A simple meta-analytic approach for using a binary surrogate endpoint to predict the effect of intervention on true endpoint

A surrogate endpoint is an endpoint that is obtained sooner, at lower cost, or less invasively than the true endpoint for a health outcome and is used to make conclusions about the effect of intervention on the true endpoint. In this approach, each previous trial with surrogate and true endpoints contributes an estimated predicted effect of intervention on true endpoint in the trial of interest based on the surrogate endpoint in the trial of interest. These predicted quantities are combined in a simple random-effects meta-analysis to estimate the predicted effect of intervention on true endpoint in the trial of interest. Validation involves comparing the average prediction error of the aforementioned approach with (i) the average prediction error of a standard meta-analysis using only true endpoints in the other trials and (ii) the average clinically meaningful difference in true endpoints implicit in the trials. Validation is illustrated using data from multiple randomized trials of patients with advanced colorectal cancer in which the surrogate endpoint was tumor response and the true endpoint was median survival time.     

Cost-Effectiveness of a Home Based Intervention for Secondary Prevention of Readmission with Chronic Heart Disease

The aim of this study is to consider the cost-effectiveness of a nurse-led, home-based intervention (HBI) in cardiac patients with private health insurance compared to usual post-discharge care. A within trial analysis of the Young @ Heart multicentre, randomized controlled trial along with a micro-simulation decision analytical model was conducted to estimate the incremental costs and quality adjusted life years associated with the home based intervention compared to usual care. For the micro-simulation model, future costs, from the perspective of the funder, and effects are estimated over a twenty-year time horizon. An Incremental Cost-Effectiveness Ratio, along with Incremental Net Monetary Benefit, is evaluated using a willingness to pay threshold of $50,000 per quality adjusted life year. Sub-group analyses are conducted for men and women across three age groups separately. Costs and benefits that arise in the future are discounted at five percent per annum. Overall, home based intervention for secondary prevention in patients with chronic heart disease identified in the Australian private health care sector is not cost-effective. The estimated within trial incremental net monetary benefit is -$3,116 [95%CI: -11,145, $4,914]; indicating that the costs outweigh the benefits. However, for males and in particular males aged 75 years and above, home based intervention indicated a potential to reduce health care costs when compared to usual care (within trial: -$10,416 [95%CI: -$26,745, $5,913]; modelled analysis: -$1,980 [95%CI: -$22,843, $14,863]). This work provides a crucial impetus for future research to understand for whom disease management programs are likely to benefit most.     

A Statistical Perspective on Prevention Trials: A View from the Women’s Health Initiative

Chronic disease prevention trials test strategies to reduce the risk of a specific health event in generally healthy people. These strategies are often thought to affect other health conditions and their use in the population requires a very favorable safety profile. A prevention trial assessing such a strategy is most valuable when designed to capture the overall public health impact and hence provide more comprehensive, reliable information for policy and practice. This broad agenda, and particularly the assessment of multiple outcomes, creates statistical challenges in the design, monitoring, and reporting of such a trial. In this article these issues are described in the context of the Women’s Health Initiative, a large randomized prevention trial testing three interventions in post-menopausal women: hormone therapy, a low-fat diet and calcium and vitamin D supplementation. Each intervention was hypothesized to influence multiple chronic disease rates including cardiovascular disease, stroke, cancers, and fractures. Here the design, monitoring, and reporting of the WHI trials is reviewed in the context of multiple outcomes and the approach to a global assessment of these interventions is described.     

Imputation strategies for missing continuous outcomes in cluster randomized trials

In cluster randomized trials, intact social units such as schools, worksites or medical practices - rather than individuals themselves - are randomly allocated to intervention and control conditions, while the outcomes of interest are then observed on individuals within each cluster. Such trials are becoming increasingly common in the fields of health promotion and health services research. Attrition is a common occurrence in randomized trials, and a standard approach for dealing with the resulting missing values is imputation. We consider imputation strategies for missing continuous outcomes, focusing on trials with a completely randomized design in which fixed cohorts from each cluster are enrolled prior to random assignment. We compare five different imputation strategies with respect to Type I and Type II error rates of the adjusted two-sample t -test for the intervention effect. Cluster mean imputation is compared with multiple imputation, using either within-cluster data or data pooled across clusters in each intervention group. In the case of pooling across clusters, we distinguish between standard multiple imputation procedures which do not account for intracluster correlation and a specialized procedure which does account for intracluster correlation but is not yet available in standard statistical software packages. A simulation study is used to evaluate the influence of cluster size, number of clusters, degree of intracluster correlation, and variability among cluster follow-up rates. We show that cluster mean imputation yields valid inferences and given its simplicity, may be an attractive option in some large community intervention trials which are subject to individual-level attrition only; however, it may yield less powerful inferences than alternative procedures which pool across clusters especially when the cluster sizes are small and cluster follow-up rates are highly variable. When pooling across clusters, the imputation procedure should generally take intracluster correlation into account to obtain valid inferences; however, as long as the intracluster correlation coefficient is small, we show that standard multiple imputation procedures may yield acceptable type I error rates; moreover, these procedures may yield more powerful inferences than a specialized procedure, especially when the number of available clusters is small. Within-cluster multiple imputation is shown to be the least powerful among the procedures considered.     

A Bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints

In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol–based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (eg, objective response and landmark progression-free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and “graduate” effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method.     

Enhanced sensitivity to punctate painful stimuli in female patients with chronic low back pain

Background

Chronic fatigue and inactivity are prevalent problems among individuals with multiple sclerosis (MS) and may independently or interactively have detrimental effects on quality of life and ability to participate in life roles. However, no studies to date have systematically evaluated the benefits of an intervention for both managing fatigue and promoting physical activity in individuals with MS. This study involves a randomized controlled trial to examine the effectiveness of a telehealth intervention that supports individuals with MS in managing fatigue and increasing physical activity levels.

Methods/Design

A randomly-allocated, three-parallel group, time-series design with a social support program serving as the control group will be used to accomplish the purpose of the study. Our goal is to recruit 189 ambulatory individuals with MS who will be randomized into one of three telehealth interventions: (1) a contact-control social support intervention, (2) a physical activity-only intervention, and (3) a physical activity plus fatigue management intervention. All interventions will last 12?weeks and will be delivered entirely over the phone. Our hypothesis is that, in comparison to the contact-control condition, both the physical activity-only intervention and the physical activity plus fatigue management intervention will yield significant increases in physical activity levels as well as improve fatigue and health and function, with the physical activity plus fatigue management intervention yielding significantly larger improvements. To test this hypothesis, outcome measures will be administered at Weeks 1, 12, and 24. Primary outcomes will be the Fatigue Impact Scale, the Godin Leisure-Time Exercise Questionnaire (GLTEQ), and Actigraph accelerometers. Secondary outcomes will include the SF-12 Survey, Mental Health Inventory, Multiple Sclerosis Impact Scale, the Community Participation Indicator, and psychosocial constructs (e.g., self-efficacy).

Discussion

The proposed study is novel, in that it represents a multi-disciplinary effort to merge two promising lines of research on MS: fatigue management and physical activity promotion. Collectively, the proposed study will be the largest randomized controlled trial to examine the effects of a lifestyle physical activity intervention in people with MS.

Trial registration

NCT01572714     

E-Rehabilitation an Internet and mobile phone based tailored intervention to enhance selfmanagement of Cardiovascular Disease: study protocol for a randomized controlled trial

ABSTRACT: BACKGROUND: Cardiac rehabilitation is very important for the recovery and the secondary prevention of cardiovascular disease, and one of its main strategies is to increase the level of physical activity. Internet and mobile phone based interventions have been successfully used to help people to achieve this. One of the components that are related to the efficacy of these interventions is tailoring of content to the individual. This trial is studying the effect of a longitudinally tailored Internet- and mobile phone based intervention that is based on models of health behaviour, on the level of physical activity and the adherence to the intervention, as an extension of a face-to-face cardiac rehabilitation stay. METHODS: A parallel group, cluster randomized controlled trial. The study population is adult participants of a cardiac rehabilitation programme in Norway with home Internet access and mobile phone, who in monthly clusters are randomized to the control or the intervention condition. Participants have access to a website with information regarding cardiac rehabilitation, an online discussion forum and an online activity calendar. Those randomized to the intervention condition, receive in addition tailored content based on models of health behaviour, through the website and mobile text messages. The objective is to assess the effect of the intervention on maintenance of self-management behaviours after the rehabilitation stay. Main outcome is the level of physical activity one month, three months and one year after the end of the cardiac rehabilitation programme. The randomization of clusters is based on a true random number online service, and participants, investigators and outcome assessor are blinded to the condition of the clusters. DISCUSSION: The study suggests a theory-based intervention that combines models of health behaviour in an innovative way, in order to tailor the delivered content. The users have been actively involved in its design, and because of the use of Open-Source software, the intervention can easily and at low-cost be reproduced and expanded by others. Challenges are the recruitment in the elderly population and the possible underrepresentation of women in the study sample. Funding by Northern Norway Regional Health Authority. Trial registration Trial registry www.clinicaltrials.gov: NCT01223170.     

