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Background: Limits on the frequency of whole blood donation exist primarily to safeguard donor health. However, there is substantial variation across blood services in the maximum frequency of donations allowed. We compared standard practice in the UK with shorter inter-donation intervals used in other countries.Methods: In this parallel group, pragmatic, randomised trial, we recruited whole blood donors aged 18 years or older from 25 centres across England, UK. By use of a computer-based algorithm, men were randomly assigned (1:1:1) to 12-week (standard) versus 10-week versus 8-week inter-donation intervals, and women were randomly assigned (1:1:1) to 16-week (standard) versus 14-week versus 12-week intervals. Participants were not masked to their allocated intervention group. The primary outcome was the number of donations over 2 years. Secondary outcomes related to safety were quality of life, symptoms potentially related to donation, physical activity, cognitive function, haemoglobin and ferritin concentrations, and deferrals because of low haemoglobin. This trial is registered with ISRCTN, number ISRCTN24760606, and is ongoing but no longer recruiting participants.Findings: 45 263 whole blood donors (22 466 men, 22 797 women) were recruited between June 11, 2012, and June 15, 2014. Data were analysed for 45 042 (99·5%) participants. Men were randomly assigned to the 12-week (n=7452) versus 10-week (n=7449) versus 8-week (n=7456) groups; and women to the 16-week (n=7550) versus 14-week (n=7567) versus 12-week (n=7568) groups. In men, compared with the 12-week group, the mean amount of blood collected per donor over 2 years increased by 1·69 units (95% CI 1·59-1·80; approximately 795 mL) in the 8-week group and by 0·79 units (0·69-0·88; approximately 370 mL) in the 10-week group (p<0·0001 for both). In women, compared with the 16-week group, it increased by 0·84 units (95% CI 0·76-0·91; approximately 395 mL) in the 12-week group and by 0·46 units (0·39-0·53; approximately 215 mL) in the 14-week group (p<0·0001 for both). No significant differences were observed in quality of life, physical activity, or cognitive function across randomised groups. However, more frequent donation resulted in more donation-related symptoms (eg, tiredness, breathlessness, feeling faint, dizziness, and restless legs, especially among men [for all listed symptoms]), lower mean haemoglobin and ferritin concentrations, and more deferrals for low haemoglobin (p<0·0001 for each) than those observed in the standard frequency groups.Interpretation: Over 2 years, more frequent donation than is standard practice in the UK collected substantially more blood without having a major effect on donors' quality of life, physical activity, or cognitive function, but resulted in more donation-related symptoms, deferrals, and iron deficiency.Funding: NHS Blood and Transplant, National Institute for Health Research, UK Medical Research Council, and British Heart Foundation. [ABSTRACT FROM AUTHOR]

Mendelian randomisation is an epidemiological method for estimating causal associations from observational data by using genetic variants as instrumental variables. Typically the genetic variants explain only a small proportion of the variation in the risk factor of interest, and so large sample sizes are required, necessitating data from multiple sources. Meta-analysis based on individual patient data requires synthesis of studies which differ in many aspects. A proposed Bayesian framework is able to estimate a causal effect from each study, and combine these using a hierarchical model. The method is illustrated for data on C-reactive protein and coronary heart disease (CHD) from the C-reactive protein CHD Genetics Collaboration (CCGC). Studies from the CCGC differ in terms of the genetic variants measured, the study design (prospective or retrospective, population-based or case-control), whether C-reactive protein was measured, the time of C-reactive protein measurement (pre- or post-disease), and whether full or tabular data were shared. We show how these data can be combined in an efficient way to give a single estimate of causal association based on the totality of the data available. Compared to a two-stage analysis, the Bayesian method is able to incorporate data on 23% additional participants and 51% more events, leading to a 23-26% gain in efficiency. [ABSTRACT FROM AUTHOR]

A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This “multivariable Mendelian randomization” approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol. [ABSTRACT FROM PUBLISHER]

