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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: To determine whether semaglutide slows progression of glycemia in people with cardiovascular disease and overweight or obesity but without diabetes. RESEARCH DESIGN AND METHODS: In a multicenter, double-blind trial, participants aged ≥45 years, with BMI ≥27 kg/m2, and with preexisting cardiovascular disease but without diabetes (HbA1c <6.5%) were randomized to receive subcutaneous semaglutide (2.4 mg weekly) or placebo. Major glycemic outcomes were HbA1c and proportions achieving biochemical normoglycemia (HbA1c <5.7%) and progressing to biochemical diabetes (HbA1c ≥6.5%). RESULTS: Of 17,604 participants, 8,803 were assigned to semaglutide and 8,801 to placebo. Mean ± SD intervention exposure was 152 ± 56 weeks and follow-up 176 ± 40 weeks. In both treatment arms mean nadir HbA1c for participants was at 20 weeks. Thereafter, HbA1c increased similarly in both arms, with a mean difference of −0.32 percentage points (95% CI −0.33 to −0.30; −3.49 mmol/mol [−3.66 to −3.32]) and with the difference favoring semaglutide throughout the study (P < 0.0001). Body weight plateaued at 65 weeks and was 8.9% lower with semaglutide. At week 156, a greater proportion treated with semaglutide were normoglycemic (69.5% vs. 35.8%; P < 0.0001) and a smaller proportion had biochemical diabetes by week 156 (1.5% vs. 6.9%; P < 0.0001). The number needed to treat was 18.5 to prevent a case of diabetes. Both regression and progression were dependent on glycemia at baseline, with the magnitude of weight reduction important in mediating 24.5% of progression and 27.1% of regression. CONCLUSIONS: In people with preexisting cardiovascular disease and overweight or obesity but without diabetes, long-term semaglutide increases regression to biochemical normoglycemia and reduces progression to biochemical diabetes but does not slow glycemic progression over time. 6357437865112 dc240491video1 Video 1. This image is from a video available online at https://bcove.video/3WedKuq. American Diabetes Association 84th Scientific Sessions: SELECT Trial—New Looks at Glycemia, Inflammation, and Heart Failure. [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]

Background/Aims: The HIV Prevention Trials Network 083 trial was a group-sequential non-inferiority trial designed to compare HIV incidence under a novel experimental regimen for HIV prevention, long-acting injectable cabotegravir, with an active-control regimen of daily oral tenofovir disoproxil fumarate/emtricitabine (brand name Truvada). In March of 2020, just as the trial had completed enrollment, the COVID-19 pandemic threatened to prevent trial participants from attending study visits and obtaining study medication, motivating the study team to update the interim monitoring plan. The Data and Safety Monitoring Board subsequently stopped the trial at the first interim review due to strong early evidence of efficacy. Methods: Here we describe some unique aspects of the trial's design, monitoring, analysis, and interpretation. We illustrate the importance of computing point estimates, confidence intervals, and p values based on the sampling distribution induced by sequential monitoring. Results: Accurate analysis, decision-making and interpretation of trial results rely on pre-specification of a stopping boundary, including the scale on which the stopping rule will be implemented, the specific test statistics to be calculated, and how the boundary will be adjusted if the available information fraction at interim review is different from planned. After appropriate adjustment for the sampling distribution and overrun, the HIV Prevention Trials Network 083 trial provided strong evidence that the experimental regimen was superior to the active control. Conclusions: For the HIV Prevention Trials Network 083 trial, the difference between corrected inferential statistics and naive results was quite small—as will often be the case—nevertheless, it is appropriate to report and publish the most accurate and unbiased statistical results. [ABSTRACT FROM AUTHOR]

