National Health and Medical Research Council.

Stepped wedge and cluster randomised crossover trials are examples of cluster randomised designs conducted over multiple time periods that are being used with increasing frequency in health research. Recent systematic reviews of both of these designs indicate that the within-cluster correlation is typically taken account of in the analysis of data using a random intercept mixed model, implying a constant correlation between any two individuals in the same cluster no matter how far apart in time they are measured: within-period and between-period intra-cluster correlations are assumed to be identical. Recently proposed extensions allow the within- and between-period intra-cluster correlations to differ, although these methods require that all between-period intra-cluster correlations are identical, which may not be appropriate in all situations. Motivated by a proposed intensive care cluster randomised trial, we propose an alternative correlation structure for repeated cross-sectional multiple-period cluster randomised trials in which the between-period intra-cluster correlation is allowed to decay depending on the distance between measurements. We present results for the variance of treatment effect estimators for varying amounts of decay, investigating the consequences of the variation in decay on sample size planning for stepped wedge, cluster crossover and multiple-period parallel-arm cluster randomised trials. We also investigate the impact of assuming constant between-period intra-cluster correlations instead of decaying between-period intra-cluster correlations. Our results indicate that in certain design configurations, including the one corresponding to the proposed trial, a correlation decay can have an important impact on variances of treatment effect estimators, and hence on sample size and power. An R Shiny app allows readers to interactively explore the impact of correlation decay.

IMPORTANCE After severe traumatic brain injury, induction of prophylactic hypothermia has been suggested to be neuroprotective and improve long-term neurologic outcomes. OBJECTIVE To determine the effectiveness of early prophylactic hypothermia compared with normothermic management of patients after severe traumatic brain injury. DESIGN, SETTING, AND PARTICIPANTS The Prophylactic Hypothermia Trial to Lessen Traumatic Brain Injury-Randomized Clinical Trial (POLAR-RCT) was a multicenter randomized trial in 6 countries that recruited 511 patients both out-of-hospital and in emergency departments after severe traumatic brain injury. The first patient was enrolled on December 5, 2010, and the last on November 10, 2017. The final date of follow-up was May 15, 2018. INTERVENTIONS There were 266 patients randomized to the prophylactic hypothermia group and 245 to normothermic management. Prophylactic hypothermia targeted the early induction of hypothermia (33°C-35°C) for at least 72 hours and up to 7 days if intracranial pressures were elevated, followed by gradual rewarming. Normothermia targeted 37°C, using surface-cooling wraps when required. Temperature was managed in both groups for 7 days. All other care was at the discretion of the treating physician. MAIN OUTCOMES AND MEASURES The primary outcome was favorable neurologic outcomes or independent living (Glasgow Outcome Scale-Extended score, 5-8 [scale range, 1-8]) obtained by blinded assessors 6 months after injury. RESULTS Among 511 patients who were randomized, 500 provided ongoing consent (mean age, 34.5 years [SD, 13.4]; 402 men [80.2%]) and 466 completed the primary outcome evaluation. Hypothermia was initiated rapidly after injury (median, 1.8 hours [IQR, 1.0-2.7 hours]) and rewarming occurred slowly (median, 22.5 hours [IQR, 16-27 hours]). Favorable outcomes (Glasgow Outcome Scale-Extended score, 5-8) at 6 months occurred in 117 patients (48.8%) in the hypothermia group and 111 (49.1%) in the normothermia group (risk difference, 0.4% [95% CI,-9.4% to 8.7%]; relative risk with hypothermia, 0.99 [95% CI, 0.82-1.19]; P = .94). In the hypothermia and normothermia groups, the rates of pneumonia were 55.0% vs 51.3%, respectively, and rates of increased intracranial bleeding were 18.1% vs 15.4%, respectively. CONCLUSIONS AND RELEVANCE Among patients with severe traumatic brain injury, early prophylactic hypothermia compared with normothermia did not improve neurologic outcomes at 6 months. These findings do not support the use of early prophylactic hypothermia for patients with severe traumatic brain injury.