Background: Case-cohort studies are increasingly used to quantify the association of novel factors with disease risk. Conventional measures of predictive ability need modification for this design. We show how Harrell's C-index, Royston's D, and the category-based and continuous versions of the net reclassification index (NRI) can be adapted. Methods: We simulated full cohort and case-cohort data, with sampling fractions ranging from 1% to 90%, using covariates from a cohort study of coronary heart disease, and two incidence rates. We then compared the accuracy and precision of the proposed risk prediction metrics. Results: The C-index and D must be weighted in order to obtain unbiased results. The NRI does not need modification, provided that the relevant non-subcohort cases are excluded from the calculation. The empirical standard errors across simulations were consistent with analytical standard errors for the C-index and D but not for the NRI. Good relative efficiency of the prediction metrics was observed in our examples, provided the sampling fraction was above 40% for the C-index, 60% for D, or 30% for the NRI. Stata code is made available. Conclusions: Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI. [ABSTRACT FROM AUTHOR]

Background An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome.Methods Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate ‘weak instrument’ bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed.Results Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases.Conclusions Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score. [ABSTRACT FROM AUTHOR]

Background Confounding caused by selection bias is often a key difference between non-randomized studies (NRS) and randomized controlled trials (RCTs) of interventions. Key methodological issues In this third paper of the series, we consider issues relating to the inclusion of NRS in systematic reviews on the effects of interventions. We discuss whether potential biases from confounding in NRS can be accounted for, the limitations of current methods for attempting to do so, the different contexts of NRS and RCTs, the problems these issues create for reviewers, and a research agenda for the future. Guidance Reviewers who are considering whether or not to include NRS in meta-analyses must weigh a number of factors. Including NRS may allow a review to address outcomes or pragmatic implementations of an intervention not studied in RCTs, but it will also increase the workload for the review team, as well as their required technical repertoire. Furthermore, the results of a synthesis involving NRS will likely be more difficult to interpret, and less certain, relative to the results of a synthesis involving only randomized studies. When both randomized and non-randomized evidence are available, we favor a strategy of including NRS and RCTs in the same systematic review but synthesizing their results separately. Conclusion Including NRS will often make the limitations of the evidence derived from RCTs more apparent, thereby guiding inferences about generalizability, and may help with the design of the next generation of RCTs. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Causal estimates can be obtained by instrumental variable analysis using a two-stage method. However, these can be biased when the instruments are weak. We introduce a Bayesian method, which adjusts for the first-stage residuals in the second-stage regression and has much improved bias and coverage properties. In the continuous outcome case, this adjustment reduces median bias from weak instruments to close to zero. In the binary outcome case, bias from weak instruments is reduced and the estimand is changed from a marginal population-based effect to a conditional effect. The lack of distributional assumptions on the posterior distribution of the causal effect gives a better summary of uncertainty and more accurate coverage levels than methods that rely on the asymptotic distribution of the causal estimate. We discuss these properties in the context of Mendelian randomization. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background Mendelian randomization is used to test and estimate the magnitude of a causal effect of a phenotype on an outcome by using genetic variants as instrumental variables (IVs). Estimates of association from IV analysis are biased in the direction of the confounded, observational association between phenotype and outcome. The magnitude of the bias depends on the F-statistic for the strength of relationship between IVs and phenotype. We seek to develop guidelines for the design and analysis of Mendelian randomization studies to minimize bias.Methods IV analysis was performed on simulated and real data to investigate the effect on bias of size of study, number and choice of instruments and method of analysis.Results Bias is shown to increase as the expected F-statistic decreases, and can be reduced by using parsimonious models of genetic association (i.e. not over-parameterized) and by adjusting for measured covariates. Using data from a single study, the causal estimate of a unit increase in log-transformed C-reactive protein on fibrinogen (μmol/l) is shown to increase from −0.005 (P = 0.99) to 0.792 (P = 0.00003) due to injudicious choice of instrument. Moreover, when the observed F-statistic is larger than expected in a particular study, the causal estimate is more biased towards the observational association and its standard error is smaller. This correlation between causal estimate and standard error introduces a second source of bias into meta-analysis of Mendelian randomization studies. Bias can be alleviated in meta-analyses by using individual level data and by pooling genetic effects across studies.Conclusions Weak instrument bias is of practical importance for the design and analysis of Mendelian randomization studies. Post hoc choice of instruments, genetic models or data based on measured F-statistics can exacerbate bias. In particular, the commonly cited rule of thumb that F > 10 avoids bias in IV analysis is misleading. [ABSTRACT FROM AUTHOR]

Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

It is commonly assumed that random effects in hierarchical models follow a normal distribution. This can be extremely restrictive in practice. We explore the use of more flexible alternatives for this assumption, namely the t distribution, and skew extensions to the normal and t distributions, implemented using Markov Chain Monte Carlo methods. Models are compared in terms of parameter estimates, deviance information criteria, and predictive distributions. These methods are applied to examples in meta-analysis and health-professional variation, where the distribution of the random effects is of direct interest. The results highlight the importance of allowing for potential skewing and heavy tails in random-effects distributions, especially when estimating a predictive distribution. We describe the extension of these random-effects models to the bivariate case, with application to a meta-analysis examining the relationship between treatment effect and baseline response. We conclude that inferences regarding the random effects can crucially depend on the assumptions made and recommend using a distribution, such as those suggested here, which is more flexible than the normal. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Wei, Lin and Weissfeld (WLW) have applied an elaboration of Cox's proportional hazards regression to the analysis of recurrent events data. This application is controversial and has attracted criticism in a piecemeal fashion over 15 years. A frequently raised concern is the method's 'risk set': each individual is considered to be at risk of all recurrent events from the start of the observation period. The WLW method often gives estimates that exceed those provided by alternative approaches. This paper investigates whether the estimates are a consequence of biased estimation, or reflect a particular aspect of the treatment effect. Simulation studies show that the WLW method infringes the proportional hazards assumption when applied to recurrent events data, but that the bias this may cause is not behind the distinctive effect estimates. Instead, the method's risk set is demonstrated to be responsible, leading to discussion of the interpretation of the treatment effect being estimated. Analyses of medical data indicate that the infringement of the proportional hazards assumption is not necessarily greater than that experienced with other applications of proportional hazards regression and need not prohibit the application of WLW's method to recurrent events data. [ABSTRACT FROM AUTHOR]

Abstract: Differences in the mean, spread and skewness of cost data collected from different countries present problems for analysis and interpretation. Here we develop generalised linear multilevel models to estimate the effects of patient and national characteristics on costs. Using gamma distributions and multiplicative effects for patient characteristics fitted the data better than models which assumed normal distributions or estimated additive effects. A multilevel gamma model is employed to allow for heterogeneity in the effects of patient case-mix across centres. Analysis of multinational cost data must recognise differences in mean, spread and skewness across centres, as well as the data's hierarchical structure. [Copyright &y& Elsevier]

