Improving methods for specifying target differences in sample size calculations for randomised trial of treatments for osteoarthritis
Lead Research Organisation:
University of Oxford
Department Name: NDORMS
Abstract
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 corresponding
analysis 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.
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 corresponding
analysis 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.
Organisations
People |
ORCID iD |
| Jonathan Cook (Primary Supervisor) | |
| Bethan Copsey (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509711/1 | 01/10/2016 | 30/09/2021 | |||
| 1809220 | Studentship | EP/N509711/1 | 01/10/2016 | 30/09/2019 | Bethan Copsey |
| Description | My PhD thesis was submitted prior to the end of the award. Key research methods and skills developed included: presentations, programming and design of simulation studies, conduct of a discrete choice experiment, and experience of patient and public involvement during the study design phase. Several papers have been published in scientific journals based on the PhD components and further papers on the later chapters are in preparation. I am conducting additional simulations based my work on longitudinal methods during my PhD to enhance the paper. The published and planned papers have all been presented at scientific conferences to medical and methodological audiences. |
| Exploitation Route | The systematic reviews highlighted poor reporting of sample size calculations in osteoarthritis trials and on the methods used in the WOMAC measure. Hopefully, this will encourage improved reporting in these areas and spark further research assessing the different versions of the WOMAC measure and ideally, finding a optimal version for use across trials to facilitate comparing and synthesising trial results. The discrete choice experiment highlighted the importance of duration of treatment effect to people living with osteoarthritis. A potential outcome of this work could include encouraging researchers to consider duration more when designing future trials and encouraging clinicians to consider duration of treatment effect when assessing the benefit and risks of different treatments. The planned paper presenting the results of the simulation study would be of interest to applied statisticians who analyse clinical trial data. This should provide insight into the optimal methods to use to analyse longitudinal data and inform their approach to statistical analysis for future trials. It could also encourage methodologists to explore comparing these methods (and others) in additional scenarios. |
| Sectors | Healthcare,Pharmaceuticals and Medical Biotechnology,Other |