Background/aims: After a new treatment is recommended to be first-line treatment for a specific indication, outcome and population, it may be unethical to use placebo as a comparator in trials for that setting. Nevertheless, in specific circumstances, use of a placebo group might be warranted, for example, when it is believed that an active treatment may not be efficacious or cost-effective for a specific subpopulation. An example is antibiotic treatment for pneumonia, which may not be effective for many patients taking it due to the emergence of antibiotic-resistant strains or the high prevalence of viral and low prevalence of bacterial pneumonia. Methods: We explore the applicability of different design options in cases where the benefit of an established treatment is questioned, with particular emphasis on issues that arise in a low-resource setting. Using the example of a clinical trial comparing the effectiveness of placebo versus amoxicillin in treating children 2–59 months of age with fast breathing pneumonia in Lilongwe, Malawi, we discuss the pros and cons of superiority versus non-inferiority designs, an intent-to-treat versus as-treated analysis and the use and interpretation of one- versus two-sided confidence intervals. Results: We find that a non-inferiority design using an intent-to-treat analysis is the most appropriate design and analysis option. In addition, the presentation of one- versus two-sided confidence intervals can depend on the results but can maintain type I error. Conclusion: In the setting where the benefit of a previously established beneficial treatment is questioned, a non-inferiority design that includes placebo as the tested treatment option can be the most appropriate design option. [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]

Aims/hypothesis: The Trial Comparing Cardiovascular Safety of Insulin Degludec vs Insulin Glargine in Patients with Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE) was a double-blind, randomised, event-driven, treat-to-target prospective trial comparing the cardiovascular safety of insulin degludec with that of insulin glargine U100 (100 units/ml) in patients with type 2 diabetes at high risk of cardiovascular events. This paper reports a secondary analysis investigating associations of day-to-day fasting glycaemic variability (pre-breakfast self-measured blood glucose [SMBG]) with severe hypoglycaemia and cardiovascular outcomes. Methods: In DEVOTE, patients with type 2 diabetes were randomised to receive insulin degludec or insulin glargine U100 once daily. The primary outcome was the first occurrence of an adjudicated major adverse cardiovascular event (MACE). Adjudicated severe hypoglycaemia was the pre-specified secondary outcome. In this article, day-to-day fasting glycaemic variability was based on the standard deviation of the pre-breakfast SMBG measurements. The variability measure was calculated as follows. Each month, only the three pre-breakfast SMBG measurements recorded before contact with the site were used to determine a day-to-day fasting glycaemic variability measure for each patient. For each patient, the variance of the three log-transformed pre-breakfast SMBG measurements each month was determined. The standard deviation was determined as the square root of the mean of these monthly variances and was defined as day-to-day fasting glycaemic variability. The associations between day-to-day fasting glycaemic variability and severe hypoglycaemia, MACE and all-cause mortality were analysed for the pooled trial population with Cox proportional hazards models. Several sensitivity analyses were conducted, including adjustments for baseline characteristics and most recent HbA. Results: Day-to-day fasting glycaemic variability was significantly associated with severe hypoglycaemia (HR 4.11, 95% CI 3.15, 5.35), MACE (HR 1.36, 95% CI 1.12, 1.65) and all-cause mortality (HR 1.58, 95% CI 1.23, 2.03) before adjustments. The increased risks of severe hypoglycaemia, MACE and all-cause mortality translate into 2.7-, 1.2- and 1.4-fold risk, respectively, when a patient's day-to-day fasting glycaemic variability measure is doubled. The significant relationships of day-to-day fasting glycaemic variability with severe hypoglycaemia and all-cause mortality were maintained after adjustments. However, the significant association with MACE was not maintained following adjustment for baseline characteristics with either baseline HbA (HR 1.19, 95% CI 0.96, 1.47) or the most recent HbA measurement throughout the trial (HR 1.21, 95% CI 0.98, 1.49). Conclusions/interpretation: Higher day-to-day fasting glycaemic variability is associated with increased risks of severe hypoglycaemia and all-cause mortality. Trial registration: NCT01959529 [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The double-blind Trial Comparing Cardiovascular Safety of Insulin Degludec vs Insulin Glargine in Patients with Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE) assessed the cardiovascular safety of insulin degludec. The incidence and rates of adjudicated severe hypoglycaemia, and all-cause mortality were also determined. This paper reports a secondary analysis investigating associations of severe hypoglycaemia with cardiovascular outcomes and mortality. Methods: In DEVOTE, patients with type 2 diabetes were randomised to receive either insulin degludec or insulin glargine U100 (100 units/ml) once daily (between dinner and bedtime) in an event-driven, double-blind, treat-to-target cardiovascular outcomes trial. The primary outcome was the first occurrence of an adjudicated major adverse cardiovascular event (MACE; cardiovascular death, non-fatal myocardial infarction or non-fatal stroke). Adjudicated severe hypoglycaemia was the pre-specified secondary outcome. In the present analysis, the associations of severe hypoglycaemia with both MACE and all-cause mortality was evaluated in the pooled trial population using time-to-event analyses, with severe hypoglycaemia as a time-dependent variable and randomised treatment as a fixed factor. An investigation with interaction terms indicated that the effect of severe hypoglycaemia on the risk of MACE and all-cause mortality were the same for both treatment arms, and so the temporal association for severe hypoglycaemia with subsequent MACE and all-cause mortality is reported for the pooled population. Results: There was a non-significant difference in the risk of MACE for individuals who had vs those who had not experienced severe hypoglycaemia during the trial (HR 1.38, 95% CI 0.96, 1.96; p = 0.080) and therefore there was no temporal relationship between severe hypoglycaemia and MACE. There was a significantly higher risk of all-cause mortality for patients who had vs those who had not experienced severe hypoglycaemia during the trial (HR 2.51, 95% CI 1.79, 3.50; p < 0.001). There was a higher risk of all-cause mortality 15, 30, 60, 90, 180 and 365 days after experiencing severe hypoglycaemia compared with not experiencing severe hypoglycaemia in the same time interval. The association between severe hypoglycaemia and all-cause mortality was maintained after adjustment for the following baseline characteristics: age, sex, HbA, BMI, diabetes duration, insulin regimen, hepatic impairment, renal status and cardiovascular risk group. Conclusions/interpretation: The results from these analyses demonstrate an association between severe hypoglycaemia and all-cause mortality. Furthermore, they indicate that patients who experienced severe hypoglycaemia were particularly at greater risk of death in the short term after the hypoglycaemic episode. These findings indicate that severe hypoglycaemia is associated with higher subsequent mortality; however, they cannot answer the question as to whether severe hypoglycaemia serves as a risk marker for adverse outcomes or whether there is a direct causal effect. Trial registration: NCT01959529 [ABSTRACT FROM AUTHOR]