Statistical methods for the analysis of recurrent events are often evaluated in simulation studies. A factor rarely varied in such studies is the underlying event generation process. If the relative performance of statistical methods differs across generation processes, then studies based upon one process may mislead. This paper describes the simulation of recurrent events data using four models of the generation process: Poisson, mixed Poisson, autoregressive, and Weibull. For each model four commonly used statistical methods for the analysis of recurrent events (Cox's proportional hazards method, the Andersen-Gill method, negative binomial regression, the Prentice-Williams-Peterson method) were applied to 200 simulated data sets, and the mean estimates, standard errors, and confidence intervals obtained. All methods performed well for the Poisson process. Otherwise, negative binomial regression only performed well for the mixed Poisson process, as did the Andersen-Gill method with a robust estimate of the standard error. The Prentice-Williams-Peterson method performed well only for the autoregressive and Weibull processes. So the relative performance of statistical methods depended upon the model of event generation used to simulate data. In conclusion, it is important that simulation studies of statistical methods for recurrent events include simulated data sets based upon a range of models for event generation. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: In studies of chronic disease the outcome measure may be based upon the duration of recurring illness. Our example is the UK700 trial of psychiatric case management, where the total number of days spent in hospital over a 2-year follow-up was the primary outcome. Investigations of treatment effect modifiers were undertaken using an analysis of that primary outcome, a comparison of length of hospitalizations, and also a multi-state modeling approach. The days in hospital outcome was relatively straightforward to analyze, and allowed the complete randomized treatment groups to be compared. In contrast, the comparison of length of hospitalizations included only hospitalized patients, with censored observations not being well accommodated. The multi-state model provided separate treatment effect estimates for admission and discharge, this being more informative about how any reduction in days spent in hospital is achieved. Estimation of the treatment effects through the use of proportional hazards regression allowed appropriate incorporation of censored observations. However, with the multi-state model approach treatment effect estimates are not based upon comparisons of complete randomized treatment groups, as individuals are removed from the risk set for admission whilst at risk for discharge, and vice versa. We conclude that total duration is an appropriate primary outcome for clinical trials, but that multi-state models deserve greater use as an informative secondary analysis. [Copyright &y& Elsevier]

Adjustment for baseline variables in a randomized trial can increase power to detect a treatment effect. However, when baseline data are partly missing, analysis of complete cases is inefficient. We consider various possible improvements in the case of normally distributed baseline and outcome variables. Joint modelling of baseline and outcome is the most efficient method. Mean imputation is an excellent alternative, subject to three conditions. Firstly, if baseline and outcome are correlated more than about 0.6 then weighting should be used to allow for the greater information from complete cases. Secondly, imputation should be carried out in a deterministic way, using other baseline variables if possible, but not using randomized arm or outcome. Thirdly, if baselines are not missing completely at random, then a dummy variable for missingness should be included as a covariate (the missing indicator method). The methods are illustrated in a randomized trial in community psychiatry. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Meta-analyses of randomized trials aim to summarise the effects of interventions across many patients, and can seem remote from the clinical issue of how individual patients should be treated and which patient groups will benefit the most from treatment. One method that attempts to address this point entails relating the overall effect in every trial to summaries of patient characteristics. This is called meta-regression. The interpretation of such analyses is not straightforward, however, because of a combination of confounding and other biases. Much more useful is to compare the outcomes for patient subgroups within trials and combine these results across trials. Unfortunately this method is rarely possible using published information, so analyses of individual patient data from trials are necessary. Also, although meta-analyses generally summarise an intervention's effect as a relative risk reduction, the groups of patients with the greatest absolute risk reduction have the most to gain. [ABSTRACT FROM AUTHOR]

Meta-regression has become a commonly used tool for investigating whether study characteristics may explain heterogeneity of results among studies in a systematic review. However, such explorations of heterogeneity are prone to misleading false-positive results. It is unclear how many covariates can reliably be investigated, and how this might depend on the number of studies, the extent of the heterogeneity and the relative weights awarded to the different studies. Our objectives in this paper are two-fold. First, we use simulation to investigate the type I error rate of meta-regression in various situations. Second, we propose a permutation test approach for assessing the true statistical significance of an observed meta-regression finding. Standard meta-regression methods suffer from substantially inflated false-positive rates when heterogeneity is present, when there are few studies and when there are many covariates. These are typical of situations in which meta-regressions are routinely employed. We demonstrate in particular that fixed effect meta-regression is likely to produce seriously misleading results in the presence of heterogeneity. The permutation test appropriately tempers the statistical significance of meta-regression findings. We recommend its use before a statistically significant relationship is claimed from a standard meta-regression analysis. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The usual test statistic is computed by summing the squared deviations of each study's estimate from the overall meta-analytic estimate, weighting each study's contribution in the same manner as in the meta-analysis.

Assesses the cost effectiveness and clinical outcomes of pragmatic randomized trials. Aspects of the cost data considered important; How costs should be compared; Examples from three publications on the analysis of pragmatic randomized trials; Appropriate methods of analysis.

