Advantages of a wholly Bayesian approach to assessing efficacy in early drug development: a case study

Walley, Rosalind; Smith, Claire; Gale, Jeremy D.; Woodward, Phil

doi:10.1002/pst.1675

Cited by 49 publications

(50 citation statements)

References 27 publications

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“…An interim analysis for futility was conducted when approximately 25% of subjects had completed the 12 week randomized treatment period. More details of the statistical design and analysis including the outlier robust approach can be found in Walley et al 33 In addition, secondary endpoints including urine protein/creatinine ratio, eGFR, and systolic and diastolic BP, were analyzed using a similar analysis of covariance as for the primary endpoint but with uninformative priors for all parameters.…”

Section: Statistical Analysesmentioning

confidence: 99%

Phosphodiesterase Type 5 Inhibition Reduces Albuminuria in Subjects with Overt Diabetic Nephropathy

Scheele

Diamond

Gale

et al. 2016

JASN

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Diabetic nephropathy (DN) is the leading cause of ESRD worldwide. Reduced bioavailability or uncoupling of nitric oxide in the kidney, leading to decreased intracellular levels of the nitric oxide pathway effector molecule cyclic guanosine monophosphate (cGMP), has been implicated in the progression of DN. Preclinical studies suggest that elevating the cGMP intracellular pool through inhibition of the cGMP-hydrolyzing enzyme phosphodiesterase type 5 (PDE5) might exert renoprotective effects in DN. To test this hypothesis, the novel, highly specific, and long-acting PDE5 inhibitor, PF-00489791, was assessed in a multinational, multicenter, randomized, double-blind, placebo-controlled, parallel group trial of subjects with type 2 diabetes mellitus and overt nephropathy receiving angiotensin converting enzyme inhibitor or angiotensin receptor blocker background therapy. In total, 256 subjects with an eGFR between 25 and 60 ml/min per 1.73 m 2 and macroalbuminuria defined by a urinary albumin-to-creatinine ratio .300 mg/g, were randomly assigned 3:1, respectively, to receive PF-00489791 (20 mg) or placebo orally, once daily for 12 weeks. Using the predefined primary assessment of efficacy (Bayesian analysis with informative prior), we observed a significant reduction in urinary albumin-to-creatinine ratio of 15.7% (ratio 0.843; 95% credible interval 0.73 to 0.98) in response to the 12-week treatment with PF-00489791 compared with placebo. PF-00489791 was safe and generally well tolerated in this patient population. Most common adverse events were mild in severity and included headache and upper gastrointestinal events. In conclusion, the safety and efficacy profile of PDE5 inhibitor PF-00489791 supports further investigation as a novel therapy to improve renal outcomes in DN. The global prevalence of diabetes mellitus (DM) is increasing, with more than 400 million people projected to be affected by 2030.

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Section: Statistical Analysesmentioning

confidence: 99%

Phosphodiesterase Type 5 Inhibition Reduces Albuminuria in Subjects with Overt Diabetic Nephropathy

Scheele

Diamond

Gale

et al. 2016

JASN

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“…The null hypothesis (that the test treatment has efficacy equal to that of the control arm) may be rejected in favor of the alternative hypothesis (that the test treatment has better efficacy than that of the control arm) once a prespecified significance level is met. Bayesian approaches to the design and analysis of phase 2 trials are increasingly being applied and can offer a balance between delivering results with speed and gathering sufficiently robust data. Such methods can leverage preexisting historical data for the control arm (considered as a prior belief or distribution), to which emerging trial data is incorporated, providing a posterior distribution on treatment effects .…”

Section: Introductionmentioning

confidence: 99%

Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Smith

Thomas

Enas

et al. 2020

Pharmaceutical Statistics

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Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error.Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies.Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient-level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors. K E Y W O R D SBayesian, borrowing, covariates, historical data

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“…If the drug is not successful, this also ensures that as few patients as possible are exposed to it. For typical proof-of-concept studies, where the key comparison is between the investigational new drug, often at the maximum tolerated dose, and placebo, a Bayesian analysis with an informative prior for the placebo arm may be implemented in an effort to reduce sample size [2][3][4][5]. In addition, there is also increasingly the desire to benchmark any investigational new drug against one or more standard marketed drugs as early as possible in order to de-risk the new drug.…”

Section: Introductionmentioning

confidence: 99%

Addressing potential prior‐data conflict when using informative priors in proof‐of‐concept studies

Mutsvari

Tytgat

Walley

2015

Pharmaceutical Statistics

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Bayesian methods are increasingly used in proof-of-concept studies. An important benefit of these methods is the potential to use informative priors, thereby reducing sample size. This is particularly relevant for treatment arms where there is a substantial amount of historical information such as placebo and active comparators. One issue with using an informative prior is the possibility of a mismatch between the informative prior and the observed data, referred to as prior-data conflict. We focus on two methods for dealing with this: a testing approach and a mixture prior approach. The testing approach assesses prior-data conflict by comparing the observed data to the prior predictive distribution and resorting to a non-informative prior if prior-data conflict is declared. The mixture prior approach uses a prior with a precise and diffuse component. We assess these approaches for the normal case via simulation and show they have some attractive features as compared with the standard one-component informative prior. For example, when the discrepancy between the prior and the data is sufficiently marked, and intuitively, one feels less certain about the results, both the testing and mixture approaches typically yield wider posterior-credible intervals than when there is no discrepancy. In contrast, when there is no discrepancy, the results of these approaches are typically similar to the standard approach. Whilst for any specific study, the operating characteristics of any selected approach should be assessed and agreed at the design stage; we believe these two approaches are each worthy of consideration.

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Advantages of a wholly Bayesian approach to assessing efficacy in early drug development: a case study

Cited by 49 publications

References 27 publications

Phosphodiesterase Type 5 Inhibition Reduces Albuminuria in Subjects with Overt Diabetic Nephropathy

Phosphodiesterase Type 5 Inhibition Reduces Albuminuria in Subjects with Overt Diabetic Nephropathy

Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Addressing potential prior‐data conflict when using informative priors in proof‐of‐concept studies

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