Improving methods for specifying target differences in sample size calculations for randomised trial of treatments for osteoarthritis

该项目的第一阶段将涉及对现有文献的系统审查。该综述将审查在评估肌肉骨骼疾病治疗的试验方案中报告的靶点差异选择的基础。与已发表的试验结果相比，试验方案提供了更高水平的样本量计算细节。它将提供证据，证明试验者使用的目标差异是否远远低于或高于MCID，这可能表明试验者正在考虑其他因素，如不遵守规定。它还将强调试验人员在计算重要差异时是否以及如何考虑时间。在第二阶段，将使用时间权衡方法检查时间对评估特定疾病患者报告结果的重要差异的影响。我将对肌肉骨骼疾病的患者进行一个焦点小组的调查，以评估他们对重要程度是否存在差异的理解是否取决于评估的时间点。时间权衡技术包括向参与者展示不同的场景，这些场景给出了治疗可能影响患者病情时间进程的不同方式。该项目的第三阶段将涉及(A)评估计算重要差异的标准单一时间点方法随时间的稳定性，以及(B)比较包含纵向数据的目标差异的计算方法。首先，现有的单一时间点方法将应用于下腰痛患者认知行为方法(BEST)和类风湿性关节炎患者手问题锻炼计划(Sarah)的随机对照试验的数据集(Lamb 2010，Lamb 2015)。其次，一项模拟研究将比较将目标差异纳入样本量计算和相应分析的方法，这些方法利用多个时间点的数据。这些方法将是(1)分别针对每个时间点的单一时间点分析(例如ANCOVA)，(2)混合线性回归模型，以及(3)曲线下面积(AUC)分析。这些方法将从权力和重要性的水平、不同损失水平的数据丢失对后续行动的影响以及解释的简易性等方面进行比较。我将使用特定疾病患者的报告结果，就指定目标差异和报告肌肉骨骼疾病随机试验的样本量计算提出建议。

The first stage of the project will involve a systematic review of the existing literature. The review will examine the basis for the choice of target difference as reported in protocols of trials evaluating treatments of musculoskeletal conditions. Trial protocols provide a higher level of detail on sample size calculations than is reported in published trial results. It will provide evidence for whether trialists are using target differences much lower or higher than the MCID which may suggest that trialists are taking other factors into account, such as non-compliance. It will also highlight whether and how trialists take time into account when calculating important differences.During the second stage, the impact of time on the assessment of important differences in disease specific patient reported outcomes will be examined using time trade-off methodology. I will conduct a focus group of patients with musculoskeletal conditions in order to assess their opinions about whether their interpretation of whether a difference in important would depend on the time point of assessment. Time trade-off techniques involve presenting participants with scenarios which give different ways in which a treatment could influence the time course of a patient's condition. The findings will provide estimates of patients relative valuations of treatment benefits of varying duration.The third phase of the project will involve (a) assessing the stability over time of standard single time point approaches to calculating important differences and (b) comparing methods to calculate target differences which incorporate longitudinal data. First, existing single time-point methods will be applied to datasets from randomised controlled trials of a cognitive-behavioural approach in low back pain patients (BeST) and an exercise programme in patients with hand problems due to rheumatoid arthritis (SARAH) (Lamb 2010, Lamb 2015). Second, a simulation study will compare approaches for incorporating target difference in the sample size calculation and correspondinganalysis which utilise data from multiple time points. The approaches will be (1) single time point analysis (e.g. ANCOVA) separately for each time point, (2) mixed linear regression models, and (3) area-under-the-curve (AUC) analysis. These approaches will be compared regarding the level of power and significance, the effect of missing data from different levels of loss to follow-up, and the ease of interpretation. I will produce recommendations on specifying the target difference and reporting sample size calculations for randomised trials in musculoskeletal conditions using disease specific patient reported outcomes.