining Information from All Sources

Cited by 5 publications

References 29 publications

Alone, together: On the benefits of Bayesian borrowing in a meta‐analytic setting

Alone, together: On the benefits of Bayesian borrowing in a meta‐analytic setting

Borrowing From Historical Control Data in Cancer Drug Development: A Cautionary Tale and Practical Guidelines

A simple method for correcting for the Will Rogers phenomenon with biometrical applications

Contact Info

Product

Resources

About