IMPORTANCE Most clinical guidelines do not recommend platelet-rich plasma (PRP) for knee osteoarthritis (OA) because of lack of high-quality evidence on efficacy for symptoms and joint structure, but the guidelines emphasize the need for rigorous studies. Despite this, use of PRP in knee OA is increasing.OBJECTIVE To evaluate the effects of intra-articular PRP injections on symptoms and joint structure in patients with symptomatic mild to moderate radiographic medial knee OA.DESIGN, SETTING, AND PARTICIPANTS This randomized, 2-group, placebo-controlled, participant-, injector-, and assessor-blinded clinical trial enrolled community-based participants (n = 288) aged 50 years or older with symptomatic medial knee OA (Kellgren and Lawrence grade 2 or 3) in Sydney and Melbourne, Australia, from August 24, 2017, to July 5, 2019. The 12-month follow-up was completed on July 22, 2020.INTERVENTIONS Interventions involved 3 intra-articular injections at weekly intervals of either leukocyte-poor PRP using a commercially available product (n = 144 participants) or saline placebo (n = 144 participants). MAIN OUTCOMES AND MEASURESThe 2 primary outcomes were 12-month change in overall average knee pain scores (11-point scale; range, 0-10, with higher scores indicating worse pain; minimum clinically important difference of 1.8) and percentage change in medial tibial cartilage volume as assessed by magnetic resonance imaging (MRI). Thirty-one secondary outcomes (25 symptom related and 6 MRI assessed; minimum clinically important difference not known) evaluated pain, function, quality of life, global change, and joint structures at 2-month and/or 12-month follow-up. RESULTS Among 288 patients who were randomized (mean age, 61.9 [SD, 6.5] years; 169 [59%] women), 269 (93%) completed the trial. In both groups, 140 participants (97%) received all 3 injections. After 12 months, treatment with PRP vs placebo injection resulted in a mean change in knee pain scores of −2.1 vs −1.8 points, respectively (difference, −0.4 [95% CI, −0.9 to 0.2] points; P = .17). The mean change in medial tibial cartilage volume was −1.4% vs −1.2%, respectively (difference, −0.2% [95% CI, −1.9% to 1.5%]; P = .81). Of 31 prespecified secondary outcomes, 29 showed no significant between-group differences.CONCLUSIONS AND RELEVANCE Among patients with symptomatic mild to moderate radiographic knee OA, intra-articular injection of PRP, compared with injection of saline placebo, did not result in a significant difference in symptoms or joint structure at 12 months. These findings do not support use of PRP for the management of knee OA.

Objective. To investigate whether a 12-week physical therapist-delivered combined pain coping skills training (PCST) and exercise (PCST/exercise) is more efficacious and cost effective than either treatment alone for knee osteoarthritis (OA). Methods. This was an assessor-blinded, 3-arm randomized controlled trial in 222 people (73 PCST/exercise, 75 exercise, and 74 PCST) ages ‡50 years with knee OA. All participants received 10 treatments over 12 weeks plus a home program. PCST covered pain education and training in cognitive and behavioral pain coping skills, exercise comprised strengthening exercises, and PCST/exercise integrated both. Primary outcomes were self-reported average knee pain (visual analog scale, range 0-100 mm) and physical function (Western Ontario and McMaster Universities Osteoarthritis Index, range 0-68) at week 12. Secondary outcomes included other pain measures, global change, physical performance, psychological health, physical activity, quality of life, and cost effectiveness. Analyses were by intent-totreat methodology with multiple imputation for missing data. Results. A total of 201 participants (91%), 181 participants (82%), and 186 participants (84%) completed week 12, 32, and 52 measurements, respectively. At week 12, there were no significant between-group differences for reductions in pain comparing PCST/exercise versus exercise (mean difference 5.8 mm [95% confidence interval (95% CI) 21.4, 13.0]) and PCST/exercise versus PCST (6.7 mm [95% CI 20.6, 14.1]). Significantly greater improvements in function were found for PCST/exercise versus exercise (3.7 units [95% CI 0.4, 7.0]) and PCST/exercise versus PCST (7.9 units [95% CI 4.7, 11.2]). These differences persisted at weeks 32 (both) and 52 (PCST). Benefits favoring PCST/exercise were seen on several secondary outcomes. Cost effectiveness of PCST/exercise was not demonstrated. Conclusion. This model of care could improve access to psychological treatment and augment patient outcomes from exercise in knee OA, although it did not appear to be cost effective.

