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We investigate the familywise error rate (FWER) for time‐to‐event endpoints evaluated using a group sequential design with a hierarchical testing procedure for secondary endpoints. We show that, in this setup, the correlation between the log‐rank test statistics at interim and at end of study is not congruent with the canonical correlation derived for normal‐distributed endpoints. We show, both theoretically and by simulation, that the correlation also depends on the level of censoring, the hazard rates of the endpoints, and the hazard ratio. To optimize operating characteristics in this complex scenario, we propose a simulation‐based method to assess the FWER which, better than the alpha‐spending approach, can inform the choice of critical values for testing secondary endpoints. [ABSTRACT FROM AUTHOR]

Objective: This paper describes the baseline characteristics of the Semaglutide Effects on Heart Disease and Stroke in Patients with Overweight or Obesity (SELECT) study, one of the largest cardiovascular (CV) outcome studies in the field of obesity, which evaluates the effect of semaglutide versus placebo on major CV events. Methods: SELECT enrolled individuals with overweight or obesity without diabetes, with prior myocardial infarction, stroke, and/or peripheral artery disease. This study reports participants' baseline characteristics in the full study population and subgroups defined by baseline glycated hemoglobin (HbA1c; <5.7%, ≥5.7 to <6.0%, ≥6.0 to <6.5%), baseline waist to height ratio tertile, and qualifying prior CV event or condition. Results: The study enrolled 17,605 participants (72.5% male) with an average (SD) age of 61.6 (8.9) years and BMI of 33.34 (5.04) kg/m2. The most common prior CV event was myocardial infarction (76.3% of participants), followed by stroke (23.3%) and peripheral artery disease (8.6%). Furthermore, 24.3% had a heart failure diagnosis. Two‐thirds of participants (66%) had HbA1c in the prediabetes range (5.7%‐6.4%). Across groups of increasing HbA1c, prevalence of all CV risk factors increased. Conclusions: The enrolled population in SELECT includes participants across a broad range of relevant risk categories. This will allow the study to garner information about the CV benefits of semaglutide across these relevant clinical subgroups. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study was to describe the risks of cardiovascular (CV) events and severe hypoglycaemia with insulin degludec (degludec) vs insulin glargine 100 units/mL (glargine U100) in patients with type 2 diabetes (T2D) aged 65 years or older. Materials and methods: A total of 7637 patients in the DEVOTE trial, a treat‐to‐target, randomized, double‐blind trial evaluating the CV safety of degludec vs glargine U100, were divided into three age groups (50‐64 years, n = 3682; 65‐74 years, n = 3136; ≥75 years, n = 819). Outcomes by overall age group and randomized treatment differences were analysed for major adverse cardiovascular events (MACE), all‐cause mortality, severe hypoglycaemia and serious adverse events (SAEs). Results: Patients with increasing age had higher risks of CV death, all‐cause mortality and SAEs, and there were non‐significant trends towards higher risks of MACE and severe hypoglycaemia. Treatment effects on the risk of MACE, all‐cause mortality, severe hypoglycaemia and SAEs were consistent across age groups, based on the non‐significant interactions between treatment and age with regard to these outcomes. Conclusions: There were higher risks of CV death, all‐cause mortality and SAEs, and trends towards higher risks of MACE and severe hypoglycaemia with increasing age after adjusting for baseline differences. The effects across age groups of degludec vs glargine U100 on MACE, all‐cause mortality and severe hypoglycaemia were comparable, suggesting that the risk of MACE, as well as all‐cause mortality, is similar and the risk of severe hypoglycaemia is lower with degludec regardless of age. Evidence is conclusive only until 74 years of age. [ABSTRACT FROM AUTHOR]

Aim: To compare the associations between concomitant liraglutide use versus no liraglutide use and the risk of major adverse cardiovascular events (MACE) and all‐cause mortality among patients receiving basal insulin (either insulin degludec [degludec] or insulin glargine 100 units/mL [glargine U100]) in the Trial Comparing Cardiovascular Safety of Insulin Degludec versus Insulin Glargine in Patients with Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE). Materials and Methods: Patients with type 2 diabetes and high cardiovascular risk were randomized 1:1 to degludec or glargine U100. Hazard ratios for MACE/mortality were calculated using a Cox regression model adjusted for treatment and time‐varying liraglutide use at any time during the trial, without interaction. Sensitivity analyses were adjusted for baseline covariates including, but not limited to, age, sex, smoking and prior cardiovascular disease. Results: At baseline, 436/7637 (5.7%) patients were treated with liraglutide; after baseline, 187/7637 (2.4%) started and 210/7637 (2.7%) stopped liraglutide. Mean liraglutide exposure from randomization was 530.2 days. Liraglutide use versus no liraglutide use was associated with significantly lower hazard rates for MACE [0.62 (0.41; 0.92)95%CI] and all‐cause mortality [0.50 (0.29; 0.88)95%CI]. There was no significant difference in the rate of severe hypoglycaemia with versus without liraglutide use. Multiple sensitivity analyses yielded similar results. Conclusions: Use of liraglutide was associated with significantly lower risk of MACE and death in patients with type 2 diabetes and high cardiovascular risk using basal insulin. [ABSTRACT FROM AUTHOR]