Presents a study that assessed the statistical methods used for health economic evaluations in randomized controlled trials where an estimate of cost is available for each patient. Costs estimation of competing treatments; Methodology; Main outcome measures; Findings.

Any screening program based on a quantitative risk factor has the objective of identifying individuals at high risk of disease. When the risk factor varies within individuals over time or is subject to measurement error, repeat measurements should aid risk assessment. This ankle presents statistical guidelines for sequential screening based on measurements accumulated over time. The aim for each subject is to decide whether (a) risk is sufficiently low that screening can be stopped, or (b) risk is sufficiently high that intervention is indicated, or (c) further measurements should be Obtained to improve risk assessment. The guidelines presented are optimal in a population, public health sense; that is, they are designed to give the maximal expected risk in the group identified for intervention, subject to constraints on that group's size and on the cost of screening (defined in terms of the average number of measurements per person required). An example concerning screening for cardiovascular disease in middle-aged men using diastolic blood pressure measurements is presented. [ABSTRACT FROM AUTHOR]

Investigates the existence and possible significance of the acute-phase responses of C-reactive protein and another sensitive reactant, serum amyloid A protein, in patients with unstable or stable angina. Methods; Findings; Interpretation.

Discusses the relative treatment benefit and underlying risk of interpreting clinical trials using meta-analysis. Variation in treatment benefits; Advantages of evaluation of measurable patient characteristics over meta-analysis of clinical trials; Link between treatment effects and underlying risks.

Investigates the sources of heterogeneity in meta-analysis. Distinction between the concepts of clinical and statistical heterogeneity; Purpose of meta-analysis of a set of clinical trials; Concept of meta-analysis; Correlation of serum cholesterol concentration with the risk of ischemic heart disease.

Suggests that patient outcomes in many randomised trials depend on the health professional delivering the intervention but that this variable is rarely considered in the analysis. Example of when more than one physician is involved in a non-pharmaceutical intervention; How varying levels of skill and effectiveness can invalidate the findings of a study; Clustering and how that can effect the results of a trial; Example of a large trial and the result clustering had on the outcome; Need to anticipate clustering at the time of trial design and increase the sample size to compensate.

The author discusses at the efforts of health study group UK Biobank to improve the prevention, diagnosis, and treatment of diseases. It discusses evidence that increases in physical activity, smoking cessation, and recommend the intake of healthy diet to increase longevity, improving self awareness of health status, and analyses of risk factors for particular diseases and health outcomes.

Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk-set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline-only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome. [ABSTRACT FROM AUTHOR]

Background: Case-cohort studies are increasingly used to quantify the association of novel factors with disease risk. Conventional measures of predictive ability need modification for this design. We show how Harrell's C-index, Royston's D, and the category-based and continuous versions of the net reclassification index (NRI) can be adapted.Methods: We simulated full cohort and case-cohort data, with sampling fractions ranging from 1% to 90%, using covariates from a cohort study of coronary heart disease, and two incidence rates. We then compared the accuracy and precision of the proposed risk prediction metrics.Results: The C-index and D must be weighted in order to obtain unbiased results. The NRI does not need modification, provided that the relevant non-subcohort cases are excluded from the calculation. The empirical standard errors across simulations were consistent with analytical standard errors for the C-index and D but not for the NRI. Good relative efficiency of the prediction metrics was observed in our examples, provided the sampling fraction was above 40% for the C-index, 60% for D, or 30% for the NRI. Stata code is made available.Conclusions: Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI. [ABSTRACT FROM AUTHOR]

