Impact of dairy fat manipulation on endothelial function and lipid regulation in human aortic endothelial cells exposed to human plasma samples: an in vitro investigation from the RESET study.

PURPOSE: Longer-term intake of fatty acid (FA)-modified dairy products (SFA-reduced, MUFA-enriched) was reported to attenuate postprandial endothelial function in humans, relative to conventional (control) dairy. Thus, we performed an in vitro study in human aortic endothelial cells (HAEC) to investigate mechanisms underlying the effects observed in vivo. METHODS: This sub-study was conducted within the framework of the RESET study, a 12-week randomised controlled crossover trial with FA-modified and control dairy diets. HAEC were incubated for 24 h with post-intervention plasma samples from eleven adults (age: 57.5 ± 6.0 years; BMI: 25.7 ± 2.7 kg/m2) at moderate cardiovascular disease risk following representative sequential mixed meals. Markers of endothelial function and lipid regulation were assessed. RESULTS: Relative to control, HAEC incubation with plasma following the FA-modified treatment increased postprandial NOx production (P-interaction = 0.019), yet up-regulated relative E-selectin mRNA gene expression (P-interaction = 0.011). There was no impact on other genes measured. CONCLUSION: Incubation of HAEC with human plasma collected after longer-term dairy fat manipulation had a beneficial impact on postprandial NOx production. Further ex vivo research is needed to understand the impact of partial replacement of SFA with unsaturated fatty acids in dairy foods on pathways involved in endothelial function.

Postprandial Fatty Acid Profile, but Not Cardiometabolic Risk Markers, Is Modulated by Dairy Fat Manipulation in Adults with Moderate Cardiovascular Disease Risk: The Randomized Controlled REplacement of SaturatEd fat in dairy on Total cholesterol (RESET) Study.

BACKGROUND: Chronic consumption of dairy products with an SFA-reduced, MUFA-enriched content was shown to impact favorably on brachial artery flow-mediated dilatation (FMD). However, their acute effect on postprandial cardiometabolic risk biomarkers requires investigation. OBJECTIVE: The effects of sequential high-fat mixed meals rich in fatty acid (FA)-modified or conventional (control) dairy products on postprandial FMD (primary outcome) and systemic cardiometabolic biomarkers in adults with moderate cardiovascular risk (≥50% above the population mean) were compared. METHODS: In a randomized crossover trial, 52 participants [mean ± SEM age: 53 ± 2 y; BMI (kg/m2) 25.9 ± 0.5] consumed a high-dairy-fat breakfast (0 min; ∼50 g total fat: modified: 25 g SFAs, 20 g MUFAs; control: 32 g SFAs, 12 g MUFAs) and lunch (330 min; ∼30 g total fat; modified: 15 g SFAs, 12 g MUFAs; control: 19 g SFAs, 7 g MUFAs). Blood samples were obtained before and until 480 min after breakfast, with FMD assessed at 0, 180, 300, and 420 min. Data were analyzed by linear mixed models. RESULTS: Postprandial changes in cardiometabolic biomarkers were comparable between the different dairy meals, with the exception of a tendency for a 4% higher AUC for the %FMD response following the modified-dairy-fat meals (P = 0.075). Plasma total lipid FA analysis revealed that incremental AUC responses were 53% lower for total SFAs, 214% and 258% higher for total cis-MUFAs (predominantly cis-9 18:1), and trans-18:1, respectively, following the modified relative to the control dairy meals (all P < 0.0001). CONCLUSIONS: In adults at moderate cardiovascular risk, acute consumption of sequential high-fat meals containing FA-modified dairy products had little impact on postprandial endothelial function or systemic cardiometabolic biomarkers, but a differential effect on the plasma total lipid FA profile, relative to conventional dairy fat meals.This trial was registered at clinicaltrials.gov as NCT02089035.

Assessment of the information theory approach to evaluating time-to-event surrogate and true endpoints in a meta-analytic setting.