In phase 3 clinical trials, ethical and financial concerns motivate sequential analyses in which the data are analyzed prior to completion of the entire planned study. Existing group sequential software accounts for the effects of these interim analyses on the sampling density by assuming that the contribution of subsequent increments is independent of the contribution from previous data. This independent increment assumption is satisfied in many common circumstances, including when using the efficient estimator. However, certain circumstances may dictate using an inefficient estimator, and the independent increment assumption may then be violated. Consequences of assuming independent increments in a setting where the assumption does not hold have not been previously explored. One important setting in which independent increments may not hold is the setting of longitudinal clinical trials. This paper considers dependent increments that arise because of heteroscedastic and correlated data in the context of longitudinal clinical trials that use a generalized estimating equation (GEE) approach. Both heteroscedasticity over time and correlation of observations within subjects may lead to departures from the independent increment assumption when using GEE. We characterize situations leading to greater departures in this paper. Despite violations of the independent increment assumption, simulation results suggest that operating characteristics of sequential designs are largely maintained for typically observed patterns of accrual, correlation, and heteroscedasticity even when using analyses that use standard software that depends on an independent increment structure. More extreme scenarios may require greater care to avoid departures from the nominal type I error rate and power. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Adaptive clinical trial design has been proposed as a promising new approach that may improve the drug discovery process. Proponents of adaptive sample size re-estimation promote its ability to avoid 'up-front' commitment of resources, better address the complicated decisions faced by data monitoring committees, and minimize accrual to studies having delayed ascertainment of outcomes. We investigate aspects of adaptation rules, such as timing of the adaptation analysis and magnitude of sample size adjustment, that lead to greater or lesser statistical efficiency. Owing in part to the recent Food and Drug Administration guidance that promotes the use of pre-specified sampling plans, we evaluate alternative approaches in the context of well-defined, pre-specified adaptation. We quantify the relative costs and benefits of fixed sample, group sequential, and pre-specified adaptive designs with respect to standard operating characteristics such as type I error, maximal sample size, power, and expected sample size under a range of alternatives. Our results build on others' prior research by demonstrating in realistic settings that simple and easily implemented pre-specified adaptive designs provide only very small efficiency gains over group sequential designs with the same number of analyses. In addition, we describe optimal rules for modifying the sample size, providing efficient adaptation boundaries on a variety of scales for the interim test statistic for adaptation analyses occurring at several different stages of the trial. We thus provide insight into what are good and bad choices of adaptive sampling plans when the added flexibility of adaptive designs is desired. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present an approach for inference on contrasts of clinically meaningful functionals of a survivor distribution (e.g., restricted mean, quantiles) that can avoid strong parametric or semiparametric assumptions on the underlying structure of the data. In this multistage approach, we first use an adaptive predictive model to estimate conditional survival distributions based on covariates. We then estimate nonparametrically one or more functionals of survival from the covariate-specific survival curves and evaluated contrasts of those functionals. We find that the use of an adaptive nonparametric tree-based predictive model leads to minimal loss in precision when semiparametric assumptions hold and provides marked improvement in accuracy when those assumptions are invalid. Therefore, this work as a whole supports the use of survival summaries appropriate to a given medical application, whether that be, for example, the median or 75th percentile in some settings or perhaps a restricted mean in others. The approach is also compared with the Mayo R score for primary biliary cirrhosis prognosis. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