Will Mobile Diabetes Education Teams (MDETs) in primary care improve patient care processes and health outcomes? Study protocol for a randomized controlled trial

ABSTRACT: BACKGROUND: There is evidence to suggest that delivery of diabetes self-management support by diabetes educators in primary care may improve patient care processes and patient clinical outcomes; however, the evaluation of such a model in primary care is nonexistent in Canada. This article describes the design for the evaluation of the implementation of Mobile Diabetes Education Teams (MDETs) in primary care settings in Canada. METHODS: This study will have a non-blinded, cluster-randomized controlled trial stepped wedge design. A cluster, randomized controlled trial will be used to evaluate the Mobile Diabetes Education Teams' intervention in improving patient clinical and care process outcomes. A total of 1,200 patient charts at participating primary care sites will be reviewed for data extraction. Eligible patients will be those aged >=18, who have type 2 diabetes and a hemoglobin A1c (HbA1c) of >=8 %. Clusters (that is, primary care sites) will be randomized to the intervention and control group using a block randomization procedure within practice size as the blocking factor. A stepped wedge design will be used to sequentially roll out the intervention so that all clusters eventually receive the intervention. The time at which each cluster begins the intervention is randomized to one of the four roll out periods (0, 6, 12, and 18 months). Clusters that are randomized into the intervention later will act as the control for those receiving the intervention earlier. The primary outcome measure will be the difference in the proportion of patients who achieve the recommended HbA1c target of <=7 % between intervention and control groups. Qualitative work (in-depth interviews with primary care physicians, MDET educators and patients; and MDET educators' field notes and debriefing sessions) will be undertaken to assess the implementation process and effectiveness of the MDET intervention.Trial registrationClinicalTrials.gov NCT01553266.     

On small‐sample inference in group randomized trials with binary outcomes and cluster‐level covariates

Group randomized trials (GRTs) randomize groups, or clusters, of people to intervention or control arms. To test for the effectiveness of the intervention when subject‐level outcomes are binary, and while fitting a marginal model that adjusts for cluster‐level covariates and utilizes a logistic link, we develop a pseudo‐Wald statistic to improve inference. Alternative Wald statistics could employ bias‐corrected empirical sandwich standard error estimates, which have received limited attention in the GRT literature despite their broad utility and applicability in our settings of interest. The test could also be carried out using popular approaches based upon cluster‐level summary outcomes. A simulation study covering a variety of realistic GRT settings is used to compare the accuracy of these methods in terms of producing nominal test sizes. Tests based upon the pseudo‐Wald statistic and a cluster‐level summary approach utilizing the natural log of observed cluster‐level odds worked best. Due to weighting, some popular cluster‐level summary approaches were found to lead to invalid inference in many settings. Finally, although use of bias‐corrected empirical sandwich standard error estimates did not consistently result in nominal sizes, they did work well, thus supporting the applicability of marginal models in GRT settings.     

The randomized controlled trial in studies using biomarkers

The randomized controlled trial (RCT) is a scientific experiment during which observations on the effects of therapy or a preventive action are conducted by the researcher under rigorous control. The purpose of the experiment is to clear the uncertainties surrounding a clinical/research issue and involves isolating the 'treatment' and 'end result' variables from external influences. RCTs therefore make use of scientific method standards: measuring, which includes the possibility of reproducing observations; controlling factors unconnected to the cause-effect relationship of interest; and the external verification or 'falsification' of the cause-effect relationship. Many RCTs are now including biomarkers to answer scientific questions in a more accurate way. In the present methodological paper, the main aspects involved in the design and conduction of a trial are discussed, with special emphasis on the use of biomarkers. Aspects that are often overlooked by scientists involved in the design of trials include multiple comparisons, subgroup analysis, the duration of the observations, the use of surrogate endpoints, and ethical issues. This review summarizes the main issues that should be addressed in a protocol, and illustrates these with an example.     

Missing Data in Randomized Clinical Trials for Weight Loss: Scope of the Problem,State of the Field,and Performance of Statistical Methods

Background

Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods.

Methodology/Principal Findings

We searched PubMed and Cochrane databases (2000–2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e^−λt) where λ was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive.

