TCT-550 Novel Application of a Bias-Reduction Methodology for the Sample Size Estimation Design in Randomized Controlled Trials

Background: In multiple sclerosis (MS) studies, the most appropriate model for the distribution of the number of relapses was shown to be the negative binomial (NB) distribution. Objective: To determine whether the sample-size estimation (SSE) and the analysis of annualized relapse rates (ARRs) in randomized controlled trials (RCTs) were aligned and compare the SSE between normal and NB distributions. Methods: Systematic review of phase 3 and 4 RCTs for which the primary endpoint was ARR in relapsing remitting MS published since 2008 in pre-selected major medical journals. A PubMed search was performed on 30 November 2020. We checked whether the SSE and ARR analyses were congruent. We also performed standardized (fixed α/β, number of arms and overdispersion) SSEs using data collected from the studies. Results: Twenty articles (22 studies) were selected. NB distribution (or quasi-Poisson) was used for SSE in only 7/22 studies, whereas 21/22 used it for ARR analyses. SSE relying on NB regression necessitated a smaller sample size in 21/22 of our calculations. Conclusion: SSE was rarely performed using the most appropriate model. However, the use of an NB model is recommended to optimize the number of included patients and to be congruent with the final analysis.

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Statistical power, sample size, and their reporting in randomized controlled trials

Journal of Neurosurgical Anesthesiology ◽

10.1097/00008506-199501000-00018 ◽

1995 ◽

Vol 7 (1) ◽

pp. 69

Author(s):

David S. Warner

Keyword(s):

Randomized Controlled Trials ◽

Sample Size ◽

Statistical Power ◽

Controlled Trials ◽

Randomized Controlled

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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

Acta Endocrinologica ◽

10.1530/eje-14-0441 ◽

2015 ◽

Vol 172 (3) ◽

pp. R93-R101 ◽

Cited By ~ 42

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%.

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Pitfalls in Reporting Sample Size Calculation Across Randomized Controlled Trials Involving Ivermectin for the treatment of COVID-19

American Journal of Therapeutics ◽

10.1097/mjt.0000000000001441 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Chia Siang Kow ◽

Syed S. Hasan

Keyword(s):

Randomized Controlled Trials ◽

Sample Size ◽

Sample Size Calculation ◽

Controlled Trials ◽

Randomized Controlled

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A comparison of methods for sample size estimation for non-inferiority studies with binary outcomes

Statistical Methods in Medical Research ◽

10.1177/0962280210378945 ◽

2010 ◽

Vol 20 (6) ◽

pp. 595-612 ◽

Cited By ~ 16

Author(s):

Steven A Julious ◽

Roger J Owen

Keyword(s):

Sample Size ◽

Bayesian Methods ◽

Sensitivity Analyses ◽

Binary Outcomes ◽

Sample Size Estimation ◽

Controlled Trials ◽

Comparison Of Methods ◽

Size Estimation ◽

Control Response ◽

Established Treatment

Non-inferiority trials are motivated in the context of clinical research where a proven active treatment exists and placebo-controlled trials are no longer acceptable for ethical reasons. Instead, active-controlled trials are conducted where a treatment is compared to an established treatment with the objective of demonstrating that it is non-inferior to this treatment. We review and compare the methodologies for calculating sample sizes and suggest appropriate methods to use. We demonstrate how the simplest method of using the anticipated response is predominantly consistent with simulations. In the context of trials with binary outcomes with expected high proportions of positive responses, we show how the sample size is quite sensitive to assumptions about the control response. We recommend when designing such a study that sensitivity analyses be performed with respect to the underlying assumptions and that the Bayesian methods described in this article be adopted to assess sample size.

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

Healthcare ◽

10.3390/healthcare7040137 ◽

2019 ◽

Vol 7 (4) ◽

pp. 137 ◽

Cited By ~ 3

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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Revisiting the Sample Size and Statistical Power of Randomized Controlled Trials in Orthopaedics After 2 Decades

JBJS Reviews ◽

10.2106/jbjs.rvw.19.00079 ◽

2020 ◽

Vol 8 (2) ◽

pp. e0079 ◽

Cited By ~ 1

Author(s):

Aleksi Reito ◽

Lauri Raittio ◽

Olli Helminen

Keyword(s):

Randomized Controlled Trials ◽

Sample Size ◽

Statistical Power ◽

Controlled Trials ◽

Randomized Controlled

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Randomized controlled trials and neurosurgery: the ideal fit or should alternative methodologies be considered?

Journal of Neurosurgery ◽

10.3171/2014.12.jns142465 ◽

2016 ◽

Vol 124 (2) ◽

pp. 558-568 ◽

Cited By ~ 30

Author(s):

Alireza Mansouri ◽

Benjamin Cooper ◽

Samuel M. Shin ◽

Douglas Kondziolka

Keyword(s):

Randomized Controlled Trials ◽

Sample Size ◽

English Language ◽

Sample Size Calculation ◽

Median Number ◽

Controlled Trials ◽

Quality Of Reporting ◽

Randomized Controlled ◽

Mesh Terms

OBJECT Randomized-controlled trials (RCTs) are advocated to provide high-level medical evidence. However, in neurosurgery, there are barriers to conducting RCTs. The authors of this study sought to analyze the quality of neurosurgical RCTs since 2000 to determine the adequacy of their design and reporting. METHODS A search of the MEDLINE and EMBASE databases (2000–2014) was conducted. The medical subject heading (MeSH) terms used in the search included: “neurosurgery” OR “neurosurgical procedure,” “brain neoplasms,” “infarction” and “decompression,” “carotid stenosis,” “cerebral hemorrhage,” and “spinal fusion.” These studies were limited to RCTs, in humans, and in the English language. The Consolidated Standards for Reporting of Trials (CONSORT) and Jadad scales were used to assess the quality of RCT design and reporting. The standardized median times cited (median citations divided by years since publication) were used to assess impact. A pragmatic-explanatory continuum indicator summary-based scale was used to assess the design of the studies as primarily pragmatic or explanatory. RESULTS Sixty-one articles were identified, and the following subspecialties were the most common: vascular (23, 37%), followed by functional neurosurgery and neurooncology (both 13, 21%). The following nations were the primary leaders in RCTs: US (25 studies, 41%), Germany (8 studies, 13%), and the United Kingdom (7 studies, 11%). Median sample size was 100 (interquartile range [IQR] 41.5–279). The majority of the studies (40, 66%) had pragmatic objectives. The median number of times cited overall was 69 (IQR 20.5–193). The combined median CONSORT score was 36 (IQR 27.5–39). Blinding was most deficiently reported. Other areas with a relatively low quality of reporting were sample size calculation (34.2% of surgical, 38.5% of drug, and 20% of device studies), allocation concealment (28.9% of surgical, 23.1% of drug, and 50% of device studies), and protocol implementation (18.4% of surgical, 23% of drug, and 20% of device studies). The quality of reporting did not correlate with the study impact. All studies had a median Jadad score ≤ 3. Thirty-three pragmatic studies (83%) and 5 explanatory studies (25%) met the design objectives. All pragmatic studies based on drug and device trials met their objectives, while 74% of pragmatic surgical trials met their objectives. CONCLUSIONS The prevalence of neurosurgical RCTs is low. The quality of RCT design and reporting in neurosurgery is also low. Many study designs are not compatible with stated objectives. Pragmatic studies were more likely to meet design objectives. Given the role of RCTs as one of the highest levels of evidence, it is critical to improve on their methodology and reporting.

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