After a non-inferiority clinical trial, a new therapy may be accepted as effective, even if its treatment effect is slightly smaller than the current standard. It is therefore possible that, after a series of trials where the new therapy is slightly worse than the preceding drugs, an ineffective or harmful therapy might be incorrectly declared efficacious; this is known as 'bio-creep'. Several factors may influence the rate at which bio-creep occurs, including the distribution of the effects of the new agents being tested and how that changes over time, the choice of active comparator, the method used to account for the variability of the estimate of the effect of the active comparator, and changes in the effect of the active comparator from one trial to the next (violations of the constancy assumption). We performed a simulation study to examine which of these factors might lead to bio-creep and found that bio-creep was rare, except when the constancy assumption was violated. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Group sequential stopping rules are often used as guidelines in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Such stopping rules have been proposed based on a variety of different criteria, both scientific (e.g. estimates of treatment effect) and statistical (e.g. frequentist type I error, Bayesian posterior probabilities, stochastic curtailment). It is easily shown, however, that a stopping rule based on one of these criteria induces a stopping rule on all other criteria. Thus, the basis used to initially define a stopping rule is relatively unimportant so long as the operating characteristics of the stopping rule are fully investigated. In this paper we describe how the frequentist operating characteristics of a particular stopping rule might be evaluated to ensure that the selected clinical trial design satisfies the constraints imposed by the many different disciplines represented by the clinical trial collaborators. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Clinical trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confidence intervals ( Statist. Med. 2005, in revision). Increasingly, however, clinical trials are designed and analysed in the Bayesian paradigm. In this paper, we describe how the Bayesian operating characteristics of a particular stopping rule might be evaluated and communicated to the scientific community. In particular, we consider a choice of probability models and a family of prior distributions that allows concise presentation of Bayesian properties for a specified sampling plan. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mitochondria-deficient cells (ρo cells) survive through enhanced glycolytic metabolism in the presence of pyruvate and uridine. The plasma membrane redox system (PMRS) contains several NAD(P)H-related enzymes and plays a key role in maintaining the levels of NAD+/NADH and reduced coenzyme Q. In this study, ρo cells were used to investigate how the PMRS is regulated under conditions of mitochondrial dysfunction. ρo cells exhibited a lower oxygen consumption rate and higher levels of lactate than parental cells, and were more sensitive to glycolysis inhibitors (2-deoxyglucose and iodoacetamide) than control cells. However, they were more resistant to H2O2, consistent with increased catalase activity and decreased oxidative damage (protein carbonyls and nitrotyrosine). PM-associated redox enzyme activities were enhanced in ρo cells compared to those in control cells. Our data suggest that all PMRS enzymes and biomarkers tested are closely related to the ability of the PMs to maintain redox homeostasis. These results illustrate that an up-regulated PM redox activity can protect cells from oxidative stress as a result of an improved antioxidant capacity, and suggest a mechanism by which neurons adapt to conditions of impaired mitochondrial function. [ABSTRACT FROM AUTHOR]

Sequential sampling plans are often used in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Group sequential stopping rules are perhaps the most commonly used approaches, but in recent years, a number of authors have proposed adaptive methods of choosing a stopping rule. In general, such adaptive approaches come at a price of inefficiency (almost always) and clouding of the scientific question (sometimes). In this paper, I review the degree of adaptation possible within the largely prespecified group sequential stopping rules, and discuss the operating characteristics that can be characterized fully prior to collection of the data. I then discuss the greater flexibility possible when using several of the adaptive approaches receiving the greatest attention in the statistical literature and conclude with a discussion of the scientific and statistical issues raised by their use. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Longitudinal endpoints are used in clinical trials, and the analysis of the results is often conducted using within-individual summary statistics. When these trials are monitored, interim analyses that include subjects with incomplete follow-up can give incorrect decisions due to bias by non-linearity in the true time trajectory of the treatment effect. Linear mixed-effects models can be used to remove this bias, but there is a lack of software to support both the design and implementation of monitoring plans in this setting. This paper considers a clinical trial in which the measurement time schedule is fixed (at least for pre-trial design), and the scientific question is parameterized by a contrast across these measurement times. This setting assures generalizable inference in the presence of non-linear time trajectories. The distribution of the treatment effect estimate at the interim analyses using the longitudinal outcome measurements is given, and software to calculate the amount of information at each interim analysis is provided. The interim information specifies the analysis timing thereby allowing standard group sequential design software packages to be used for trials with longitudinal outcomes. The practical issues with implementation of these designs are described; in particular, methods are presented for consistent estimation of treatment effects at the interim analyses when outcomes are not measured according to the pre-trial schedule. Splus/R functions implementing this inference using appropriate linear mixed-effects models are provided. These designs are illustrated using a clinical trial of statin treatment for the symptoms of peripheral arterial disease. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recent research has derived preference scores from the SF-36. We compare the practicality and construct validity of SF-36 derived preference scores with directly elicited time trade off (TTO) and visual analogue scale (VAS) scores. In this observational study, low back pain (LBP), patients were asked to complete disease specific, generic (SF-36), and health state preference (VAS and TTO) instruments. Baseline SF-36 responses were converted to preference scores using six published algorithms. Response rates for the SF-36 derived and TTO preference values were 354 of 379 (93%) and 303 of 379 (80%), respectively. Thirty patients were excluded from the TTO exercise because of difficulties comprehending the scaling task. Choice based methods (standard gamble, TTO) yielded higher and more uniform estimates of preference (0.77-0.79) than non-choice based methods (VAS) (0.42-0.70). Directly elicited TTO values were variable and had less power to distinguish among patients with differing severity of LBP. All SF-36 derived preferences exhibited a minimum threshold implying a potential floor effect for severely ill patients. SF-36 derived preferences demonstrated good practicality and construct validity in this setting, however different methods will yield disparate estimates of marginal benefit. This emphasises the need for a standardised algorithm for deriving SF-36 preference scores. [ABSTRACT FROM AUTHOR]