Bayesian Meta‐Experimental Design: Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes

Ibrahim, Joseph G.; Chen, Ming Hui; Xia, H. Amy; Liu, Thomas T.

doi:10.1111/j.1541-0420.2011.01679.x

Cited by 34 publications

(47 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, trials are designed to show noninferiority between treatment arms with respect to CV events while maintaining similar glycemic control (14): investigators are free to modify glucose-lowering medications other than the experimental drug in order to maintain good glycemic control in both treatment groups, and in fact, between-group differences in HbA 1c Figure 1-Timing of CV safety trials with drugs for type 2 diabetes. are small (3)(4)(5).…”

Section: Outcome Versus CV Safety Trialsmentioning

confidence: 99%

Analyses of Results From Cardiovascular Safety Trials With DPP-4 Inhibitors: Cardiovascular Outcomes, Predefined Safety Outcomes, and Pooled Analysis and Meta-analysis

2016

View full text Add to dashboard Cite

The U.S. Food and Drug Administration requires that the cardiovascular (CV) safety of all new drugs for diabetes be demonstrated through pooled analyses of phase III studies or specifically designed trials. This requirement prompted several placebo-controlled, noninferiority CV safety trials in high-risk patients; to date, all completed trials showed that dipeptidyl peptidase (DPP)-4 inhibitors do not increase or reduce the risk of major CV events. These results apparently contrast with those of pooled analyses and meta-analyses of previous, smaller trials with metabolic end points, which had suggested a reduction of risk. However, the design of CV trials, which were required to demonstrate safety, is not adequate (for duration, management of concurrent therapies, etc.) for the assessment of potential therapeutic benefits. In addition, CV safety trials enroll patients at high risk of CV events, who are different from those included in earlier trials with metabolic end points. Differences in characteristics of patients enrolled probably account for most of the discrepancy in CV outcomes between CV safety studies and earlier trials. The availability of several large-scale trials with longer duration provides the unique opportunity for assessment of the safety of DPP-4 inhibitors not only with respect to major CV events but also with reference to other safety issues. For example, CV safety trials can be a source of information for pancreatitis, cancer, or hypoglycemia.In December 2008, the U.S. Food and Drug Administration (FDA) published guidance prompting pooled analyses and meta-analyses of cardiovascular (CV) events (sometimes with post hoc adjudication) observed in trials with metabolic outcomes (1). A shift of emphasis occurred from short-term, HbA 1c -centered phase II-III trials in "healthy" patients with type 2 diabetes to CV safety studies in a more vulnerable population (2). Results from the first trials according to the FDA guidance were

show abstract

Section: Outcome Versus CV Safety Trialsmentioning

confidence: 99%

Analyses of Results From Cardiovascular Safety Trials With DPP-4 Inhibitors: Cardiovascular Outcomes, Predefined Safety Outcomes, and Pooled Analysis and Meta-analysis

2016

View full text Add to dashboard Cite

show abstract

“…. , I and the total follow-up to first event or censoring t ij· -the sufficient statistic for the parameters of an exponential time-to-event distribution under random right-censorship (Ibrahim et al, 2012). Similar issues as those described for logistic regression may occur, if there are no events in some or all arms of a trial.…”

Section: The Exponential Time-to-event Modelmentioning

confidence: 97%

Meta-analysis of aggregate data on medical events

Holzhauer

2016

Statist. Med.

View full text Add to dashboard Cite

A meta-analysis combines analysis results from multiple independent sets of data. Metaanalyses to assess whether a treatment affects the occurrence of a medical event are frequently based on published aggregate data from controlled clinical trials. In that setting, the number of patients with an event in each trial is often treated as a binomial random variable. This assumes that censoring times have the same distribution in all treatment groups of a trial, and are independent of event times.To allow for different drop-out time distributions across treatment groups, we derive a likelihood for commonly available aggregate data that assumes specific event and drop-out time distributions for a number of situations. These include exponentially or Weibull distributed event and drop-out times, event-driven trials, the situation when a patient may experience multiple potentially fatal events, and when individual patient data are available for some trials.The assumption that parameters of survival distributions are exchangeable between trials is more plausible than for the expected proportion of patients with an event.For this reason the proposed likelihood is more suitable than a binomial likelihood for use in hierarchical meta-analysis models and for incorporating prior information from historical control group data. Hierarchical models and prior information are useful in sparse data settings and to avoid parameter identifiability problems. We use simulations to compare hierarchical Bayesian models with the proposed trial-level likelihood against other meta-analysis methods and to compare methods for using historical control group data. We also demonstrate how conjugate priors may be used to analyze exponentially distributed failure times without the need for Markov chain Monte Carlo methods.

show abstract

“…Comparatively, little has been done with survival models for right-censored data. Notable exceptions are Ibrahim et al 12 and Chen et al 13,14 Bayesian designs that control type I error in some sense have been recently considered in Ibrahim et al 12 and Chen et al 13-15 The approach to type I error control considered by those authors is closely related to frequentist type I error control and the associated sampling priors can be viewed as limiting cases of the truncated priors that we develop in this paper. Bayesian analysis of univariate cure rate models has been considered in Chen et al, 9,18,19 Ibrahim et al, 16,17 and Tsodikov et al 20 For the proposed methodology, information from the historical trial is borrowed by way of the power prior.…”

Section: Introductionmentioning

confidence: 99%

Bayesian design of a survival trial with a cured fraction using historical data

Psioda

Ibrahim

2018

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

In this paper, we develop a general Bayesian clinical trial design methodology, tailored for time-to-event trials with a cured fraction in scenarios where a previously completed clinical trial is available to inform the design and analysis of the new trial. Our methodology provides a conceptually appealing and computationally feasible framework that allows one to construct a fixed, maximally informative prior a priori while simultaneously identifying the minimum sample size required for the new trial so that the design has high power and reasonable type I error control from a Bayesian perspective. This strategy is particularly well suited for scenarios where adaptive borrowing approaches are not practical due to the nature of the trial, complexity of the model, or the source of the prior information. Control of a Bayesian type I error rate offers a sensible balance between wanting to use high-quality information in the design and analysis of future trials while still controlling type I errors in an equitable way. Moreover, sample size determination based on our Bayesian view of power can lead to a more adequately sized trial by virtue of taking into account all the uncertainty in the treatment effect. We demonstrate our methodology by designing a cancer clinical trial in high-risk melanoma.

show abstract

Bayesian Meta‐Experimental Design: Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes

Cited by 34 publications

References 16 publications

Analyses of Results From Cardiovascular Safety Trials With DPP-4 Inhibitors: Cardiovascular Outcomes, Predefined Safety Outcomes, and Pooled Analysis and Meta-analysis

Analyses of Results From Cardiovascular Safety Trials With DPP-4 Inhibitors: Cardiovascular Outcomes, Predefined Safety Outcomes, and Pooled Analysis and Meta-analysis

Meta-analysis of aggregate data on medical events

Bayesian design of a survival trial with a cured fraction using historical data

Contact Info

Product

Resources

About