scholarly journals Approaches to sample size calculation for clinical trials in rare diseases

2018 ◽  
Vol 17 (3) ◽  
pp. 214-230 ◽  
Author(s):  
Frank Miller ◽  
Sarah Zohar ◽  
Nigel Stallard ◽  
Jason Madan ◽  
Martin Posch ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Rare Diseases ◽  
Sample Size Calculation

Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events

1994 ◽  
Vol 13 (8) ◽  
pp. 859-870 ◽  
Author(s):  
Robert P. McMahon ◽  
Michael Proschan ◽  
Nancy L. Geller ◽  
Peter H. Stone ◽  
George Sopko
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Calculation

Statistical Principles for Clinical Trials

1998 ◽  
Vol 26 (2) ◽  
pp. 57-65 ◽  
Author(s):  
R Kay
Keyword(s):  
Clinical Trials ◽  
Confidence Interval ◽  
Sample Size ◽  
Sample Size Calculation ◽  
P Value ◽  
Clinical Value ◽  
Treatment Groups ◽  
Correct Calculation ◽  
Intent To Treat ◽  
Comparative 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.

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

2019 ◽  
Vol 16 (5) ◽  
pp. 531-538 ◽  
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.

Sample size calculation of clinical trials in geriatric medicine

2020 ◽  
Author(s):  
Graziella D’Arrigo ◽  
Stefanos Roumeliotis ◽  
Claudia Torino ◽  
Giovanni Tripepi
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Geriatric Medicine ◽  
Sample Size Calculation

Proposals for Sample Size Calculation Programs

2007 ◽  
Vol 46 (06) ◽  
pp. 655-661 ◽  
Author(s):  
H. Heinzl ◽  
A. Benner ◽  
C. Ittrich ◽  
M. Mittlböck
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Effect Sizes ◽  
Detectable Effect ◽  
Planning Stage ◽  
Type Iii ◽  
Error Risk ◽  
Q Values

Summary Objectives : Numerous sample size calculation programs are available nowadays. They include both commercial products as well as public domain and open source applications. We propose modifications for these programs in order to even better support statistical consultation during the planning stage of a two-armed clinical trial. Methods : Directional two-sided tests are commonly used for two-armed clinical trials. This may lead to a non-negligible Type III error risk in a severely underpowered study. In the case of a reasonably sized study the question for the so-called auxiliary alternative may evolve. Results : We propose that sample size calculation programs should be able to compute i) Type III errors and the so-called (q-values, ii) minimum sample sizes required to keep the (q-values below pre-specified levels, and iii) detectable effect sizes of the so-called auxiliary alternatives. Conclusions : Proposals iand ii are intended to help prevent irresponsibly underpowered clinical trials, whereas the proposal iii is meant as additional assistance for the planning of reasonably sized clinical trials.

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