Search

The point at which clinical development programs transition from early phase to pivotal trials is a critical milestone. Substantial uncertainty about the outcome of pivotal trials may remain even after seeing positive early phase data, and companies may need to make difficult prioritization decisions for their portfolio. The probability of success (PoS) of a program, a single number expressed as a percentage reflecting the multitude of risks that may influence the final program outcome, is a key decision‐making tool. Despite its importance, companies often rely on crude industry benchmarks that may be "adjusted" by experts based on undocumented criteria and which are typically misaligned with the definition of success used to drive commercial forecasts, leading to overly optimistic expected net present value calculations. We developed a new framework to assess the PoS of a program before pivotal trials begin. Our definition of success encompasses the successful outcome of pivotal trials, regulatory approval and meeting the requirements for market access as outlined in the target product profile. The proposed approach is organized in four steps and uses an innovative Bayesian approach to synthesize all relevant evidence. The new PoS framework is systematic and transparent. It will help organizations to make more informed decisions. In this paper, we outline the rationale and elaborate on the structure of the proposed framework, provide examples, and discuss the benefits and challenges associated with its adoption. [ABSTRACT FROM AUTHOR]

There are several steps to confirming the safety and efficacy of a new medicine. A sequence of trials, each with its own objectives, is usually required. Quantitative risk metrics can be useful for informing decisions about whether a medicine should transition from one stage of development to the next. To obtain an estimate of the probability of regulatory approval, pharmaceutical companies may start with industry‐wide success rates and then apply to these subjective adjustments to reflect program‐specific information. However, this approach lacks transparency and fails to make full use of data from previous clinical trials. We describe a quantitative Bayesian approach for calculating the probability of success (PoS) at the end of phase II which incorporates internal clinical data from one or more phase IIb studies, industry‐wide success rates, and expert opinion or external data if needed. Using an example, we illustrate how PoS can be calculated accounting for differences between the phase II data and future phase III trials, and discuss how the methods can be extended to accommodate accelerated drug development pathways. [ABSTRACT FROM AUTHOR]

Available data of event-based clinical outcomes trials show that little evidence supports the guidelines recommendations to lower blood pressure (BP) to <130/80 mmHg in middle-aged and elderly people with type 2 diabetes mellitus and hypertension. We addressed this issue by post-hoc analysing the risk of cardiovascular (CV) events in mostly elderly high-risk hypertensive patients with type 2 diabetes mellitus participating in the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial. Patients (n = 5250) were divided into 4 groups according to the proportion of on-treatment visits before the occurrence of an event (<25% to ≥ 75%) in which BP was reduced to <140/90 or <130/80 mmHg. After adjustment for baseline demographic differences between groups, a reduction in the proportion of visits in which BP achieved <140/90 mmHg accompanied a progressive increase in the risk of CV mortality and morbidity as well as of cause-specific events such as stroke, myocardial infarction and heart failure. A progressive reduction in the proportion of visits in which BP was reduced <130/80 mmHg did not have any effect on CV risks. In mostly elderly high-risk hypertensive patients with type 2 diabetes mellitus participating in the VALUE trial, achieving more frequently BP <140/90 mmHg showed a marked protective effect on overall and all cause-specific cardiovascular outcomes. This was not the case for a more frequent achievement of the more intensive BP target, i.e. <130/80 mmHg. [ABSTRACT FROM AUTHOR]

This article deals with comparing a test with a control therapy using meta-analyses of data from randomized controlled trials with a time-to-event endpoint. Such analyses can often benefit from prior information about the distribution of control group outcomes. One possible source of this information is the published aggregate data about control groups of historical trials from the medical literature. We review methods for making posterior inference about exponentially distributed event times more robust to prior-data conflicts by discounting the prior information based on the extent of observed prior-data conflict. We use simulations to compare analyses without prior information with the meta-analytic combined, meta-analytic predictive and robust meta-analytic predictive approaches, as well as Bayesian model averaging using shrinkage priors. Bayesian model averaging via shrinkage priors with well-chosen hyperpriors performed best in terms of credible interval coverage and mean-squared error across scenarios. For the robust meta-analytic predictive approach, there was little benefit in increasing the weight of the informative mixture components beyond 0.2–0.5. This was the case even when little prior-data conflict was expected, except with very sparse data or substantial between-trial heterogeneity in control group hazard rates. for this article are available online. [ABSTRACT FROM AUTHOR]

