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包括对现有文献的系统回顾。这篇综述将检查在评估肌肉骨骼疾病治疗的试验方案中报告的目标差异选择的基础。与已发表的试验结果相比，试验方案提供了更多关于样本量计算的细节。它将为试验人员是否使用远低于或高于MCID的目标差异提供证据，这可能表明试验人员正在考虑其他因素，例如不遵守规定。它还将强调在计算重要差异时，试用者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。我将对患有肌肉骨骼疾病的患者进行一个焦点小组，以评估他们对是否重要的差异的解释是否取决于评估的时间点的意见。时间权衡技术包括向参与者展示不同的场景，其中一种治疗方法可以影响患者病情的时间进程。研究结果将提供患者对不同疗程治疗效果的相对评价。该项目的第三阶段将涉及(a)评估计算重要差异的标准单时间点方法随时间的稳定性，以及(b)比较包含纵向数据的计算目标差异的方法。首先，现有的单一时间点方法将应用于随机对照试验的数据集，这些试验包括腰痛患者的认知行为方法（BeST）和类风湿性关节炎患者的运动计划（SARAH） （Lamb 2010, Lamb 2015）。其次，模拟研究将比较在样本量计算和利用多个时间点数据的相应分析中纳入目标差异的方法。方法将是(1)对每个时间点分别进行单时间点分析（例如ANCOVA），(2)混合线性回归模型，以及(3)曲线下面积（AUC）分析。我们将比较这些方法的有效性和重要性、不同程度的数据丢失对随访的影响以及解释的容易程度。我将提出关于使用特定疾病患者报告结果来指定肌肉骨骼疾病随机试验的目标差异和报告样本量计算的建议。

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.