In many disease areas, commonly used long-term clinical endpoints are becoming increasingly difficult to implement due to long follow-up times and/or increased costs. Shorter-term surrogate endpoints are urgently needed to expedite drug development, the evaluation of which requires robust and reliable statistical methodology to drive meaningful clinical conclusions about the strength of relationship with the true long-term endpoint. This paper uses a simulation study to explore one such previously proposed method, based on information theory, for evaluation of time to event surrogate and long-term endpoints, including the first examination within a meta-analytic setting of multiple clinical trials with such endpoints. The performance of the information theory method is examined for various scenarios including different dependence structures, surrogate endpoints, censoring mechanisms, treatment effects, trial and sample sizes, and for surrogate and true endpoints with a natural time-ordering. Results allow us to conclude that, contrary to some findings in the literature, the approach provides estimates of surrogacy that may be substantially lower than the true relationship between surrogate and true endpoints, and rarely reach a level that would enable confidence in the strength of a given surrogate endpoint. As a result, care is needed in the assessment of time to event surrogate and true endpoints based only on this methodology.

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection.

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two-stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.

Comparison of Bayesian and frequentist group-sequential clinical trial designs.

BACKGROUND: There is a growing interest in the use of Bayesian adaptive designs in late-phase clinical trials. This includes the use of stopping rules based on Bayesian analyses in which the frequentist type I error rate is controlled as in frequentist group-sequential designs. METHODS: This paper presents a practical comparison of Bayesian and frequentist group-sequential tests. Focussing on the setting in which data can be summarised by normally distributed test statistics, we evaluate and compare boundary values and operating characteristics. RESULTS: Although Bayesian and frequentist group-sequential approaches are based on fundamentally different paradigms, in a single arm trial or two-arm comparative trial with a prior distribution specified for the treatment difference, Bayesian and frequentist group-sequential tests can have identical stopping rules if particular critical values with which the posterior probability is compared or particular spending function values are chosen. If the Bayesian critical values at different looks are restricted to be equal, O'Brien and Fleming's design corresponds to a Bayesian design with an exceptionally informative negative prior, Pocock's design to a Bayesian design with a non-informative prior and frequentist designs with a linear alpha spending function are very similar to Bayesian designs with slightly informative priors.This contrasts with the setting of a comparative trial with independent prior distributions specified for treatment effects in different groups. In this case Bayesian and frequentist group-sequential tests cannot have the same stopping rule as the Bayesian stopping rule depends on the observed means in the two groups and not just on their difference. In this setting the Bayesian test can only be guaranteed to control the type I error for a specified range of values of the control group treatment effect. CONCLUSIONS: Comparison of frequentist and Bayesian designs can encourage careful thought about design parameters and help to ensure appropriate design choices are made.

A Review of Clinical Trials With an Adaptive Design and Health Economic Analysis.

OBJECTIVE: An adaptive design uses data collected as a clinical trial progresses to inform modifications to the trial. Hence, adaptive designs and health economics aim to facilitate efficient and accurate decision making. Nevertheless, it is unclear whether the methods are considered together in the design, analysis, and reporting of trials. This review aims to establish how health economic outcomes are used in the design, analysis, and reporting of adaptive designs. METHODS: Registered and published trials up to August 2016 with an adaptive design and health economic analysis were identified. The use of health economics in the design, analysis, and reporting was assessed. Summary statistics are presented and recommendations formed based on the research team's experiences and a practical interpretation of the results. RESULTS: Thirty-seven trials with an adaptive design and health economic analysis were identified. It was not clear whether the health economic analysis accounted for the adaptive design in 17/37 trials where this was thought necessary, nor whether health economic outcomes were used at the interim analysis for 18/19 of trials with results. The reporting of health economic results was suboptimal for the (17/19) trials with published results. CONCLUSIONS: Appropriate consideration is rarely given to the health economic analysis of adaptive designs. Opportunities to use health economic outcomes in the design and analysis of adaptive trials are being missed. Further work is needed to establish whether adaptive designs and health economic analyses can be used together to increase the efficiency of health technology assessments without compromising accuracy.

Development process of a consensus-driven CONSORT extension for randomised trials using an adaptive design.