Information from historical trials is important for the design, interim monitoring, analysis, and interpretation of clinical trials. Meta‐analytic models can be used to synthesize the evidence from historical data, which are often only available in aggregate form. We consider evidence synthesis methods for trials with recurrent event endpoints, which are common in many therapeutic areas. Such endpoints are typically analyzed by negative binomial regression. However, the individual patient data necessary to fit such a model are usually unavailable for historical trials reported in the medical literature. We describe approaches for back‐calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log‐likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta‐analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back‐calculated parameter estimates results in very similar inference as using parameter estimates from individual patient data as an input. We illustrate how to design and analyze a new randomized placebo‐controlled exacerbation trial in severe eosinophilic asthma using data from 11 historical trials. [ABSTRACT FROM AUTHOR]

Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship, and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared with time-to-event methods. To deal with differences in follow-up-at the cost of assuming specific distributions for event and drop-out times-we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases, and discontinuations in each group, as well as the planned trial duration and groups sizes. Such a model also enables exchangeability assumptions about parameters of survival distributions, for which they are more appropriate than for the expected proportion of patients with an event across trials of substantially different length. Borrowing information from other trials within a meta-analysis or from historical data is particularly useful for rare events data. Prior information or exchangeability assumptions also avoid the parameter identifiability problems that arise when using more flexible event and drop-out time distributions than the exponential one. We discuss the derivation of robust historical priors and illustrate the discussed methods using an example. We also compare the proposed approach against other aggregate data meta-analysis methods in a simulation study. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Previous studies have debated the notion that low blood pressure (BP) during treatment, particularly diastolic (DBP), is associated with increased risk of cardiovascular disease. We evaluated the impact of low BP on cardiovascular outcomes in a high-risk population of 15,244 hypertensive patients, almost half of whom had a history of coronary artery disease (CAD). In the prospective Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial, patients were randomized to valsartan or amlodipine regimens and followed for 4.2 years (mean) with no difference in the primary cardiovascular endpoint. A Cox proportional hazards model was used to evaluate the relationship between average on-treatment BP and clinical outcomes. The relationship between BP and cardiovascular events was adjusted for age, gender and body mass index, and baseline qualifying risk factors and diseases (smoking, high total cholesterol, diabetes mellitus, proteinuria, CAD, previous stroke and left ventricular hypertrophy). DBP ≥ 90 mmHg, compared with < 90 mmHg, was associated with increased incidence of the primary cardiovascular endpoint (all cardiac events); however, DBP < 70 mmHg, compared with ≥ 70 mmHg, was not associated with increased incidence after covariate adjustment (no J-shaped curve). Similar results were observed for death, myocardial infarction (MI), heart failure and stroke, considered separately. Nadir for MI was at DBP of 76 mmHg and for stroke 60 mmHg. The ratio of MI to stroke increased with lower DBP. In CAD patients the MI to stroke ratio was more pronounced than in patients without CAD but there was no significant J-curve in either group. Systolic BP ≥ 150 but not < 130 mmHg, compared with 130–149 mmHg, similarly was associated with increased risk for primary outcome. In conclusion, patients in BP strata ≥ 150/90 mmHg, but not patients in BP strata < 130/70 mmHg, were at increased risk for adverse outcomes in this hypertensive, high-risk population. Although benefit in preventing MI in relation to preventing stroke levels off for the lowest BPs, these data provide no support for a J-curve in the treatment of high-risk hypertensive patients . The increase in the ratio of MI to stroke with lower DBP indicates target organ heterogeneity in that the optimal on-treatment DBP for cerebroprotection is below that for cardioprotection. [ABSTRACT FROM AUTHOR]

Dose-related efficacy and safety of fevipiprant (QAW039), an oral DP 2 (CRTh2) receptor antagonist, was assessed in patients with allergic asthma uncontrolled by low-dose inhaled corticosteroids (ICS).Adult patients were randomised to 12 weeks' treatment with once-daily (1, 3, 10, 30, 50, 75, 150, 300 or 450 mg q.d ) or twice-daily (2, 25, 75 or 150 mg b.i.d ) fevipiprant (n=782), montelukast 10 mg q.d (n=139) or placebo (n=137). All patients received inhaled budesonide 200 μg b.i.d Fevipiprant produced a statistically significant improvement in the primary end-point of change in pre-dose forced expiratory volume in 1 s at week 12 (p=0.0035) with a maximum model-averaged difference to placebo of 0.112 L. The most favourable pairwise comparisons to placebo were for the fevipiprant 150 mg q.d and 75 mg b.i.d groups, with no clinically meaningful differences between q.d and b.i.d Montelukast also demonstrated a significant improvement in this end-point. No impact on other efficacy end-points was observed. Adverse events were generally mild/moderate in severity, and were evenly distributed across doses and treatments.Fevipiprant appears to be efficacious and well-tolerated in this patient population, with an optimum total daily dose of 150 mg. Further investigations into the clinical role of fevipiprant in suitably designed phase III clinical trials are warranted., Competing Interests: Conflict of interest: Disclosures can be found alongside this article at erj.ersjournals.com, (Copyright ©ERS 2017.)