Statistical intuition suggests that increasing the total number of observations available for analysis should increase the precision with which parameters can be estimated. Such monotonic growth of statistical information is of particular importance when data are analyzed sequentially, such as in confirmatory clinical trials. However, monotonic information growth is not always guaranteed, even when using a valid, but inefficient estimator. In this article, we demonstrate the theoretical possibility of nonmonotonic information growth when using generalized estimating equations (GEE) to estimate a slope and provide intuition for why this possibility exists. We use theoretical and simulation-based results to characterize situations that may result in nonmonotonic information growth. Nonmonotonic information growth is most likely to occur when (1) accrual is fast relative to follow-up on each individual, (2) correlation among measurements from the same individual is high, and (3) measurements are becoming more variable further from randomization. In situations that may lead to nonmonotonic information growth, study designers should plan interim analyses to avoid situations most likely to result in nonmonotonic information growth. [ABSTRACT FROM PUBLISHER]

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]

Many papers have introduced adaptive clinical trial methods that allow modifications to the sample size based on interim estimates of treatment effect. There has been extensive commentary on type I error control and efficiency considerations, but little research on estimation after an adaptive hypothesis test. We evaluate the reliability and precision of different inferential procedures in the presence of an adaptive design with pre-specified rules for modifying the sampling plan. We extend group sequential orderings of the outcome space based on the stage at stopping, likelihood ratio statistic, and sample mean to the adaptive setting in order to compute median-unbiased point estimates, exact confidence intervals, and P-values uniformly distributed under the null hypothesis. The likelihood ratio ordering is found to average shorter confidence intervals and produce higher probabilities of P-values below important thresholds than alternative approaches. The bias adjusted mean demonstrates the lowest mean squared error among candidate point estimates. A conditional error-based approach in the literature has the benefit of being the only method that accommodates unplanned adaptations. We compare the performance of this and other methods in order to quantify the cost of failing to plan ahead in settings where adaptations could realistically be pre-specified at the design stage. We find the cost to be meaningful for all designs and treatment effects considered, and to be substantial for designs frequently proposed in the literature. [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]

