Propensity Score Matching in Randomized Clinical Trials

Cluster randomization trials with relatively few clusters have been widely used in recent years for evaluation of health-care strategies. On average, randomized treatment assignment achieves balance in both known and unknown confounding factors between treatment groups, however, in practice investigators can only introduce a small amount of stratification and cannot balance on all the important variables simultaneously. The limitation arises especially when there are many confounding variables in small studies. Such is the case in the  INSTINCT  trial designed to investigate the effectiveness of an education program in enhancing the tPA use in stroke patients. In this article, we introduce a new randomization design, the balance match weighted (BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design and aims to minimize the mean squared error (MSE) of the treatment effect estimator. A simulation study shows that, under various confounding scenarios, the BMW design can yield substantial reductions in the MSE for the treatment effect estimator compared to a completely randomized or matched-pair design. The BMW design is also compared with a model-based approach adjusting for the estimated propensity score and Robins-Mark-Newey E-estimation procedure in terms of efficiency and robustness of the treatment effect estimator. These investigations suggest that the BMW design is more robust and usually, although not always, more efficient than either of the approaches. The design is also seen to be robust against heterogeneous error. We illustrate these methods in proposing a design for the  INSTINCT  trial.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78638/1/j.1541-0420.2009.01364.x.pd

Ethical and policy issues in cluster randomized trials: rationale and design of a mixed methods research study

<p>Abstract</p> <p>Background</p> <p>Cluster randomized trials are an increasingly important methodological tool in health research. In cluster randomized trials, intact social units or groups of individuals, such as medical practices, schools, or entire communities – rather than individual themselves – are randomly allocated to intervention or control conditions, while outcomes are then observed on individual cluster members. The substantial methodological differences between cluster randomized trials and conventional randomized trials pose serious challenges to the current conceptual framework for research ethics. The ethical implications of randomizing groups rather than individuals are not addressed in current research ethics guidelines, nor have they even been thoroughly explored. The main objectives of this research are to: (1) identify ethical issues arising in cluster trials and learn how they are currently being addressed; (2) understand how ethics reviews of cluster trials are carried out in different countries (Canada, the USA and the UK); (3) elicit the views and experiences of trial participants and cluster representatives; (4) develop well-grounded guidelines for the ethical conduct and review of cluster trials by conducting an extensive ethical analysis and organizing a consensus process; (5) disseminate the guidelines to researchers, research ethics boards (REBs), journal editors, and research funders.</p> <p>Methods</p> <p>We will use a mixed-methods (qualitative and quantitative) approach incorporating both empirical and conceptual work. Empirical work will include a systematic review of a random sample of published trials, a survey and in-depth interviews with trialists, a survey of REBs, and in-depth interviews and focus group discussions with trial participants and gatekeepers. The empirical work will inform the concurrent ethical analysis which will lead to a guidance document laying out principles, policy options, and rationale for proposed guidelines. An Expert Panel of researchers, ethicists, health lawyers, consumer advocates, REB members, and representatives from low-middle income countries will be appointed. A consensus conference will be convened and draft guidelines will be generated by the Panel; an e-consultation phase will then be launched to invite comments from the broader community of researchers, policy-makers, and the public before a final set of guidelines is generated by the Panel and widely disseminated by the research team.</p

