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Background: Cluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, "The Randomized Recruitment Intervention Trial (RECRUIT)," designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208)., Methods: The novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented., Results: Simulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach., Conclusion: The article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.

Background: Intention-to-treat (ITT) analysis requires all randomised individuals to be included in the analysis in the groups to which they were randomised. However, there is confusion about how ITT analysis should be performed in the presence of missing outcome data., Purposes: To explain, justify, and illustrate an ITT analysis strategy for randomised trials with incomplete outcome data., Methods: We consider several methods of analysis and compare their underlying assumptions, plausibility, and numbers of individuals included. We illustrate the ITT analysis strategy using data from the UK700 trial in the management of severe mental illness., Results: Depending on the assumptions made about the missing data, some methods of analysis that include all randomised individuals may be less valid than methods that do not include all randomised individuals. Furthermore, some methods of analysis that include all randomised individuals are essentially equivalent to methods that do not include all randomised individuals., Limitations: This work assumes that the aim of analysis is to obtain an accurate estimate of the difference in outcome between randomised groups and not to obtain a conservative estimate with bias against the experimental intervention., Conclusions: Clinical trials should employ an ITT analysis strategy, comprising a design that attempts to follow up all randomised individuals, a main analysis that is valid under a stated plausible assumption about the missing data, and sensitivity analyses that include all randomised individuals in order to explore the impact of departures from the assumption underlying the main analysis. Following this strategy recognises the extra uncertainty arising from missing outcomes and increases the incentive for researchers to minimise the extent of missing data.

Background: The intention-to-treat comparison is the primary, if not the only, analytic approach of many randomized clinical trials., Purpose: To review the shortcomings of intention-to-treat analyses, and of 'as treated' and 'per protocol' analyses as commonly implemented, with an emphasis on problems that are especially relevant for comparative effectiveness research., Methods and Results: In placebo-controlled randomized clinical trials, intention-to-treat analyses underestimate the treatment effect and are therefore nonconservative for both safety trials and noninferiority trials. In randomized clinical trials with an active comparator, intention-to-treat estimates can overestimate a treatment's effect in the presence of differential adherence. In either case, there is no guarantee that an intention-to-treat analysis estimates the clinical effectiveness of treatment. Inverse probability weighting, g-estimation, and instrumental variable estimation can reduce the bias introduced by nonadherence and loss to follow-up in 'as treated' and 'per protocol' analyses., Limitations: These analyse require untestable assumptions, a dose-response model, and time-varying data on confounders and adherence., Conclusions: We recommend that all randomized clinical trials with substantial lack of adherence or loss to follow-up are analyzed using different methods. These include an intention-to-treat analysis to estimate the effect of assigned treatment and 'as treated' and 'per protocol' analyses to estimate the effect of treatment after appropriate adjustment via inverse probability weighting or g-estimation.

Background: Although intention-to-treat analysis is a standard approach, additional supplemental analyses are often required to evaluate the biological relationship among interventions, intermediates, and outcomes. Therefore, we need to evaluate whether the effect of an intervention on a particular outcome is mediated by a hypothesized intermediate variable., Purpose: To evaluate the size of the direct effect in the total effect, we applied the marginal structural model to estimate the average natural direct and indirect effects in a large-scale randomized controlled trial (RCT). Method The average natural direct effect is defined as the difference in the probability of a counterfactual outcome between the experimental and control arms, with the intermediate set to what it would have been, had the intervention been a control treatment. We considered two marginal structural models to estimate the average natural direct and indirect effects introduced by VanderWeele (Epidemiology 2009) and applied them in a large-scale RCT - the Candesartan Antihypertensive Survival Evaluation in Japan (CASE-J trial) - that compared angiotensin receptor blockers and calcium-channel blockers in high-risk hypertensive patients., Results: There were no strong blood pressure-independent or dependent effects; however, a systolic blood pressure reduction of about 1.9  mmHg suppressed all events. Compared to the blood pressure-independent effects of calcium channel blockers, those of angiotensin receptor blockers contributed positively to cardiovascular and cardiac events, but negatively to cerebrovascular events., Limitations: There is a particular condition for estimating the average natural direct effect. It is impossible to check whether this condition is satisfied with the available data., Conclusion: We estimated the average natural direct and indirect effects through the achieved systolic blood pressure in the CASE-J trial. This first application of estimating the average natural effects in an RCT can be useful for obtaining an in-depth understanding of the results and further development of similar interventions.