scholarly journals Assessing accuracy of Weibull shape parameter estimate from historical studies for subsequent sample size calculation in clinical trials with time-to-event outcome

2020 ◽  
Vol 17 ◽  
pp. 100548
Author(s):  
Milind A. Phadnis ◽  
Palash Sharma ◽  
Nadeesha Thewarapperuma ◽  
Prabhakar Chalise
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Shape Parameter ◽  
Sample Size Calculation ◽  
Parameter Estimate ◽  
Time To Event ◽  
Subsequent Sample

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 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

Control arm heterogeneity in trials for unselected advanced triple-negative breast cancer (aTNBC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1082-1082
Author(s):  
Kinisha Gala ◽  
Ankit Kalucha ◽  
Samuel Martinet ◽  
Anushri Goel ◽  
Kalpana Devi Narisetty ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Single Agent ◽  
Progression Free Survival ◽  
Sample Sizes ◽  
Time To Event ◽  
Sample Size Calculations ◽  
Subgroup Analyses ◽  
Combination Regimens

1082 Background: Primary endpoints of clinical trials frequently include subgroup-analyses. Several solid cancers such as aTNBC are heterogeneous, which can lead to unpredictable control arm performance impairing accurate assumptions for sample size calculations. We explore the value of a comprehensive clinical trial results repository in assessing control arm heterogeneity with aTNBC as the pilot. Methods: We identified P2/3 trials reporting median overall survival (mOS) and/or median progression-free survival (mPFS) in unselected aTNBC through a systematic search of PubMed, clinical trials databases and conference proceedings. Trial arms with sample sizes ≤25 or evaluating drugs no longer in development were excluded. Due to inconsistency among PD-L1 assays, PD-L1 subgroup analyses were not assessed separately. The primary aim was a descriptive analysis of control arm mOS and mPFS across all randomized trials in first line (1L) aTNBC. Secondary aims were to investigate time-to-event outcomes in control arms in later lines and to assess time-trends in aTNBC experimental and control arm outcomes. Results: We included 33 trials published between June 2013-Feb 2021. The mOS of control arms in 1L was 18.7mo (range 12.6-22.8) across 5 trials with single agent (nab-) paclitaxel [(n)P], and 18.1mo (similar range) for 7 trials including combination regimens (Table). The mPFS of control arms in 1L was 4.9mo (range 3.8-5.6) across 5 trials with single-agent (n)P, and 5.6mo (range 3.8-6.1) across 8 trials including combination regimens. Control arm mOS was 13.1mo (range 9.4-17.4) for 3 trials in first and second line (1/2L) and 8.7mo (range 6.7-10.8) across 5 trials in 2L and beyond. R2 for the mOS best-fit lines across control and experimental arms over time was 0.09, 0.01 and 0.04 for 1L, 1/2L and 2L and beyond, respectively. Conclusions: Median time-to-event outcomes of control arms in 1L aTNBC show considerable heterogeneity, even among trials with comparable regimens and large sample sizes. Disregarding important prognostic factors at stratification can lead to imbalances between arms, which may jeopardize accurate sample size calculations, trial results and interpretation. Optimizing stratification and assumptions for power calculations is of utmost importance in aTNBC and beyond. A digitized trial results repository with precisely defined patient populations and treatment settings could improve accuracy of assumptions during clinical trial design.[Table: see text]

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.

scholarly journals Power and Sample-Size Analysis for the Royston–Parmar Combined Test in Clinical Trials with a Time-to-Event Outcome: Correction and Program Update

2018 ◽  
Vol 18 (4) ◽  
pp. 995-996 ◽  
Author(s):  
Patrick Royston
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Estimation Procedure ◽  
Worked Examples ◽  
Ordinary Least Squares ◽  
Size Analysis ◽  
Size Estimation ◽  
Probit Regression ◽  
Time To Event ◽  
Combined Test

The changes made to Royston (2018) and to power_ct are i) in section 2.4 ( Sample-size calculation for the combined test), to replace ordinary least-squares (OLS) regression using regress with grouped probit regression using glm; ii) in section 4 ( Examples), to revisit the worked examples of sample-size estimation in light of the revised estimation procedure; and iii) to update the help file entry for the option n( numlist). The updated software is version 1.2.0.

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