Comparison of the accuracy of two diagnostic tests using the receiver operating characteristic (ROC) curves from two diagnostic tests has been typically conducted using fixed sample designs. On the other hand, the human experimentation inherent in a comparison of diagnostic modalities argues for periodic monitoring of the accruing data to address many issues related to the ethics and efficiency of the medical study. To date, very little research has been done on the use of sequential sampling plans for comparative ROC studies, even when these studies may use expensive and unsafe diagnostic procedures. In this article we propose a nonparametric group sequential design plan. The nonparametric sequential method adapts a nonparametric family of weighted area under the ROC curve statistics ( Wieand et al., 1989 , Biometrika 76, 585–592) and a group sequential sampling plan. We illustrate the implementation of this nonparametric approach for sequentially comparing ROC curves in the context of diagnostic screening for nonsmall-cell lung cancer. We also describe a semiparametric sequential method based on proportional hazard models. We compare the statistical properties of the nonparametric approach with alternative semiparametric and parametric analyses in simulation studies. The results show the nonparametric approach is robust to model misspecification and has excellent finite-sample performance. [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]

Background. Human immunodeficiency virus (HIV) coinfection and low peripheral blood CD4+ T cell counts are associated with increased hepatitis C liver disease. Methods. Hepatitis C virus (HCV)–specific CD4+ T cell responses were assessed using interferon (IFN)-g enzyme-linked immunospot assays on peripheral blood mononuclear cells and expanded liver lymphocytes from HCV-monoinfected and HCV/HIV-coinfected subjects. Cell frequencies were determined using flow cytometry. Results. HIV coinfection was associated with decreased CD4+T cell percentages in both peripheral blood (21% vs. 48%; P<.0001) and liver (15% vs. 36%;<.0001) and with reduced responsiveness of peripheral CD4+ T cells to HCV antigens compared with HCV monoinfection (22% vs. 45%; P = .021 ). However, intrahepatic HCV-specific responses were maintained in HCV/HIV coinfection, compared with HCV monoinfection (38% vs. 32%; P = 7). Notably, the presence of HCV-specific responses was not related to the frequency of liver CD4+ T cells (P = 4 ). Circulating and liver CD4+ T cell percentages were correlated ( r = 0.58; P <.0001 ). Circulating percentages were also inversely associated with liver fibrosis stage among HCV/HIV-coinfected subjects (P = .029 ). Neither hepatic CD4+ T cell percentages nor HCV-specific IFN-γ responses in the liver or periphery predicted stage. Conclusions. Despite decreases in peripheral blood HCV-specific CD4+ T cell responses and intrahepatic CD4+T cell percentages, intrahepatic HCV-specific CD4+ IFN-γ responses were preserved in HCV/HIV coinfection. [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]

The plasma membrane (PM) contains redox enzymes that provide electrons for energy metabolism and recycling of antioxidants such as coenzyme Q and α-tocopherol. Brain aging and neurodegenerative disorders involve impaired energy metabolism and oxidative damage, but the involvement of the PM redox system (PMRS) in these processes is unknown. Caloric restriction (CR), a manipulation that protects the brain against aging and disease, increased activities of PMRS enzymes (NADH-ascorbate free radical reductase, NADH-quinone oxidoreductase 1, NADH-ferrocyanide reductase, NADH-coenzyme Q10 reductase, and NADH-cytochrome c reductase) and antioxidant levels (α-tocopherol and coenzyme Q10) in brain PM during aging. Age-related increases in PM lipid peroxidation, protein carbonyls, and nitrotyrosine were attenuated by CR, levels of PMRS enzyme activities were higher, and markers of oxidative stress were lower in cultured neuronal cells treated with CR serum compared with those treated with ad libitum serum. These findings suggest important roles for the PMRS in protecting brain cells against age-related increases in oxidative and metabolic stress. [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]

