Cyrus R. Mehta

Alogliptin after Acute Coronary Syndrome in Patients with Type 2 Diabetes

BACKGROUND To assess potentially elevated cardiovascular risk related to new antihyperglycemic drugs in patients with type 2 diabetes, regulatory agencies require a comprehensive evaluation of the cardiovascular safety profile of new antidiabetic therapies. We assessed cardiovascular outcomes with alogliptin, a new inhibitor of dipeptidyl peptidase 4 (DPP-4), as compared with placebo in patients with type 2 diabetes who had had a recent acute coronary syndrome. METHODS We randomly assigned patients with type 2 diabetes and either an acute myocardial infarction or unstable angina requiring hospitalization within the previous 15 to 90 days to receive alogliptin or placebo in addition to existing antihyperglycemic and cardiovascular drug therapy. The study design was a double-blind, noninferiority trial with a prespecified noninferiority margin of 1.3 for the hazard ratio for the primary end point of a composite of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. RESULTS A total of 5380 patients underwent randomization and were followed for up to 40 months (median, 18 months). A primary end-point event occurred in 305 patients assigned to alogliptin (11.3%) and in 316 patients assigned to placebo (11.8%) (hazard ratio, 0.96; upper boundary of the one-sided repeated confidence interval, 1.16; P<0.001 for noninferiority). Glycated hemoglobin levels were significantly lower with alogliptin than with placebo (mean difference, -0.36 percentage points; P<0.001). Incidences of hypoglycemia, cancer, pancreatitis, and initiation of dialysis were similar with alogliptin and placebo. CONCLUSIONS Among patients with type 2 diabetes who had had a recent acute coronary syndrome, the rates of major adverse cardiovascular events were not increased with the DPP-4 inhibitor alogliptin as compared with placebo. (Funded by Takeda Development Center Americas; EXAMINE ClinicalTrials.gov number, NCT00968708.).

A Network Algorithm for Performing Fisher's Exact Test in r × c Contingency Tables

Abstract An exact test of significance of the hypothesis that the row and column effects are independent in an r × c contingency table can be executed in principle by generalizing Fishers exact treatment of the 2 × 2 contingency table. Each table in a conditional reference set of r × c tables with fixed marginal sums is assigned a generalized hypergeometric probability. The significance level is then computed by summing the probabilities of all tables that are no larger (on the probability scale) than the observed table. However, the computational effort required to generate all r × c contingency tables with fixed marginal sums severely limits the use of Fishers exact test. A novel technique that considerably extends the bounds of computational feasibility of the exact test is proposed here. The problem is transformed into one of identifying all paths through a directed acyclic network that equal or exceed a fixed length. Some interesting new optimization theorems are developed in the process. The numer...

Heart failure and mortality outcomes in patients with type 2 diabetes taking alogliptin versus placebo in EXAMINE: A multicentre, randomised, double-blind trial

BACKGROUND The EXAMINE trial showed non-inferiority of the DPP-4 inhibitor alogliptin to placebo on major adverse cardiac event (MACE) rates in patients with type 2 diabetes and recent acute coronary syndromes. Concerns about excessive rates of in-hospital heart failure in another DPP-4 inhibitor trial have been reported. We therefore assessed hospital admission for heart failure in the EXAMINE trial. METHODS Patients with type 2 diabetes and an acute coronary syndrome event in the previous 15-90 days were randomly assigned alogliptin or placebo plus standard treatment for diabetes and cardiovascular disease prevention. The prespecified exploratory extended MACE endpoint was all-cause mortality, non-fatal myocardial infarction, non-fatal stroke, urgent revascularisation due to unstable angina, and hospital admission for heart failure. The post-hoc analyses were of cardiovascular death and hospital admission for heart failure, assessed by history of heart failure and brain natriuretic peptide (BNP) concentration at baseline. We also assessed changes in N-terminal pro-BNP (NT-pro-BNP) from baseline to 6 months. This study is registered with ClinicalTrials.gov, number NCT00968708. FINDINGS 5380 patients were assigned to alogliptin (n=2701) or placebo (n=2679) and followed up for a median of 533 days (IQR 280-751). The exploratory extended MACE endpoint was seen in 433 (16·0%) patients assigned to alogliptin and in 441 (16·5%) assigned to placebo (hazard ratio [HR] 0·98, 95% CI 0·86-1·12). Hospital admission for heart failure was the first event in 85 (3·1%) patients taking alogliptin compared with 79 (2·9%) taking placebo (HR 1·07, 95% CI 0·79-1·46). Alogliptin had no effect on composite events of cardiovascular death and hospital admission for heart failure in the post hoc analysis (HR 1·00, 95% CI 0·82-1·21) and results did not differ by baseline BNP concentration. NT-pro-BNP concentrations decreased significantly and similarly in the two groups. INTERPRETATION In patients with type 2 diabetes and recent acute coronary syndromes, alogliptin did not increase the risk of heart failure outcomes. FUNDING Takeda Development Center Americas.