Conclusion/Significance

Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.     

Effects of curcumin supplementation on blood glucose,insulin resistance and androgens in patients with polycystic ovary syndrome: A randomized double-blind placebo-controlled clinical trial

BackgroundCurcumin is a biologically active phytochemical ingredient found in turmeric. It has several pharmacologic effects that might benefit patients with polycystic ovary syndrome (PCOS).ObjectiveWe hypothesized curcumin to be effective in improving blood sugar levels, insulin resistance and hyperandrogenism in individuals with PCOS.MethodsIn a randomized double-blind placebo-controlled trial, individuals with PCOS were treated with curcumin (500 mg three times daily) or placebo for 12 weeks. Primary outcome measures were fasting plasma glucose (FPG), fasting insulin (FI), sex hormone levels, and hirsutism (Ferriman-Gallwey [mFG] score). Secondary outcomes included anthropometric measurements.ResultsOf 72 randomized individuals, 67 completed the trial. The two groups were comparable at baseline. At the end of the study, FPG and Dehydroepiandrosterone levels had decreased significantly in the intervention group compared to control (difference of change (post-pre) between intervention and placebo groups: -4.11 mg/dL; 95% CI: -8.35, -0.35 mg/dL; p = 0.033 and -26.53 microg/dL; 95% CI: -47.99, -4.34 µg/dL; p = 0.035, respectively). We also observed a statistically non-significant increase (p = 0.082) in Estradiol levels in the intervention group compared to control. No serious adverse events were reported throughout the trial.ConclusionsCurcumin might be a safe and useful supplement to ameliorate PCOS-associated hyperandrogenemia and hyperglycemia. However, longer trials investigating different dosages in longer durations are needed to underpin these findings.     

Design and Analysis of Crossover Trials for Absorbing Binary Endpoints

Summary The crossover is a popular and efficient trial design used in the context of patient heterogeneity to assess the effect of treatments that act relatively quickly and whose benefit disappears with discontinuation. Each patient can serve as her own control as within‐individual treatment and placebo responses are compared. Conventional wisdom is that these designs are not appropriate for absorbing binary endpoints, such as death or HIV infection. We explore the use of crossover designs in the context of these absorbing binary endpoints and show that they can be more efficient than the standard parallel group design when there is heterogeneity in individuals' risks. We also introduce a new two‐period design where first period “survivors” are rerandomized for the second period. This design combines the crossover design with the parallel design and achieves some of the efficiency advantages of the crossover design while ensuring that the second period groups are comparable by randomization. We discuss the validity of the new designs and evaluate both a mixture model and a modified Mantel–Haenszel test for inference. The mixture model assumes no carryover or period effects while the Mantel–Haenszel approach conditions out period effects. Simulations are used to compare the different designs and an example is provided to explore practical issues in implementation.     

Cloning and characterisation of glutathione reductase cDNAs and identification of two genes encoding the tobacco enzyme

We have isolated 4 cDNA clones (GRT1-4) encoding glutathione reductase (GR) from a tobacco (Nicotiana tabacum L.) leaf cDNA library. The cDNAs were almost identical: GRT1, GRT3 and GRT4 represented the same gene, differing only in that GRT4 contained an intron within the C-terminal part of the coding sequence. Failure to splice out this intron resulted in a substitution of the final 13 amino acids of the deduced amino acid sequence. A second gene was represented by GRT2. Southern blots indicated that there were two related GR genes in tobacco. The presence of multiple isoforms of GR in tobacco may be explained in part by the expression of a small gene family. In addition, alternative isoforms may result from translation of different mRNAs derived from the same gene by intron skipping during the splicing of nascent GR mRNAs.     

Ensayo clínico aleatorizado de una estrategia de prevención de caídas en ancianos institucionalizados basada en el Mini Falls Assessment Instrument

