Statistical Power, Sample Size, and Their Reporting in Randomized Controlled Trials

JAMA ◽  
1994 ◽  
Vol 272 (2) ◽  
pp. 122 ◽  
Author(s):  
David Moher
Keyword(s):  
Randomized Controlled Trials ◽  
Sample Size ◽  
Statistical Power ◽  
Controlled Trials ◽  
Randomized Controlled

scholarly journals MANAGEMENT OF ENDOCRINE DISEASE: Effects of telecare intervention on glycemic control in type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials

2015 ◽  
Vol 172 (3) ◽  
pp. R93-R101 ◽  
Author(s):  
Zhenru Huang ◽  
Hong Tao ◽  
Qingdong Meng ◽  
Long Jing
Keyword(s):  
Type 2 Diabetes ◽  
Glycemic Control ◽  
Randomized Controlled Trials ◽  
Sample Size ◽  
Plasma Glucose ◽  
Small Sample ◽  
Cochrane Library ◽  
Controlled Trials ◽  
Randomized Controlled

ObjectiveTo review the published literature on the effects of telecare intervention in patients with type 2 diabetes and inadequate glycemic control.Design and methodsA review of randomized controlled trials on telecare intervention in patients with type 2 diabetes, and a search of electronic databases such as The Cochrane Library, PubMed, EBSCO, CINAHL, Science Direct, Journal of Telemedicine and Telecare, and China National Knowledge Infrastructure (CNKI), were conducted from December 8 to 16, 2013. Two evaluators independently selected and reviewed the eligible studies. Changes in HbA1c, fasting plasma glucose (FPG), post-prandial plasma glucose (PPG), BMI, and body weight were analyzed.ResultsAn analysis of 18 studies with 3798 subjects revealed that telecare significantly improved the management of diabetes. Mean HbA1c values were reduced by −0.54 (95% CI, −0.75 to −0.34; P<0.05), mean FPG levels by −9.00 mg/dl (95% CI, −17.36 to −0.64; P=0.03), and mean PPG levels reduced by −52.86 mg/dl (95% CI, −77.13 to −28.58; P<0.05) when compared with the group receiving standard care. Meta-regression and subgroup analyses indicated that study location, sample size, and treatment-monitoring techniques were the sources of heterogeneity.ConclusionsPatients monitored by telecare showed significant improvement in glycemic control in type 2 diabetes when compared with those monitored by routine follow-up. Significant reduction in HbA1c levels was associated with Asian populations, small sample size, and telecare, and with those patients with baseline HbA1c greater than 8.0%.

scholarly journals Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies

Healthcare ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 137 ◽  
Author(s):  
J. Blackston ◽  
Andrew Chapple ◽  
James McGree ◽  
Suzanne McDonald ◽  
Jane Nikles
Keyword(s):  
Randomized Controlled Trials ◽  
Sample Size ◽  
Selection Bias ◽  
Clinical Care ◽  
Controlled Trials ◽  
Type I ◽  
Operating Characteristics ◽  
Crossover Designs ◽  
Carryover Effects ◽  
Randomized Controlled

Background: N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with 3 cycles, which compare aggregated N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. Results: Aggregated N-of-1 trials outperformed both traditional parallel RCT and crossover designs when these trial designs were simulated in terms of power and required sample size to obtain a given power. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs.

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