Enabling robust assessment of QTc prolongation in early phase clinical trials

Since the implementation of the International Conference on Harmonization (ICH) E14 guideline in 2005, regulators have required a “thorough QTc” (TQT) study for evaluating the effects of investigational drugs on delayed cardiac repolarization as manifested by a prolonged QTc interval. However, TQT studies have increasingly been viewed unfavorably because of their low cost effectiveness. Several researchers have noted that a robust drug concentration‐QTc (conc‐QTc) modeling assessment in early phase development should, in most cases, obviate the need for a subsequent TQT study. In December 2015, ICH released an “E14 Q&As (R3)” document supporting the use of conc‐QTc modeling for regulatory decisions. In this article, we propose a simple improvement of two popular conc‐QTc assessment methods for typical first‐in‐human crossover‐like single ascending dose clinical pharmacology trials. The improvement is achieved, in part, by leveraging routinely encountered (and expected) intrasubject correlation patterns encountered in such trials. A real example involving a single ascending dose and corresponding TQT trial, along with results from a simulation study, illustrate the strong performance of the proposed method. The improved conc‐QTc assessment will further enable highly reliable go/no‐go decisions in early phase clinical development and deliver results that support subsequent TQT study waivers by regulators.     

A Bayesian sequential design with binary outcome

Several researchers have proposed solutions to control type I error rate in sequential designs. The use of Bayesian sequential design becomes more common; however, these designs are subject to inflation of the type I error rate. We propose a Bayesian sequential design for binary outcome using an alpha‐spending function to control the overall type I error rate. Algorithms are presented for calculating critical values and power for the proposed designs. We also propose a new stopping rule for futility. Sensitivity analysis is implemented for assessing the effects of varying the parameters of the prior distribution and maximum total sample size on critical values. Alpha‐spending functions are compared using power and actual sample size through simulations. Further simulations show that, when total sample size is fixed, the proposed design has greater power than the traditional Bayesian sequential design, which sets equal stopping bounds at all interim analyses. We also find that the proposed design with the new stopping for futility rule results in greater power and can stop earlier with a smaller actual sample size, compared with the traditional stopping rule for futility when all other conditions are held constant. Finally, we apply the proposed method to a real data set and compare the results with traditional designs.     

Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

A powerful and efficient algorithm for breaking the links between aliased effects in asymmetric designs

Fractional factorial (FF) designs are no doubt the most widely used designs in experimental investigations due to their efficient use of experimental runs. One price we pay for using FF designs is, clearly, our inability to obtain estimates of some important effects (main effects or second order interactions) that are separate from estimates of other effects (usually higher order interactions). When the estimate of an effect also includes the influence of one or more other effects the effects are said to be aliased. Folding over an FF design is a method for breaking the links between aliased effects in a design. The question is, how do we define the foldover structure for asymmetric FF designs, whether regular or nonregular? How do we choose the optimal foldover plan? How do we use optimal foldover plans to construct combined designs which have better capability of estimating lower order effects? The main objective of the present paper is to provide answers to these questions. Using the new results in this paper as benchmarks, we can implement a powerful and efficient algorithm for finding optimal foldover plans which can be used to break links between aliased effects.     

A simulation study of methods for selecting subgroup‐specific doses in phase 1 trials

Patient heterogeneity may complicate dose‐finding in phase 1 clinical trials if the dose‐toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively,it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem,we consider a generalization of the continual reassessment method on the basis of a hierarchical Bayesian dose‐toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup‐specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to 3 alternative approaches,on the basis of nonhierarchical models,that make different types of assumptions about within‐subgroup dose‐toxicity curves. The simulations show that the hierarchical model‐based method is recommended in settings where the dose‐toxicity curves are exchangeable between subgroups. We present practical guidelines for application and provide computer programs for trial simulation and conduct.     

Control‐based imputation for sensitivity analyses in informative censoring for recurrent event data

In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control‐based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.     

The challenge of pension reform in Georgia: Non‐contributory pensions and elderly poverty

Georgia's national social security system offers almost universal non‐contributory basic pension coverage. The basic pension has, to date, proved effective in dealing with old‐age poverty. But Georgia's fiscal constraints and ageing population also highlight the importance of improving the pension system, in order to ensure its sustainability. This article presents policy reform choices, which suggest that, in Georgia, pension reform might include increasing the statutory retirement ages and reducing the generosity of benefits through means testing. The case of the Georgian non‐contributory basic pension might hold value for some low‐ and middle‐income countries that are considering the implementation of, or expanding coverage under, a non‐contributory pension programme.     

Can Quasi‐market and Multi‐level Governance Co‐exist? Insights from the Case of Lombardy's Employment Services System

This article explores the functioning of Lombardy's networked employment services system, inspired by quasi‐market and horizontal subsidiarity principles, and specifically addresses a gap in the quasi‐market literature, where little attention is devoted to the role played by institutions at lower levels of government. A qualitative study of the Lombardy system, with a focus on the municipality of Milan, is relied upon in order to explore the extent to which the principles of quasi‐market and multi‐level governance pursued by the regional government are allowed to co‐exist in practice. Here, sub‐regional levels of government are directly involved in services provision, but enjoy a more privileged condition relative to the private providers, thereby jeopardizing the implementation of an effective quasi‐market. The article contributes to existing theories by suggesting that horizontal subsidiarity and marketization cannot neglect multi‐level governance in those sectors where public bodies at various levels of government are directly involved in implementation.     

NDIS Self‐Management Approaches: Opportunities for choice and control or an Uber‐style wild west?

This article examines the emerging challenges and opportunities presented by self‐management options in Australia's National Disability Insurance Scheme (NDIS). We examine the three different ways in which NDIS participants can opt to self‐manage their funding and services, including direct employment and emerging Uber‐style online platforms, and explore the potential implications of these options for NDIS participants, service providers and the disability support workforce. In particular, we focus on these options in relation to the transition to a marketised services landscape being developed alongside the NDIS, and examine both the risks and opportunities for each stakeholder group. Through this analysis, we identify implications for policy and practice, in particular around regulatory mechanisms and the role of government within this emerging market economy and transforming service landscape.     

Do peer effects mediate the association between family socio‐economic status and educational achievement?

Differences in levels of academic achievement according to socio‐economic status (SES), and parental education in particular, have been a persistent feature of Australian education systems. Young people with highly educated parents are more likely than their peers with low‐educated parents to attain high levels of achievement at school. Students with low levels of achievement are less likely than their high achieving peers to complete Year 12 and are more likely to experience negative post‐school outcomes. The SES of the neighbourhood, and in particular, the school attended, has also been found to have an effect on levels of both academic achievement and attainment. For this paper, we conduct analyses of National Assessment Program – Literacy and Numeracy test scores for four cohorts of secondary school students attending government schools in the Australian Capital Territory to examine the associations between parental education, school attended and levels of educational achievement. Our findings show that students with university‐educated parents achieve at much higher levels than their peers with low‐educated parents and that attending a school with a higher proportion of students from educationally disadvantaged families has a negative effect on educational achievement.