Background. Hepatitis C virus (HCV) quasispecies variability has been associated with liver disease progression. The effects of human immunodeficiency virus (HIV) coinfection and highly active antiretroviral therapy (HAART) on HCV quasispecies variability have not been firmly established. Methods. We determined HCV quasispecies complexity and diversity in 69 subjects, 28 of whom were HIV infected, using clonal frequency analysis via heteroduplex mobility analysis of the second envelope gene hypervariable region. Nucleotide sequencing was performed for a small subset of subjects. Results. HIV-positive, HAART-naive subjects had significantly lower HCV quasispecies complexity and diversity than did both HIV-negative and HIV-positive HAART-treated subjects. In multivariate analysis, HIV infection predicted decreased complexity (P<.0001) and diversity (P = .001) of HCV quasispecies, whereas HAART predicted increased complexity (P = 0.013) and diversity (P = 0.026). For 4 of 6 patients, sequence analysis yielded data supporting the model that positive host pressure drives HCV quasispecies heterogeneity, although data favoring the hypothesis of selective outgrowth of the most fit variants were also observed. Conclusion. HIV coinfection is associated with decreased HCV quasispecies variability, which appears to be reversed by effective HAART. Although HIV- and HAART-related effects on host immune pressure are likely to play a role in the observed differences in HCV genetic heterogeneity, other mechanisms may be operative. [ABSTRACT FROM AUTHOR]

Background: Soy isoflavones have numerous biological properties that suggest that they may protect against colorectal cancer. Colorectal epithelial cell proliferation has been used extensively as an intermediate endpoint biomarker for colorectal neoplasia. Objective: We tested the hypothesis that supplementation with soy protein containing isoflavones decreases colorectal epithelial cell proliferation. Design: A 12-mo randomized intervention was conducted in men and women aged 50-80 y with recently diagnosed adenomatous polyps. One hundred fifty participants were enrolled and randomly assigned to an active treatment group (58 g protein powder/d containing 83 mg isoflavones/d; +ISO) or a control group (ethanol-extracted soy-protein powder containing 3 mg isoflavones; -ISO). Biopsy specimens from the cecum, sigmoid colon, and rectum were collected at baseline and at the 12-mo follow-up. Ki-67 antibody immunohistostaining was used to detect cell proliferation. One hundred twenty-five participants completed the study, and proliferation was measured in the first 91 who completed the study. Results: In the sigmoid colon, cell proliferation increased by 0.9 (95% CI: 0.09, 1.9) labeled nuclei per crypt more (11%) in the +ISO group than in the -ISO group over the 12-mo intervention, which was opposite the direction predicted. The number of labeled nuclei per 100 μm crypt height also increased more in the +ISO than in the -ISO group. In the cecum and sigmoid colon, but not in the rectum, the proliferation count increased as the serum genistein concentration increased. Proliferation distribution and crypt height were not changed by treatment at any site. Conclusions: Supplementation with soy protein containing isoflavones does not reduce colorectal epithelial cell proliferation or the average height of proliferating cells in the cecum, sigmoid colon, and rectum and increases cell proliferation measures in the sigmoid colon. [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]

Group sequential designs are often used for periodically assessing treatment efficacy during the course of a clinical trial. Following a group sequential test, P-values computed under the assumption that the data were gathered according to a fixed sample design are no longer uniformly distributed under the null hypothesis of no treatment effect. Various sample space orderings have been proposed for computing proper P-values following a group sequential test. Although many of the proposed orderings have been compared in the setting of time-invariant treatment effects, little attention has been given to their performance when the effect of treatment within an individual varies over time. Our interest here is to compare two of the most commonly used methods for computing proper P-values following a group sequential test, based upon the analysis time (AT) and Z-statistic orderings, with respect to resulting power functions when treatment effects on survival are delayed. Power under the AT ordering is shown to be heavily influenced by the presence of a delayed treatment effect, while power functions corresponding to the Z-statistic ordering remain robust under time-varying treatment effects. [ABSTRACT FROM AUTHOR]