Objective. To investigate whether simultaneous telephone coaching improves the clinical effectiveness of a physiotherapist-prescribed home-based physical activity program for knee osteoarthritis (OA). Methods. A total of 168 inactive adults ages ‡50 years with knee pain on a numeric rating scale ‡4 (NRS; range 0-10) and knee OA were recruited from the community and randomly assigned to a physiotherapy (PT) and coaching group (n 5 84) or PTonly (n 5 84) group. All participants received five 30-minute consultations with a physiotherapist over 6 months for education, home exercise, and physical activity advice. PT1coaching participants also received 6-12 telephone coaching sessions by clinicians trained in behavioral-change support for exercise and physical activity. Primary outcomes were pain (NRS) and physical function (Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC; score range 0-68]) at 6 months. Secondary outcomes were these same measures at 12 and 18 months, as well as physical activity, exercise adherence, other pain and function measures, and quality of life. Analyses were intent-to-treat with multiple imputation for missing data. Results. A total of 142 (85%), 136 (81%), and 128 (76%) participants completed 6-, 12-, and 18-month measurements, respectively. The change in NRS pain (mean difference 0.4 unit [95% confidence interval (95% CI) 20.4, 1.3]) and in WOMAC function (1.8 [95% CI 21.9, 5.5]) did not differ between groups at 6 months, with both groups showing clinically relevant improvements. Some secondary outcomes related to physical activity and exercise behavior favored PT1coaching at 6 months but generally not at 12 or 18 months. There were no between-group differences in most other outcomes. Conclusion. The addition of simultaneous telephone coaching did not augment the pain and function benefits of a physiotherapist-prescribed home-based physical activity program.

ObjectivesAlthough several rehabilitation programmes following hip arthroscopy for femoracetabular impingement (FAI) syndrome have been described, there are no clinical trials evaluating whether formal physiotherapy-prescribed rehabilitation improves recovery compared with self-directed rehabilitation. The objective of this study was to evaluate the efficacy of adding a physiotherapist-prescribed rehabilitation programme to arthroscopic surgery for FAI syndrome.DesignRandomised controlled trial.MethodsPeople aged ≥16 years with FAI syndrome scheduled for hip arthroscopy were recruited and randomly allocated to physiotherapy (PT) or control. The PT group received seven PT sessions (one preoperative and six postoperative) incorporating education, manual therapy and a progressive rehabilitation programme of home, aquatic and gym exercises while the control group did not undertake PT rehabilitation. Measurements were taken at baseline (2 weeks presurgery) and 14 and 24 weeks postsurgery. The primary outcomes were the International Hip Outcome Tool (iHOT-33) and the sport subscale of the Hip Outcome Score (HOS) at week 14.ResultsDue to slower than expected recruitment and funding constraints, recruitment was ceased after 23 months. Thirty participants (14 PT and 16 control) were randomised and 28 (14 PT and 14 control; 93%) and 22 (11 PT and 11 control; 73%) completed week 14 and 24 measurements, respectively. For the 14-week primary outcomes, the PT group showed significantly greater improvements on the iHOT-33 (mean difference 14.2 units; 95% CI 1.2 to 27.2) and sport subscale of the HOS (13.8 units; 95% CI 0.3 to 27.3). There were no significant between-group differences at week 24.ConclusionsAn individual PT treatment and rehabilitation programme may augment improvements in patient-reported outcomes following arthroscopy for FAI syndrome. However, given the small sample size, larger trials are needed to validate the findings.Trial registration numberTrial registered with the Australian New Zealand Clinical Trials Registry :ACTRN12613000282785, Results.

It has long been recognized that sample size calculations for cluster randomized trials require consideration of the correlation between multiple observations within the same cluster. When measurements are taken at anything other than a single point in time, these correlations depend not only on the cluster but also on the time separation between measurements and additionally, on whether different participants (cross-sectional designs) or the same participants (cohort designs) are repeatedly measured. This is particularly relevant in trials with multiple periods of measurement, such as the cluster cross-over and stepped-wedge designs, but also to some degree in parallel designs. Several papers describing sample size methodology for these designs have been published, but this methodology might not be accessible to all researchers. In this article we provide a tutorial on sample size calculation for cluster randomized designs with particular emphasis on designs with multiple periods of measurement and provide a web-based tool, the Shiny CRT Calculator, to allow researchers to easily conduct these sample size calculations. We consider both cross-sectional and cohort designs and allow for a variety of assumed within-cluster correlation structures. We consider cluster heterogeneity in treatment effects (for designs where treatment is crossed with cluster), as well as individually randomized group-treatment trials with differential clustering between arms, for example designs where clustering arises from interventions being delivered in groups. The calculator will compute power or precision, as a function of cluster size or number of clusters, for a wide variety of designs and correlation structures. We illustrate the methodology and the flexibility of the Shiny CRT Calculator using a range of examples.