Use of the Beta-Binomial Model for Central Statistical Monitoring of Multicenter Clinical Trials

Desmet, Lieven; Venet, David; Doffagne, Erik; Timmermans, Catherine; Legrand, Catherine; Burzykowski, Tomasz; Buyse, Marc

doi:10.1080/19466315.2016.1164751

Cited by 16 publications

(22 citation statements)

References 16 publications

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“…In addition, not only continuous values but also discrete values including adverse events counts and time to event including time to withdrawal may be analyzed in actual CSM. For analyzing these various types of data, the statistical model of the proposed method is needed to be changed to according to the data type such Poisson regression model for count data, Weibull distribution model for time to event data and beta-binomial model for proportion which used in Desmet et al [ 16 ]. To adopt these models for our proposed method based on FMM, it is also necessary to use MCMC for estimation.…”

Section: Discussionmentioning

confidence: 99%

“…The conventional CSM procedures focus on relatively large clinical trials. The detection techniques to find atypical sites using the simple statistical model based on a single distribution is useful, because it is reasonable to implicitly assume the relatively small ratio of atypical sites for normal sites [ [14] , [15] , [16] ]. In actual, however, the smaller sized clinical trials have been conducted as well.…”

Section: Csm and Statistical Problem On Detecting Atypical Sitesmentioning

confidence: 99%

“…Desmet et al [ 15 ] has proposed an analysis method for continuous data (normal variates) using linear mixed effects models to detect atypical sites. Desmet et al [ 16 ] proposed the method analyzing the incidence of events using Beta-Binomial Models to detect atypical sites. For detection using multiple outcomes, currently the atypical sites are detected as multivariate outliers by the chi-square test based on the Mahalanobis distance [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials

Hatayama¹,

Yasui

2020

Contemporary Clinical Trials Communications

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Background Central monitoring (CM), in which data across all clinical sites are monitored, has an important role in risk-based monitoring. Several statistical methods have been proposed to compare patient outcomes among the sites for detecting atypical sites that have different trends in observed data. These methods assume that the number of clinical sites is not small, e.g., 100 or more. In addition, the proportion of atypical sites is assumed to be relatively small. However, in actuality, the central statistical monitoring (CSM) has to be implemented in small or moderate sized clinical trials such as small phase II clinical trials. The number of sites is no longer large in such situations. Therefore, it is of concern that existing methods may not work efficiently in CM of small or moderate sized clinical trials. In the light of this problem, we propose a Bayesian CSM method to detect atypical sites as the robust method against the existence of atypical sites. Methods We use Bayesian finite mixture models (FMM) to model patient outcome values of both atypical and typical sites. In the method, the distributions of outcome values in normal sites are determined by choosing the body distribution, which has the largest mixture parameter value of finite mixture models based on the assumption that normal sites are in the majority. Atypical sites are detected by the criterion based on the posterior predictive distribution of normal site's outcome values derived from only the chosen body distribution. Results Proposed method is evaluated by cumulative detection probability and type I error averaged over sites every round of CSM under the various scenarios, being compared with the conventional type analysis. If the total number of patients enrolled is 48, the proposed method is superior at least 10% for any shift sizes at the 2nd and the 3rd rounds. If the total number of patients is 96, both methods show similar detection probability for only one atypical site and large shift size. However, the proposed method is superior for the other scenarios. It is observed that all the type I errors averaged over sites are little difference between the methods at all the scenarios. Conclusion We propose a Bayesian CSM method which works efficiently in a practical use of CM. It is shown that our method detects atypical sites with high probability regardless of the proportion of the atypical sites under the small clinical trial settings which is the target of our proposed method.

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Section: Discussionmentioning

confidence: 99%

Section: Csm and Statistical Problem On Detecting Atypical Sitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials

Hatayama¹,

Yasui

2020

Contemporary Clinical Trials Communications

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“…Although it is a simple idea to perform a central data quality assessment based on the consistency of data across all centers, the statistical models required to implement the idea are necessarily complex to properly account for the natural variability in the data [32,33]. Essentially, a central data quality assessment is efficient if:…”

Section: Principles Of Central Statistical Monitoringmentioning

confidence: 99%

Central statistical monitoring of investigator-led clinical trials in oncology

Buyse

Trotta²,

Saad

et al. 2020

Int J Clin Oncol

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Investigator-led clinical trials are pragmatic trials that aim to investigate the benefits and harms of treatments in routine clinical practice. These much-needed trials represent the majority of all trials currently conducted. They are however threatened by the rising costs of clinical research, which are in part due to extensive trial monitoring processes that focus on unimportant details. Risk-based quality management focuses, instead, on "things that really matter". We discuss the role of central statistical monitoring as part of risk-based quality management. We describe the principles of central statistical monitoring, provide examples of its use, and argue that it could help drive down the cost of randomized clinical trials, especially investigator-led trials, whilst improving their quality.

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“…While equation (1) completes the multicenter structure, it is equation (2) that is crucial in defining the correlation structure across centers, and with respect to which we can assess outlyingness. In equation (2) we allow random deviations from the overall µ ρ to capture, e.g., unobserved differences in center-specific populations 5,6 .…”

Section: Data Models and Detection Problemmentioning

confidence: 99%

Methods for detection of clusters of observations with an outlying correlation coefficient value

Desmet

Venet

Trotta³

et al. 2020

Preprint

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Multivariate datasets with a clustered structure are the natural framework for, e.g., multicentre clinical trials. We propose a number of methods aimed at detecting clusters with outlying correlation coefficients. While the methods can be used in a variety of settings, we focus mainly on their application to central statistical monitoring of clinical trials. In particular, we consider the issue of detecting centers (or other clusters of patients such as regions) with outlying correlation coefficients for bivariate data in a multicenter clinical trial. It appears that, in that context, the proposed methods perform well, as we show by using a simulation study and a number of real life datasets.

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Use of the Beta-Binomial Model for Central Statistical Monitoring of Multicenter Clinical Trials

Cited by 16 publications

References 16 publications

Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials

Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials

Central statistical monitoring of investigator-led clinical trials in oncology

Methods for detection of clusters of observations with an outlying correlation coefficient value

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