Mendelian randomization is the use of genetic instrumental variables to obtain causal inferences from observational data. Two recent developments for combining information on multiple uncorrelated instrumental variables (IVs) into a single causal estimate are as follows: (i) allele scores, in which individual-level data on the IVs are aggregated into a univariate score, which is used as a single IV, and (ii) a summary statistic method, in which causal estimates calculated from each IV using summarized data are combined in an inverse-variance weighted meta-analysis. To avoid bias from weak instruments, unweighted and externally weighted allele scores have been recommended. Here, we propose equivalent approaches using summarized data and also provide extensions of the methods for use with correlated IVs. We investigate the impact of different choices of weights on the bias and precision of estimates in simulation studies. We show that allele score estimates can be reproduced using summarized data on genetic associations with the risk factor and the outcome. Estimates from the summary statistic method using external weights are biased towards the null when the weights are imprecisely estimated; in contrast, allele score estimates are unbiased. With equal or external weights, both methods provide appropriate tests of the null hypothesis of no causal effect even with large numbers of potentially weak instruments. We illustrate these methods using summarized data on the causal effect of low-density lipoprotein cholesterol on coronary heart disease risk. It is shown that a more precise causal estimate can be obtained using multiple genetic variants from a single gene region, even if the variants are correlated. [ABSTRACT FROM AUTHOR]

Background: Increased levels of certain hemostatic factors may play a part in the development of acute coronary syndromes and may be associated with an increased risk of coronary events in patients with angina pectoris. Methods: We conducted a prospective multicenter study of 3043 patients with angina pectoris who underwent coronary angiography and were followed for two years. Base-line measurements included the concentrations of selected hemostatic factors indicative of a thrombophilic state or endothelial injury. The results were analyzed in relation to the subsequent incidence of myocardial infarction or sudden coronary death. Results: After adjustment for the extent of coronary artery disease and other risk factors, an increased incidence of myocardial infarction or sudden death was associated with higher base-line concentrations of fibrinogen (mean ±SD, 3.28±0.74 g per liter in patients who subsequently had coronary events, as compared with 3.00±0.71 g per liter in those who did not; P = 0.01), von Willebrand factor antigen (138±49 percent vs. 125±49 percent, P = 0.05), and tissue plasminogen activator (t-PA) antigen (11.9±4.7 ng per milliliter vs. 10.0±4.2 ng per milliliter, P = 0.02). The concentration of C-reactive protein was also directly correlated with the incidence of coronary events (P = 0.05), except when we adjusted for the fibrinogen concentration. In patients with high serum cholesterol levels, the risk of coronary events rose with increasing levels of fibrinogen and C-reactive protein, but the risk remained low even given high serum cholesterol levels in the presence of low fibrinogen concentrations. Conclusions: In patients with angina pectoris, the levels of fibrinogen, von Willebrand factor antigen, and t-PA antigen are independent predictors of subsequent acute coronary syndromes. In addition, low fibrinogen concentrations characterize patients at low risk for coronary events despite increased serum cholesterol levels. Our data are consistent with a pathogenetic role of impaired fibrinolysis, endothelial-cell injury, and inflammatory activity in the progression of coronary artery disease. (N Engl J Med 1995;332:635-41.) [ABSTRACT FROM AUTHOR]

The case-cohort study design combines the advantages of a cohort study with the efficiency of a nested case-control study. However, unlike more standard observational study designs, there are currently no guidelines for reporting results from case-cohort studies. Our aim was to review recent practice in reporting these studies, and develop recommendations for the future. By searching papers published in 24 major medical and epidemiological journals between January 2010 and March 2013 using PubMed, Scopus and Web of Knowledge, we identified 32 papers reporting case-cohort studies. The median subcohort sampling fraction was 4.1% (interquartile range 3.7% to 9.1%). The papers varied in their approaches to describing the numbers of individuals in the original cohort and the subcohort, presenting descriptive data, and in the level of detail provided about the statistical methods used, so it was not always possible to be sure that appropriate analyses had been conducted. Based on the findings of our review, we make recommendations about reporting of the study design, subcohort definition, numbers of participants, descriptive information and statistical methods, which could be used alongside existing STROBE guidelines for reporting observational studies. [ABSTRACT FROM AUTHOR]