The use of group sequential methods has become standard practice in the clinical-trial setting. Because patients are not all generally entered into a clinical trial at the same time and due to delayed outcomes, interim analyses are typically planned with respect to statistical-information time rather than calendar time. In many cases it is sufficient for interim testing to be based upon the number of responses observed, e.g., the number of patients for whom a post-randomization blood pressure had been measured or the number of patients surviving past a given time period. In the case of censored survival data, the logrank statistic is a commonly used tool for comparing the survival experience of two or more groups. Assuming equal randomization and the null-hypothesis of no difference in survival, it is well know that statistical information grows in proportion to the number of events observed, making analysis planning on the information scale trivial. However,when weighted versions of the logrank statistic are used, the number of censored observations at each analysis time is affected by both the enrollment rate and the timing of interim analyses. We describe how statistical information grows when testing of censored survival data is based upon selected members of the Gρ, γ family of weighted logrank statistics under various enrollment scenarios that may be encountered in the clinical-trial setting. In addition, we consider the impact on estimation of treatment effects when performing an interim analysis at less than maximal information in the setting of nonproportional hazards survival data. A reweighting of the Gρ, γ statistic is proposed that reapportions unused weight at interim analyses to a subset of the last observed events,thereby providing estimates of treatment effect obtained at interim analyses that are more comparable to those that would have been obtained had estimation been performed under full support. [ABSTRACT FROM AUTHOR]

Since poor sleep quality is associated with multiple health problems, it is important to understand factors that may affect sleep patterns. The purpose of this study was to determine the effect of a continuous, 60-Hz, nighttime magnetic field exposure on sleep outcomes in young women sleeping at home. The study was a randomized crossover trial, comparing intervention (0.5–1.0 µT above ambient levels) with ambient magnetic field levels, during two 5-night measurement periods. Subjects lived in the Seattle, Washington, area and were 20–40 years of age, had regular menstrual cycles, were not taking oral contraceptives, and had not breastfed or been pregnant during the previous year. The study was conducted between March and September of 2001. Sleep outcomes were measured via actigraphy. The range of magnetic field exposure was 0.001–0.50 µT during the ambient period and 0.41–1.21 µT during the intervention period. Sleep outcomes were not significantly different between the intervention and the ambient measurement periods. The intervention magnetic field had no effect on sleep patterns, suggesting that this exposure may not be an important factor in predicting sleep of young women who sleep at home. [ABSTRACT FROM AUTHOR]

Group sequential stopping rules are often used during the conduct of clinical trials in order to attain more ethical treatment of patients and to better address efficiency concerns. Because the use of such stopping rules materially affects the frequentist operating characteristics of the hypothesis test, it is necessary to choose an appropriate stopping rule during the planning of the study. It is often the case, however, that the number and timing of interim analyses are not precisely known at the time of trial design, and thus the implementation of a particular stopping rule must allow for flexible determination of the schedule of interim analyses. In this article, we consider the use of constrained stopping boundaries in the implementation of stopping rules. We compare this approach when used on various scales for the test statistic. When implemented on the scale of boundary crossing probabilities, this approach is identical to the error spending function approach of Lan and DeMets (1983). [ABSTRACT FROM AUTHOR]

Context: Faster magnetic resonance imaging (MRI) scanning has made MRI a potential cost-effective replacement for radiographs for patients with low back pain. However, whether rapid MRI scanning results in better patient outcomes than radiographic evaluation or a cost-effective alternative is unknown. Objective: To determine the clinical and economic consequences of replacing spine radiographs with rapid MRI for primary care patients. Design, Setting, and Patients: Randomized controlled trial of 380 patients aged 18 years or older whose primary physicians had ordered that their low back pain be evaluated by radiographs. The patients were recruited between November 1998 and June 2000 from 1 of 4 imaging centers in the Seattle, Wash, area: a university-based teaching program, a nonuniversity-based teaching program, and 2 private clinics. Intervention: Patients were randomly assigned to receive lumbar spine evaluation by rapid MRI or by radiograph. Main Outcome Measures: Back-related disability measured by the modified Roland questionnaire. Secondary outcomes included Medical Outcomes Study 36-Item Short Form Health Survey (SF-36), pain, preference scores, satisfaction, and costs. Results: At 12 months, primary outcomes of functional disability were obtained from 337 (89%) of the 380 patients enrolled. The mean back-related disability modified Roland score for the 170 patients assigned to the radiograph evaluation group was 8.75 vs 9.34 for the 167 patients assigned the rapid MRI evaluation group (mean difference, -0.59; 95% CI, -1.69 to 0.87). The mean differences in the secondary outcomes were not statistically significant : pain bothersomeness (0.07; 95% CI -0.88 to 1.22), pain frequency (0.12; 95% CI, -0.69 to 1.37), and SF-36 subscales of bodily pain (1.25; 95% CI, -4.46 to 4.96), and physical functioning (2.73, 95% CI -4.09 to 6.22). Ten patients in the rapid MRI group vs 4 in the radiograph group had lumbar spine operations (risk difference, 0.34; 95% CI,... [ABSTRACT FROM AUTHOR]