BACKGROUND: Adequate reporting of adaptive designs (ADs) maximises their potential benefits in the conduct of clinical trials. Transparent reporting can help address some obstacles and concerns relating to the use of ADs. Currently, there are deficiencies in the reporting of AD trials. To overcome this, we have developed a consensus-driven extension to the CONSORT statement for randomised trials using an AD. This paper describes the processes and methods used to develop this extension rather than detailed explanation of the guideline. METHODS: We developed the guideline in seven overlapping stages: 1) Building on prior research to inform the need for a guideline; 2) A scoping literature review to inform future stages; 3) Drafting the first checklist version involving an External Expert Panel; 4) A two-round Delphi process involving international, multidisciplinary, and cross-sector key stakeholders; 5) A consensus meeting to advise which reporting items to retain through voting, and to discuss the structure of what to include in the supporting explanation and elaboration (E&E) document; 6) Refining and finalising the checklist; and 7) Writing-up and dissemination of the E&E document. The CONSORT Executive Group oversaw the entire development process. RESULTS: Delphi survey response rates were 94/143 (66%), 114/156 (73%), and 79/143 (55%) in rounds 1, 2, and across both rounds, respectively. Twenty-seven delegates from Europe, the USA, and Asia attended the consensus meeting. The main checklist has seven new and nine modified items and six unchanged items with expanded E&E text to clarify further considerations for ADs. The abstract checklist has one new and one modified item together with an unchanged item with expanded E&E text. The E&E document will describe the scope of the guideline, the definition of an AD, and some types of ADs and trial adaptations and explain each reporting item in detail including case studies. CONCLUSIONS: We hope that making the development processes, methods, and all supporting information that aided decision-making transparent will enhance the acceptability and quick uptake of the guideline. This will also help other groups when developing similar CONSORT extensions. The guideline is applicable to all randomised trials with an AD and contains minimum reporting requirements.

Point estimation following two-stage adaptive threshold enrichment clinical trials.

Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial.

Plasma phospholipid fatty acid profile confirms compliance to a novel saturated fat-reduced, monounsaturated fat-enriched dairy product intervention in adults at moderate cardiovascular risk: a randomized controlled trial.

BACKGROUND: Dairy products are a major contributor to dietary SFA. Partial replacement of milk SFA with unsaturated fatty acids (FAs) is possible through oleic-acid rich supplementation of the dairy cow diet. To assess adherence to the intervention of SFA-reduced, MUFA-enriched dairy product consumption in the RESET (REplacement of SaturatEd fat in dairy on Total cholesterol) study using 4-d weighed dietary records, in addition to plasma phospholipid FA (PL-FA) status. METHODS: In a randomised, controlled, crossover design, free-living UK participants identified as moderate risk for CVD (n = 54) were required to replace habitually consumed dairy foods (milk, cheese and butter), with study products with a FA profile typical of retail products (control) or SFA-reduced, MUFA-enriched profile (modified), for two 12-week periods, separated by an 8-week washout period. A flexible food-exchange model was used to implement each isoenergetic high-fat, high-dairy diet (38% of total energy intake (%TE) total fat): control (dietary target: 19%TE SFA; 11%TE MUFA) and modified (16%TE SFA; 14%TE MUFA). RESULTS: Following the modified diet, there was a smaller increase in SFA (17.2%TE vs. 19.1%TE; p < 0.001) and greater increase in MUFA intake (15.4%TE vs. 11.8%TE; p < 0.0001) when compared with the control. PL-FA analysis revealed lower total SFAs (p = 0.006), higher total cis-MUFAs and trans-MUFAs (both p < 0.0001) following the modified diet. CONCLUSION: The food-exchange model was successfully used to achieve RESET dietary targets by partial replacement of SFAs with MUFAs in dairy products, a finding reflected in the PL-FA profile and indicative of objective dietary compliance. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02089035 , date 05-01-2014.

An investigation into the two-stage meta-analytic copula modelling approach for evaluating time-to-event surrogate endpoints which comprise of one or more events of interest.

Clinical trials of experimental treatments must be designed with primary endpoints that directly measure clinical benefit for patients. In many disease areas, the recognised gold standard primary endpoint can take many years to mature, leading to challenges in the conduct and quality of clinical studies. There is increasing interest in using shorter-term surrogate endpoints as substitutes for costly long-term clinical trial endpoints; such surrogates need to be selected according to biological plausibility, as well as the ability to reliably predict the unobserved treatment effect on the long-term endpoint. A number of statistical methods to evaluate this prediction have been proposed; this paper uses a simulation study to explore one such method in the context of time-to-event surrogates for a time-to-event true endpoint. This two-stage meta-analytic copula method has been extensively studied for time-to-event surrogate endpoints with one event of interest, but thus far has not been explored for the assessment of surrogates which have multiple events of interest, such as those incorporating information directly from the true clinical endpoint. We assess the sensitivity of the method to various factors including strength of association between endpoints, the quantity of data available, and the effect of censoring. In particular, we consider scenarios where there exist very little data on which to assess surrogacy. Results show that the two-stage meta-analytic copula method performs well under certain circumstances and could be considered useful in practice, but demonstrates limitations that may prevent universal use.

Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations.

Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under- or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re-assessment of the sample size in an ongoing trial. These methods usually focus on estimating the variance. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. We develop and compare different methods for blinded estimation of the correlation coefficient that are less likely to introduce operational bias when the blinding is maintained. Their performance with respect to bias and standard error is compared to the unblinded estimator. We simulated two different settings: one assuming that all group means are the same and one assuming that different groups have different means. Simulation results show that the naïve (one-sample) estimator is only slightly biased and has a standard error comparable to that of the unblinded estimator. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups.

Effect of a synbiotic on the response to seasonal influenza vaccination is strongly influenced by degree of immunosenescence.

BACKGROUND: Ageing increases risk of respiratory infections and impairs the response to influenza vaccination. Pre- and probiotics offer an opportunity to modulate anti-viral defenses and the response to vaccination via alteration of the gut microbiota. This study investigated the effect of a novel probiotic, Bifidobacterium longum bv. infantis CCUG 52,486, combined with a prebiotic, gluco-oligosaccharide (B. longum + Gl-OS), on the response to seasonal influenza vaccination in young and older subjects in a double-blind, randomized controlled trial, taking into account the influence of immunosenescence markers at baseline. RESULTS: Vaccination resulted in a significant increase in total antibody titres, vaccine-specific IgA, IgM and IgG and seroprotection to all three subunits of the vaccine in both young and older subjects, and in general, the increases in young subjects were greater. There was little effect of the synbiotic, although it tended to reduce seroconversion to the Brisbane subunit of the vaccine and the vaccine-specific IgG response in older subjects. Immunological characterization revealed that older subjects randomized to the synbiotic had a significantly higher number of senescent (CD28(-)CD57(+)) helper T cells at baseline compared with those randomized to the placebo, and they also had significantly higher plasma levels of anti-CMV IgG and a greater tendency for CMV seropositivity. Moreover, higher numbers of CD28(-)CD57(+) helper T cells were associated with failure to seroconvert to Brisbane, strongly suggesting that the subjects randomized to the synbiotic were already at a significant disadvantage in terms of likely ability to respond to the vaccine compared with those randomized to the placebo. CONCLUSIONS: Ageing was associated with marked impairment of the antibody response to influenza vaccination in older subjects and the synbiotic failed to reverse this impairment. However, the older subjects randomized to the synbiotic were at a significant disadvantage due to a greater degree of immunosenscence at baseline compared with those randomized to the placebo. Thus, baseline differences in immunosenescence between the randomized groups are likely to have influenced the outcome of the intervention, highlighting the need for detailed immunological characterization of subjects prior to interventions. TRIAL REGISTRATION: Clinicaltrials.gov NCT01066377.

Estimation after subpopulation selection in adaptive seamless trials.

During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios.

Flexible selection of a single treatment incorporating short-term endpoint information in a phase II/III clinical trial.

Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short-term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short-term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease.

A comparison of methods for treatment selection in seamless phase II/III clinical trials incorporating information on short-term endpoints.

In an adaptive seamless phase II/III clinical trial interim analysis, data are used for treatment selection, enabling resources to be focused on comparison of more effective treatment(s) with a control. In this paper, we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis. The comparison focuses on the power and the probability of correctly identifying the most promising treatment. We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short- and long-term endpoints.

Development of a food-exchange model to replace saturated fat with MUFAs and n-6 PUFAs in adults at moderate cardiovascular risk.