Background Within-person variability in measured values of a risk factor can bias its association with disease. We investigated the extent of regression dilution bias in calculated variables and its implications for comparing the aetiological associations of risk factors.Methods Using a numerical illustration and repeats from 42 300 individuals (12 cohorts), we estimated regression dilution ratios (RDRs) in calculated risk factors [body-mass index (BMI), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)] and in their components (height, weight, waist circumference, and hip circumference), assuming the long-term average exposure to be of interest. Error-corrected hazard ratios (HRs) for risk of coronary heart disease (CHD) were compared across adiposity measures per standard-deviation (SD) change in: (i) baseline and (ii) error-corrected levels.Results RDRs in calculated risk factors depend strongly on the RDRs, correlation, and comparative distributions of the components of these risk factors. For measures of adiposity, the RDR was lower for WHR [RDR: 0.72 (95% confidence interval 0.65–0.80)] than for either of its components [waist circumference: 0.87 (0.85–0.90); hip circumference: 0.90 (0.86–0.93) or for BMI: 0.96 (0.93–0.98) and WHtR: 0.87 (0.85–0.90)], predominantly because of the stronger correlation and more similar distributions observed between waist circumference and hip circumference than between height and weight or between waist circumference and height. Error-corrected HRs for BMI, waist circumference, WHR, and WHtR, were respectively 1.24, 1.30, 1.44, and 1.32 per SD change in baseline levels of these variables, and 1.24, 1.27, 1.35, and 1.30 per SD change in error-corrected levels.Conclusions The extent of within-person variability relative to between-person variability in calculated risk factors can be considerably larger (or smaller) than in its components. Aetiological associations of risk factors should be compared through the use of error-corrected HRs per SD change in error-corrected levels of these risk factors. [ABSTRACT FROM AUTHOR]