Computing an Exact Confidence Interval for the Common Odds Ratio in Several 2×2 Contingency Tables

Abstract A quadratic time network algorithm is provided for computing an exact confidence interval for the common odds ratio in several 2×2 independent contingency tables. The algorithm is shown to be a considerable improvement on an existing algorithm developed by Thomas (1975), which relies on exhaustive enumeration. Problems that would formerly have consumed several CPU hours can now be solved in a few CPU seconds. The algorithm can easily handle sparse data sets where asymptotic results are suspect. The network approach, on which the algorithm is based, is also a powerful tool for exact statistical inference in other settings.

Exact significance testing to establish treatment equivalence with ordered categorical data.

This communication concerns the problem of establishing the therapeutic equivalence of two treatments that are being compared on the basis of ordered categorical data. The problem is formulated as a significance test in which the null hypothesis specifies a treatment difference. An efficient numerical algorithm for computing the exact significance level is provided, along with a simple method for obtaining the asymptotic significance level. Both methods are applied to a clinical trial of a new agent versus an active control. Guidelines for when to use the exact procedure and when to rely on asymptotic theory are provided.

Computing Distributions for Exact Logistic Regression

Abstract Logistic regression is a commonly used technique for the analysis of retrospective and prospective epidemiological and clinical studies with binary response variables. Usually this analysis is performed using large sample approximations. When the sample size is small or the data structure sparse, the accuracy of the asymptotic approximations is in question. On other occasions, singularity of the covariance matrix of parameter estimates precludes asymptotic analysis. Under these circumstances, use of exact inferential procedures would seem to be a prudent alternative. Cox (1970) showed that exact inference on the parameters of a logistic model with binary response requires consideration of the distribution of sufficient statistics for these parameters. To date, however, resorting to the exact method has not been computationally feasible except in a few special situations. This article presents an efficient recursive algorithm that generates the joint and conditional distributions of the sufficient...

A Randomized, Controlled Trial of Oral Propranolol in Infantile Hemangioma

BACKGROUND Oral propranolol has been used to treat complicated infantile hemangiomas, although data from randomized, controlled trials to inform its use are limited. METHODS We performed a multicenter, randomized, double-blind, adaptive, phase 2-3 trial assessing the efficacy and safety of a pediatric-specific oral propranolol solution in infants 1 to 5 months of age with proliferating infantile hemangioma requiring systemic therapy. Infants were randomly assigned to receive placebo or one of four propranolol regimens (1 or 3 mg of propranolol base per kilogram of body weight per day for 3 or 6 months). A preplanned interim analysis was conducted to identify the regimen to study for the final efficacy analysis. The primary end point was success (complete or nearly complete resolution of the target hemangioma) or failure of trial treatment at week 24, as assessed by independent, centralized, blinded evaluations of standardized photographs. RESULTS Of 460 infants who underwent randomization, 456 received treatment. On the basis of an interim analysis of the first 188 patients who completed 24 weeks of trial treatment, the regimen of 3 mg of propranolol per kilogram per day for 6 months was selected for the final efficacy analysis. The frequency of successful treatment was higher with this regimen than with placebo (60% vs. 4%, P<0.001). A total of 88% of patients who received the selected propranolol regimen showed improvement by week 5, versus 5% of patients who received placebo. A total of 10% of patients in whom treatment with propranolol was successful required systemic retreatment during follow-up. Known adverse events associated with propranolol (hypoglycemia, hypotension, bradycardia, and bronchospasm) occurred infrequently, with no significant difference in frequency between the placebo group and the groups receiving propranolol. CONCLUSIONS This trial showed that propranolol was effective at a dose of 3 mg per kilogram per day for 6 months in the treatment of infantile hemangioma. (Funded by Pierre Fabre Dermatologie; ClinicalTrials.gov number, NCT01056341.).

Exact confidence intervals following a group sequential test.

A numerical method is used to compute confidence intervals, which have exact coverage probabilities, for the mean of a normal distribution following a group sequential test. This method, which uses an ordering of the sample space similar to that employed by Siegmund (1978, Biometrika 65, 341-349), is contrasted with the usual confidence interval for the mean.

ALGORITHM 643: FEXACT: a FORTRAN subroutine for Fisher's exact test on unordered r×c contingency tables

The computer code for Mehta and Patels (1983) network algorithm for Fishers exact test on unordered r×c contingency tables is provided. The code is written in double precision FORTRAN 77. This code provides the fastest currently available method for executing Fishers exact test, and is shown to be orders of magnitude superior to any other available algorithm. Many important details of data structures and implementation that have contributed crucially to the success of the network algorithm are recorded here.

The future of drug development: advancing clinical trial design

Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools — such as Bayesian methodologies — in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.