The recommendation to reduce saturated fatty acid (SFA) consumption to ≤10% of total energy (%TE) is a key public health target aimed at lowering cardiovascular disease (CVD) risk. Replacement of SFA with unsaturated fats may provide greater benefit than replacement with carbohydrates, yet the optimal type of fat is unclear. The aim of the DIVAS (Dietary Intervention and Vascular Function) study was to develop a flexible food-exchange model to investigate the effects of substituting SFAs with monounsaturated fatty acids (MUFAs) or n-6 (ω-6) polyunsaturated fatty acids (PUFAs) on CVD risk factors. In this parallel study, UK adults aged 21-60 y with moderate CVD risk (50% greater than the population mean) were identified using a risk assessment tool (n = 195; 56% females). Three 16-wk isoenergetic diets of specific fatty acid (FA) composition (%TE SFA:%TE MUFA:%TE n-6 PUFA) were designed using spreads, oils, dairy products, and snacks as follows: 1) SFA-rich diet (17:11:4; n = 65); 2) MUFA-rich diet (9:19:4; n = 64); and 3) n-6 PUFA-rich diet (9:13:10; n = 66). Each diet provided 36%TE total fat. Dietary targets were broadly met for all intervention groups, reaching 17.6 ± 0.4%TE SFA, 18.5 ± 0.3%TE MUFA, and 10.4 ± 0.3%TE n-6 PUFA in the respective diets, with significant overall diet effects for the changes in SFAs, MUFAs, and n-6 PUFAs between groups (P < 0.001). There were no differences in the changes of total fat, protein, carbohydrate, and alcohol intake or anthropometric measures between groups. Plasma phospholipid FA composition showed changes from baseline in the proportions of total SFAs, MUFAs, and n-6 PUFAs for each diet group, with the changes in SFAs and MUFAs differing between the groups (P < 0.001). In conclusion, successful implementation of the food-exchange model broadly achieved the dietary target intakes for the exchange of SFAs with MUFAs or n-6 PUFAs with minimal disruption to the overall diet in a free-living population. This trial was registered at clinicaltrials.gov as NCT01478958.

Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data.

Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.

A comparison of methods for constructing confidence intervals after phase II/III clinical trials.

Recently, in order to accelerate drug development, trials that use adaptive seamless designs such as phase II/III clinical trials have been proposed. Phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages. Using stage 1 data, an interim analysis is performed to answer phase II objectives and after collection of stage 2 data, a final confirmatory analysis is performed to answer phase III objectives. In this paper we consider phase II/III clinical trials in which, at stage 1, several experimental treatments are compared to a control and the apparently most effective experimental treatment is selected to continue to stage 2. Although these trials are attractive because the confirmatory analysis includes phase II data from stage 1, the inference methods used for trials that compare a single experimental treatment to a control and do not have an interim analysis are no longer appropriate. Several methods for analysing phase II/III clinical trials have been developed. These methods are recent and so there is little literature on extensive comparisons of their characteristics. In this paper we review and compare the various methods available for constructing confidence intervals after phase II/III clinical trials.

Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility.

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations.

A review of the evidence for the effects of total dietary fat, saturated, monounsaturated and n-6 polyunsaturated fatty acids on vascular function, endothelial progenitor cells and microparticles.

Vascular dysfunction is recognised as an integrative marker of CVD. While dietary strategies aimed at reducing CVD risk include reductions in the intake of SFA, there are currently no clear guidelines on what should replace SFA. The purpose of this review was to assess the evidence for the effects of total dietary fat and individual fatty acids (SFA, MUFA and n-6 PUFA) on vascular function, cellular microparticles and endothelial progenitor cells. Medline was systematically searched from 1966 until November 2010. A total of fifty-nine peer-reviewed publications (covering fifty-six studies), which included five epidemiological, eighteen dietary intervention and thirty-three test meal studies, were identified. The findings from the epidemiological studies were inconclusive. The limited data available from dietary intervention studies suggested a beneficial effect of low-fat diets on vascular reactivity, which was strongest when the comparator diet was high in SFA, with a modest improvement in measures of vascular reactivity when high-fat, MUFA-rich diets were compared with SFA-rich diets. There was consistent evidence from the test meal studies that high-fat meals have a detrimental effect on postprandial vascular function. However, the evidence for the comparative effects of test meals rich in MUFA or n-6 PUFA with SFA on postprandial vascular function was limited and inconclusive. The lack of studies with comparable within-study dietary fatty acid targets, a variety of different study designs and different methods for determining vascular function all confound any clear conclusions on the impact of dietary fat and individual fatty acids on vascular function.