G/d soy protein powder supplement containing 83 mg isoflavones; ISO, 58 g/d Isoflavones; ISO, 58 g/d soy protein powder supplement containing 3 ISO, 58 g/d soy protein powder supplement containing 83 mg isoflavones; ISO, 58 g/d soy protein powder supplement containing 3 mg Isoflavones; IGF-I, insulin-like growth factor-1; IGFBP-3, insulin-like growth factor binding protein-3. [Extracted from the article]

Context: Renal failure places people at particularly high risk of hip fracture. However, the possible differential impact of dialysis and renal transplantation on this risk is not well understood. Objective: To determine if patients who receive kidney transplants are at greater risk of hip fracture compared with those who continue to undergo dialysis. Design, Setting, and Participants: Cohort study of 101 039 patients with end-stage renal disease placed on the renal transplant waiting list in the United States between January 1, 1990, and December 31, 1999. Main Outcome Measures: Hip fractures, identified from Medicare claims data. Results: Among the patients included in this analysis, 971 hip fractures were observed during the follow-up period of 314 767 person-years. The incidence rate of hip fracture in patients receiving dialysis was 2.9 per 1000 patients per year compared with 3.3 hip fractures per 1000 patients per year in those who had previously received a renal transplant. Initially, the relative risk (RR) of hip fracture associated with transplantation was 1.34-fold greater when compared with dialysis (adjusted RR, 1.34; 95% confidence interval [CI], 1.12-1.61) but then decreased by 1% per month (adjusted RR, 0.99; 95% CI, 0.98-0.99) until the estimated risk became equal for dialysis and transplant recipients approximately 630 days after transplantation (adjusted RR, 1.00; 95% CI, 0.87-1.15). Among transplant recipients, risk of fracture was relatively higher in persons with a prolonged period of dialysis before transplantation. Conclusion: The high risk of hip fracture among dialysis patients is exceeded by that among renal transplant patients during the first 1 to 3 years after transplantation. [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]

Presents two computer programs intended for statistical application: EaST, Version 2 and PEST#, Version 3. Developers of the computer software; Application of the software to sequential methods; Possible contribution to clinical analysis; System requirements and capabilities.

Many clinical studies of colorectal adenomatous polyps rely on endoscopic estimation of polyp size. To examine the reliability of such measurements, we conducted a study using artificial polyps in an endoscopy teaching model. Eight experienced endoscopists estimated the size of 13 polyps in two separate sessions 2 wk apart. Endoscopic estimates of polyp size tended to be significantly lower than the true polyp size for all polyps and all endoscopists at both sessions. We also found a statistically significant difference in the magnitude of the underestimation between the first and second session (p < 0.0001). At the first session, polyps tended to be estimated at 64% of their true size, and at the second session, the estimates tended to be at 77% of the actual polyp size. We estimate the magnitude of the variation in polyp measurements due to individual polyps, endoscopist, and examination session, and discuss the impact these sources of variation have in planning of clinical trials. [ABSTRACT FROM AUTHOR]

Discusses the use of alpha-difluoromethylornithine (DFMO) in reducing colorectal mucosal polyamine contents in patients with colon polyps. DFMO as an inhibitor of carcinogenesis in experimental animal models; Activity in preventing carcinogen-induced epithelial cancers; Doses that would deplete target tissue levels of polyamines in humans; More.