ON SAMPLE SIZE CALCULATION BASED ON ODDS RATIO IN CLINICAL TRIALS

If a trial is to be well designed, and the conclusions drawn from it valid, a thorough understanding of the benefits and pitfalls of basic statistical principles is required. When setting up a trial, appropriate sample-size calculation is vital. If initial calculations are inaccurate, trial results will be unreliable. The principle of intent-to-treat in comparative trials is examined. Randomization as a method of selecting patients to treatment is essential to ensure that the treatment groups are equalized in terms of avoiding biased allocation in the mix of patients within groups. Once trial results are available the correct calculation and interpretation of the P-value is important. Its limitations are examined, and the use of the confidence interval to help draw valid conclusions regarding the clinical value of treatments is explored.

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Approaches to sample size calculation for clinical trials in rare diseases

Pharmaceutical Statistics ◽

10.1002/pst.1848 ◽

2018 ◽

Vol 17 (3) ◽

pp. 214-230 ◽

Cited By ~ 7

Author(s):

Frank Miller ◽

Sarah Zohar ◽

Nigel Stallard ◽

Jason Madan ◽

Martin Posch ◽

...

Keyword(s):

Clinical Trials ◽

Sample Size ◽

Rare Diseases ◽

Sample Size Calculation

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Design and analysis of a clinical trial using previous trials as historical control

Clinical Trials ◽

10.1177/1740774519858914 ◽

2019 ◽

Vol 16 (5) ◽

pp. 531-538 ◽

Cited By ~ 6

Author(s):

David Alan Schoenfeld ◽

Dianne M Finkelstein ◽

Eric Macklin ◽

Neta Zach ◽

David L Ennist ◽

...

Keyword(s):

Clinical Trials ◽

Amyotrophic Lateral Sclerosis ◽

Sample Size ◽

Standard Error ◽

Controlled Trial ◽

Sample Size Calculation ◽

Control Group ◽

Controlled Trials ◽

Number Of Patients ◽

Lateral Sclerosis

Background/AimsFor single arm trials, a treatment is evaluated by comparing an outcome estimate to historically reported outcome estimates. Such a historically controlled trial is often analyzed as if the estimates from previous trials were known without variation and there is no trial-to-trial variation in their estimands. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers these sources of variation.MethodsWe fit a Bayesian hierarchical model, providing a sample from the posterior predictive distribution of the outcome estimand of a new trial, which, along with the standard error of the estimate, can be used to calculate the probability that the estimate exceeds a threshold. We then calculate criteria for statistical significance as a function of the standard error of the new trial and calculate sample size as a function of difference to be detected. We apply these methods to clinical trials for amyotrophic lateral sclerosis using data from the placebo groups of 16 trials.ResultsWe find that when attempting to detect the small to moderate effect sizes usually assumed in amyotrophic lateral sclerosis clinical trials, historically controlled trials would require a greater total number of patients than concurrently controlled trials, and only when an effect size is extraordinarily large is a historically controlled trial a reasonable alternative. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial.ConclusionThis article quantifies when historically controlled trials would not provide any sample size advantage, despite dispensing with a control group.

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Methods, transparency and reporting of clinical trials in orthodontics and periodontics

Journal of Orthodontics ◽

10.1177/1465312519842315 ◽

2019 ◽

Vol 46 (2) ◽

pp. 101-109 ◽

Cited By ~ 8

Author(s):

Spyridon N Papageorgiou ◽

Georgios N Antonoglou ◽

Conchita Martin ◽

Theodore Eliades

Keyword(s):

Clinical Trials ◽

Odds Ratio ◽

Prospective Trial ◽

Sample Size Calculation ◽

Open Data ◽

Trial Registration ◽

Cross Sectional ◽

Key Aspects ◽

Adequate Sample Size ◽

Bibliographic Study

Objective: The aim of this study was to explore the methods, reporting and transparency of clinical trials in orthodontics and compare them to the field of periodontics, as a standard within dentistry. Design/setting: Cross-sectional bibliographic study Methods: A total of 300 trials published in 2017–2018 and evenly distributed in orthodontics and periodontics were selected, assessed and analysed statistically to explore key aspects of the conduct and reporting of orthodontic clinical trials compared to trials in periodontics. Results: Several aspects are often neglected in orthodontic and periodontic trials and could be improved upon, including use of statistical expertise (22.3% of assessed trials), blinding of outcome assessors (62.3%), prospective trial registration (12.0%), adequate sample size calculation (35.7%), adherence to CONSORT (14.3%) and open data sharing (4.3%). The prevalence of statistically significant findings among orthodontic and periodontic trials was 62.3%, which was significantly associated with several methodological traits like statistician involvement (odds ratio [OR] = 0.5; 95% confidence interval [CI] = 0.3–0.9), blind outcome assessor (OR = 0.5; 95% CI = 0.2–1.0), lack of prospective trial registration (OR = 2.8; 95% CI = 1.3–5.9) and non-adherence to CONSORT (OR = 4.5; 95% CI = 1.3–15.8). Conclusions: Although trials in orthodontics seem to be significantly worse compared to periodontics in aspects like trial registration, adherence to CONSORT and declaration of competing interests or financial support, their methods do seem to have improved considerably in recent years.

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Statistical basis of clinical trials

10.1093/med/9780199609956.003.0009 ◽

2014 ◽

Author(s):

Jeremy Prout ◽

Tanya Jones ◽

Daniel Martin

Keyword(s):

Clinical Trials ◽

Statistical Analysis ◽

Observational Study ◽

Odds Ratio ◽

Binary Data ◽

Statistical Significance ◽

Sample Size Calculation ◽

Number Needed To Treat ◽

Drug Trials ◽

Research Types

This chapter summarizes some aspects of study design and statistical analysis to allow the anaesthetist to appraise research. Types of observational study are described and aspects of interventional studies such as sample size calculation and power are explained. Research governance, phases of drug trials and levels of evidence are described. A section on statistical analysis includes expression of proportion for binary data (odds ratio, number needed to treat) and use of probability and confidence intervals to measure statistical significance.

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