Multistage designs allow considerable reductions in the expected sample size of a trial. When stopping for futility or efficacy is allowed at each stage, the expected sample size under different possible true treatment effects ( δ) is of interest. The δ-minimax design is the one for which the maximum expected sample size is minimised amongst all designs that meet the types I and II error constraints. Previous work has compared a two-stage δ-minimax design with other optimal two-stage designs. Applying the δ-minimax design to designs with more than two stages was not previously considered because of computational issues. In this paper, we identify the δ-minimax designs with more than two stages through use of a novel application of simulated annealing. We compare them with other optimal multistage designs and the triangular design. We show that, as for two-stage designs, the δ-minimax design has good expected sample size properties across a broad range of treatment effects but generally has a higher maximum sample size. To overcome this drawback, we use the concept of admissible designs to find trials which balance the maximum expected sample size and maximum sample size. We show that such designs have good expected sample size properties and a reasonable maximum sample size and, thus, are very appealing for use in clinical trials. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Missing outcome data and incomplete uptake of randomised interventions are common problems, which complicate the analysis and interpretation of randomised controlled trials, and are rarely addressed well in practice. To promote the implementation of recent methodological developments, we describe sequences of randomisation-based analyses that can be used to explore both issues. We illustrate these in an Internet-based trial evaluating the use of a new interactive website for those seeking help to reduce their alcohol consumption, in which the primary outcome was available for less than half of the participants and uptake of the intervention was limited. For missing outcome data, we first employ data on intermediate outcomes and intervention use to make a missing at random assumption more plausible, with analyses based on general estimating equations, mixed models and multiple imputation. We then use data on the ease of obtaining outcome data and sensitivity analyses to explore departures from the missing at random assumption. For incomplete uptake of randomised interventions, we estimate structural mean models by using instrumental variable methods. In the alcohol trial, there is no evidence of benefit unless rather extreme assumptions are made about the missing data nor an important benefit in more extensive users of the intervention. These findings considerably aid the interpretation of the trial's results. More generally, the analyses proposed are applicable to many trials with missing outcome data or incomplete intervention uptake. To facilitate use by others, Stata code is provided for all methods. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multivariate meta-analysis is increasingly used in medical statistics. In the univariate setting, the non-iterative method proposed by DerSimonian and Laird is a simple and now standard way of performing random effects meta-analyses. We propose a natural and easily implemented multivariate extension of this procedure which is accessible to applied researchers and provides a much less computationally intensive alternative to existing methods. In a simulation study, the proposed procedure performs similarly in almost all ways to the more established iterative restricted maximum likelihood approach. The method is applied to some real data sets and an extension to multivariate meta-regression is described. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Asymmetry in the funnel plot for a meta-analysis suggests the presence of dissemination bias. This may be caused by publication bias through the decisions of journal editors, by selective reporting of research results by authors or by a combination of both. Typically, study results that are statistically significant or have larger estimated effect sizes are more likely to appear in the published literature, hence giving a biased picture of the evidence-base. Previous statistical approaches for addressing dissemination bias have assumed only a single selection mechanism. Here we consider a more realistic scenario in which multiple dissemination processes, involving both the publishing authors and journals, are operating. In practical applications, the methods can be used to provide sensitivity analyses for the potential effects of multiple dissemination biases operating in meta-analysis. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Studies in health research are commonly carried out in clustered settings, where the individual response data are correlated within clusters. Estimation and modelling of the extent of between-cluster variation contributes to understanding of the current study and to design of future studies. It is common to express between-cluster variation as an intracluster correlation coefficient (ICC), since this measure is directly comparable across outcomes. ICCs are generally reported unaccompanied by confidence intervals. In this paper, we describe a Bayesian modelling approach to interval estimation of the ICC. The flexibility of this framework allows useful extensions which are not easily available in existing methods, for example assumptions other than Normality for continuous outcome data, adjustment for individual-level covariates and simultaneous interval estimation of several ICCs. There is also the opportunity to incorporate prior beliefs on likely values of the ICC. The methods are exemplified using data from a cluster randomized trial. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The sample size required for a cluster randomized trial depends on the magnitude of the intracluster correlation coefficient (ICC). The usual sample size calculation makes no allowance for the fact that the ICC is not known precisely in advance. We develop methods which allow for the uncertainty in a previously observed ICC, using a variety of distributional assumptions. Distributions for the power are derived, reflecting this uncertainty. Further, the observed ICC in a future study will not equal its true value, and we consider the impact of this on power. We implement calculations within a Bayesian simulation approach, and provide one simplification that can be performed using simple simulation within spreadsheet software. In our examples, recognizing the uncertainty in a previous ICC estimate decreases expected power, especially when the power calculated naively from the ICC estimate is high. To protect against the possibility of low power, sample sizes may need to be very substantially increased. Recognizing the variability in the future observed ICC has little effect if prior uncertainty has already been taken into account. We show how our method can be extended to the case in which multiple prior ICC estimates are available. The methods presented in this paper can be used by applied researchers to protect against loss of power, or to choose a design which reduces the impact of uncertainty in the ICC. [ABSTRACT FROM AUTHOR]

Standard statistical analyses of randomized controlled trials with partially missing outcome data often exclude valuable information from individuals with incomplete follow-up. This may lead to biased estimates of the intervention effect and loss of precision. We consider a randomized trial with a repeatedly measured outcome, in which the value of the outcome on the final occasion is of primary interest. We propose a modelling strategy in which the model is successively extended to include baseline values of the outcome, then intermediate values of the outcome, and finally values of other outcome variables. Likelihood-based estimation of random effects models is used, allowing the incorporation of data from individuals with some missing outcomes. Each estimated intervention effect is free of non-response bias under a different missing-at-random assumption. These assumptions become more plausible as the more complex models are fitted, so we propose using the trend in estimated intervention effects to assess the nature of any non-response bias. The methods are applied to data from a trial comparing intensive case management with standard case management for severely psychotic patients. All models give similar estimates of the intervention effect and we conclude that non-response bias is likely to be small. [ABSTRACT FROM AUTHOR]

Examines the effect of breathing other people's tobacco smoke. Emphasis on the possibility of getting lung cancer; Assessment of the non-smoker person living with a smoker; Explanation on the effect of carcinogens.

A letter to the editor is presented in response to the manuscript "Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects," by S. Burgess and S. G. Thompson, published in a 2015 issue.