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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]

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]

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.

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.

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]

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data. [ABSTRACT FROM AUTHOR]

Background: The interpretation of trial results can be helped by understanding how generalisable they are to the target population for which inferences are intended. INTERVAL, a large pragmatic randomised trial of blood donors in England, is assessing the effectiveness and safety of reducing inter-donation intervals. The trial recruited mainly from the blood service's static centres, which collect only about 10 % of whole-blood donations. Hence, the extent to which the trial's participants are representative of the general blood donor population is uncertain. We compare these groups in detail.Methods: We present the CONSORT flowchart from participant invitation to randomisation in INTERVAL. We compare the characteristics of those eligible and consenting to participate in INTERVAL with the general donor population, using the national blood supply 'PULSE' database for the period of recruitment. We compare the characteristics of specific groups of trial participants recruited from different sources, as well as those who were randomised versus those not randomised.Results: From a total of 540,459 invitations, 48,725 donors were eligible and consented to participate in INTERVAL. The proportion of such donors varied from 1-22 % depending on the source of recruitment. The characteristics of those consenting were similar to those of the general population of 1.3 million donors in terms of ethnicity, blood group distribution and recent deferral rates from blood donation due to low haemoglobin. However, INTERVAL participants included more men (50 % versus 44 %), were slightly older (mean age 43.1 versus 42.3 years), included fewer new donors (3 % versus 22 %) and had given more donations over the previous 2 years (mean 3.3 versus 2.2) than the general donor population. Of the consenting participants, 45,263 (93 %) donors were randomised. Compared to those not randomised, the randomised donors showed qualitatively similar differences to those described above.Conclusions: There was broad similarity of participants in INTERVAL with the general blood donor population of England, notwithstanding some differences in age, sex and donation history. Any heterogeneity of the trial's results according to these characteristics will need to be studied to ensure its generalisability to the general donor population.Trial Registration: Current Controlled Trials ISRCTN24760606 . Registered on 25 January 2012. [ABSTRACT FROM AUTHOR]