Analysis of repeated measures in clinical trials using summary statistics

This thesis is concerned with statistical methodology for randomized clinical trials with repeated measurements over time, as regards both data analysis and the implications for study design. The inherent within-subject dependencies for repeated measurements necessitate analyses that take account of their covariance structure. There exists a whole battery of methods for analysing repeated measures designs, ranging from very simple (e.g. separate t-tests at each time-point) to very complicated (e.g. multi-level models with arbitrary error structures), but I will focus on "the summary statistic approach" which has recently become increasingly popular. When interest centres around the average response to treatment over time, a logical choice of summary statistic is the mean of each subject's post-randomisation measurements, with appropriate adjustment for pre-treatment measurements. Among the class of "mean summary statistics" analysis of covariance (ANCOVA) is shown to be superior to its competitors. In particular, variance formulae are derived both under a general covariance structure and more specific cases (e.g. compound symmetry) , allowing direct comparisons of efficiency among different summary statistics and repeated measures designs. The importance of precise estimates of the pre-entry levels and the consequences for sample size requirements are emphasized. Some additional topics in relation to mean summary statistics, notably; the bias in estimation if pre-treatment means differ, the choice between additive or multiplicative models, and the summary statistic "area under the curve", are also investigated. For studies with restrictions on the range of baseline measurements the negative consequences incurred by "regression to the mean" are explored, especially regarding the variance for between-group comparisons. For a more general class of true treatment effects over time, the optimal linear summary statistic under any covariance structure is derived. Special interest is devoted to the case of linearly diverging mean treatment curves, where the optimal alternative to the comparison of slopes is defined. Asymptotic relative efficiencies are shown to be a useful tool when contrasting different designs and different summary statistics, both in the planning and reporting of repeated measures clinical trials. Finally, comparisons with other approaches are made, and recommendations given based on the need to balance theoretical considerations with practical matters

A comparison of missing data methods for hypothesis tests of the treatment effect in substance abuse clinical trials: a Monte-Carlo simulation study

<p>Abstract</p> <p>Background</p> <p>Missing data due to attrition are rampant in substance abuse clinical trials. However, missing data are often ignored in the presentation of substance abuse clinical trials. This paper demonstrates missing data methods which may be used for hypothesis testing.</p> <p>Methods</p> <p>Methods involving stratifying and weighting individuals based on missing data pattern are shown to produce tests that are robust to missing data mechanisms in terms of Type I error and power. In this article, we describe several methods of combining data that may be used for testing hypotheses of the treatment effect. Furthermore, illustrations of each test's Type I error and power under different missing data percentages and mechanisms are quantified using a Monte-Carlo simulation study.</p> <p>Results</p> <p>Type I error rates were similar for each method, while powers depended on missing data assumptions. Specifically, power was greatest for the weighted, compared to un-weighted methods, especially for greater missing data percentages.</p> <p>Conclusion</p> <p>Results of this study as well as extant literature demonstrate the need for standards of design and analysis specific to substance abuse clinical trials. Given the known substantial attrition rates and concern for the missing data mechanism in substance abuse clinical trials, investigators need to incorporate missing data methods a priori. That is, missing data methods should be specified at the outset of the study and not after the data have been collected.</p

All-arthroscopic versus mini-open repair of small or moderate-sized rotator cuff tears: A protocol for a randomized trial [NCT00128076]

BACKGROUND: Rotator cuff tears are the most common source of shoulder pain and disability. Only poor quality studies have compared mini-open to arthroscopic repair, leaving surgeons with inadequate evidence to support optimal, minimally-invasive repair. METHODS/DESIGN: This randomized, multi-centre, national trial will determine whether an arthroscopic or mini-open repair provides better quality of life for patients with small or moderate-sized rotator cuff tears. A national consensus meeting of investigators in the Joint Orthopaedic Initiative for National Trials of the Shoulder (JOINTS Canada) identified this question as the top priority for shoulder surgeons across Canada. The primary outcome measure is a valid quality-of-life scale (Western Ontario Rotator Cuff (WORC)) that addresses 5 domains of health affected by rotator cuff disease. Secondary outcomes will assess rotator cuff functionality (ROM, strength, Constant score), secondary dimensions of health (general health status (SF-12) and work limitations), and repair integrity (MRI). Outcomes are measured at baseline, at 6 weeks, 3, 6, 12, and 24 months post-operatively by blinded research assistants and musculoskeletal radiologists. Patients (n = 250) with small or medium-sized cuff tears identified by clinical examination and MRI who meet eligibility criteria will be recruited. This sample size will provide 80% power to statistically detect a clinically important difference of 20% in WORC scores between procedures after controlling for baseline WORC score (α = 0.05). A central methods centre will manage randomization, data management, and monitoring under supervision of experienced epidemiologists. Surgeons will participate in either conventional or expertise-based designs according to defined criteria to avoid biases from differential surgeon expertise. Mini-open or all-arthroscopic repair procedures will be performed according to a standardized protocol. Central Adjudication (of cases), Trial Oversight and Safety Committees will monitor trial conduct. We will use an analysis of covariance (ANCOVA), where the baseline WORC score is used as a covariate, to compare the quality of life (WORC score) at 2 years post-operatively. As a secondary analysis, we will conduct the same statistical test but will include age and tear size as covariates with the baseline score. Enrollment will require 2 years and follow-up an additional 2 years. The trial will commence when funding is in place. DISCUSSION: These results will have immediate impact on the practice behaviors of practicing surgeons and surgical trainees at JOINTS centres across Canada. JOINTS Canada is actively engaged in knowledge exchange and will publish and present findings internationally to facilitate wider application. This trial will establish definitive evidence on this question at an international level