Imputation strategies for missing binary outcomes in cluster randomized trials

<p>Abstract</p> <p>Background</p> <p>Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate dependent missing, we compared six MI strategies which account for the intra-cluster correlation for missing binary outcomes in CRTs with the standard imputation strategies and complete case analysis approach using a simulation study.</p> <p>Method</p> <p>We considered three within-cluster and three across-cluster MI strategies for missing binary outcomes in CRTs. The three within-cluster MI strategies are logistic regression method, propensity score method, and Markov chain Monte Carlo (MCMC) method, which apply standard MI strategies within each cluster. The three across-cluster MI strategies are propensity score method, random-effects (RE) logistic regression approach, and logistic regression with cluster as a fixed effect. Based on the community hypertension assessment trial (CHAT) which has complete data, we designed a simulation study to investigate the performance of above MI strategies.</p> <p>Results</p> <p>The estimated treatment effect and its 95% confidence interval (CI) from generalized estimating equations (GEE) model based on the CHAT complete dataset are 1.14 (0.76 1.70). When 30% of binary outcome are missing completely at random, a simulation study shows that the estimated treatment effects and the corresponding 95% CIs from GEE model are 1.15 (0.76 1.75) if complete case analysis is used, 1.12 (0.72 1.73) if within-cluster MCMC method is used, 1.21 (0.80 1.81) if across-cluster RE logistic regression is used, and 1.16 (0.82 1.64) if standard logistic regression which does not account for clustering is used.</p> <p>Conclusion</p> <p>When the percentage of missing data is low or intra-cluster correlation coefficient is small, different approaches for handling missing binary outcome data generate quite similar results. When the percentage of missing data is large, standard MI strategies, which do not take into account the intra-cluster correlation, underestimate the variance of the treatment effect. Within-cluster and across-cluster MI strategies (except for random-effects logistic regression MI strategy), which take the intra-cluster correlation into account, seem to be more appropriate to handle the missing outcome from CRTs. Under the same imputation strategy and percentage of missingness, the estimates of the treatment effect from GEE and RE logistic regression models are similar.</p

Evaluation of a multi-component community tobacco intervention in three remote Australian Aboriginal communities

Objectives: To assess the effect of community tobacco interventions in Aboriginal communities.\ud \ud Methods: The study consisted of a preand post-study of the effect of a multicomponent tobacco intervention conducted in six Aboriginal communities in the Northern Territory (NT). The intervention included sports sponsorship, health promotion campaigns, training health professionals in the delivery of smoking cessation advice, school education about tobacco, and policy on smoke-free public places. The study was conducted in three intervention communities and three matched control communities. Surveys were used to measure changes in prevalence of tobacco use, changes in knowledge, and attitudes to cessation in intervention communities.\ud \ud Results: Tobacco consumption decreased in one intervention community compared with the matched control community; the trends of consumption (as measured by tobacco ordered through points of sale) in these communities were significantly different (t=-4.5, 95% Cl -33.6 –12.5, p 0.01). Community samples in intervention communities included 920 participants. There was no significant change in the prevalence of tobacco use, although knowledge of the health effects of tobacco and readiness to quit increased.\ud \ud Conclusions: Although it is difficult to demonstrate a reduction in tobacco consumption or in the prevalence of tobacco use as a result of multi-component community tobacco interventions delivered in Aboriginal communities, such interventions can increase awareness of the health effects of tobacco and increase reported readiness to cease tobacco use

Is there still a role for treatment with beta-adrenoceptor antagonists in post-myocardial infarction patients with well-preserved left ventricular systolic function?

The utility of β-adrenoceptor antagonists post myocardial infarction was established in the pre-thrombolytic era. Evidence for improvement in long-term prognosis with metoprolol, timolol and propranolol in particular derives from reduction in event rates in patients who have had substantial left ventricular damage at the time of infarction and probably correlates largely with the more recently demonstrated salutary effects of this group of drugs in patients with chronic heart failure. In all other respects, evidence for beneficial effects of β-adrenoceptor antagonists in peri-infarct and post-infarct therapeutics is equivocal. They appear to exert no major influence on outcomes in patients with unstable angina, nor do they markedly alter early clinical course in uncomplicated acute myocardial infarction, irrespective of other interventions. Furthermore, the limited available analyses suggest no discernible beneficial effect on long-term outcomes post-uncomplicated infarction. It is possible that in such patients, current recommendations for 'routine' long-term β-adrenoceptor blockade can no longer be justified.John D. Horowitz, Margaret A. Arstall, Christopher J. Zeitz, John F. Beltram