IntroductionThe Mini Falls Assessment Instrument (MFAI) identifies risk factors for falls in an individual and links them to specific interventions. This study evaluates the effectiveness of MFAI as the basis for a falls prevention strategy in institutionalized elderly.Material and methodsA cluster randomized clinical trial (identifier NCT00888953) was conducted in 16 nursing homes randomized to apply MFAI (intervention) or a modified version not linked to actions (control). The primary endpoint was the occurrence of falls during follow-up (12 months). Secondary variables were total number of falls, physical function, quality of life, functional status, and adverse effects.ResultsData from 330 participants (197 intervention, 137 control) were analyzed. Both groups had a similar number of risk factors: 7 in the intervention group (range 1-12) and 8 (1-13) in the control group. In the intervention group there were more fallers (49% vs. 38%), and higher number of falls (315 vs. 109), and fall rate per 100 person-years (192.5 vs. 179.8) than the control group. In the multivariate analysis, there were no significant differences in fall risk (odds ratio = 1.45; 95% confidence interval [CI]: .67 to 3.14; P = .350), but the incidence rate is significantly higher in the intervention group (Incidence rate ratio = 2.23; 95% CI: 1.43 to 3.48; P < .001).ConclusionsThe results on the efficacy of the MFAI as a fall prevention strategy are inconclusive. Additional studies are needed in order to provide good quality evidence.     

HPTN 071 (PopART): A Cluster-Randomized Trial of the Population Impact of an HIV Combination Prevention Intervention Including Universal Testing and Treatment: Mathematical Model

Background

The HPTN 052 trial confirmed that antiretroviral therapy (ART) can nearly eliminate HIV transmission from successfully treated HIV-infected individuals within couples. Here, we present the mathematical modeling used to inform the design and monitoring of a new trial aiming to test whether widespread provision of ART is feasible and can substantially reduce population-level HIV incidence.

Methods and Findings

The HPTN 071 (PopART) trial is a three-arm cluster-randomized trial of 21 large population clusters in Zambia and South Africa, starting in 2013. A combination prevention package including home-based voluntary testing and counseling, and ART for HIV positive individuals, will be delivered in arms A and B, with ART offered universally in arm A and according to national guidelines in arm B. Arm C will be the control arm. The primary endpoint is the cumulative three-year HIV incidence.We developed a mathematical model of heterosexual HIV transmission, informed by recent data on HIV-1 natural history. We focused on realistically modeling the intervention package. Parameters were calibrated to data previously collected in these communities and national surveillance data.We predict that, if targets are reached, HIV incidence over three years will drop by >60% in arm A and >25% in arm B, relative to arm C. The considerable uncertainty in the predicted reduction in incidence justifies the need for a trial. The main drivers of this uncertainty are possible community-level behavioral changes associated with the intervention, uptake of testing and treatment, as well as ART retention and adherence.

Conclusions

The HPTN 071 (PopART) trial intervention could reduce HIV population-level incidence by >60% over three years. This intervention could serve as a paradigm for national or supra-national implementation. Our analysis highlights the role mathematical modeling can play in trial development and monitoring, and more widely in evaluating the impact of treatment as prevention.     

On the construction of bounds in prospective studies with missing ordinal outcomes: application to the good behavior game trial

This article is concerned with drawing inference about aspects of the population distribution of ordinal outcome data measured on a cohort of individuals on two occasions, where some subjects are missing their second measurement. We present two complementary approaches for constructing bounds under assumptions on the missing data mechanism considered plausible by scientific experts. We develop our methodology within the context of a randomized trial of the "Good Behavior Game," an intervention designed to reduce aggressive misbehavior among children.     

The randomized controlled trial in studies using biomarkers

The randomized controlled trial (RCT) is a scientific experiment during which observations on the effects of therapy or a preventive action are conducted by the researcher under rigorous control. The purpose of the experiment is to clear the uncertainties surrounding a clinical/research issue and involves isolating the 'treatment' and 'end result' variables from external influences. RCTs therefore make use of scientific method standards: measuring, which includes the possibility of reproducing observations; controlling factors unconnected to the cause-effect relationship of interest; and the external verification or 'falsification' of the cause-effect relationship. Many RCTs are now including biomarkers to answer scientific questions in a more accurate way. In the present methodological paper, the main aspects involved in the design and conduction of a trial are discussed, with special emphasis on the use of biomarkers. Aspects that are often overlooked by scientists involved in the design of trials include multiple comparisons, subgroup analysis, the duration of the observations, the use of surrogate endpoints, and ethical issues. This review summarizes the main issues that should be addressed in a protocol, and illustrates these with an example.     

Interim analyses in randomized clinical trials: ramifications and guidelines for practitioners

Recent developments in group sequential methods have had a great impact on the design and analysis of randomized clinical trials. The consequences for both planned and unplanned interim analyses are discussed using several real trials as illustrations. Guidelines for the conduct of interim analysis are given, including tables of nominal significance levels and required sample sizes for several group sequential plans. Areas in need of further theoretical advance include multiple endpoints, estimation of treatment differences, stratification, and design of multiple-armed trials.