scholarly journals Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control

2015 ◽  
Vol 34 (12) ◽  
pp. 2048-2061 ◽  
Author(s):  
John Whitehead ◽  
Faye Cleary ◽  
Amanda Turner
Keyword(s):  
Clinical Trials ◽  
Sample Sizes ◽  
Experimental Treatments

scholarly journals Sample sizes in dosage investigational clinical trials: a systematic evaluation

2015 ◽  
pp. 305 ◽  
Author(s):  
Jihan Huang ◽  
Qianmin Su ◽  
juan yang ◽  
yinghua lv ◽  
yingchun he ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Systematic Evaluation ◽  
Sample Sizes

Time to first relapse as an endpoint in multiple sclerosis clinical trials

2012 ◽  
Vol 19 (4) ◽  
pp. 466-474 ◽  
Author(s):  
MP Sormani ◽  
A Signori ◽  
P Siri ◽  
N De Stefano
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Primary Outcome ◽  
Model Fitting ◽  
Clinical Trial Design ◽  
Fixed Time ◽  
Sound Design ◽  
Sample Sizes ◽  
Design Strategies ◽  
First Relapse

Background: The increasing number of effective therapies to treat multiple sclerosis (MS) raises ethical concerns for the use of placebo in clinical trials, suggesting that new clinical trial design strategies are needed. Objectives: To evaluate time to first relapse as an endpoint for MS clinical trials. Methods: A recently-developed model fitting the distribution of time to first relapse in MS was used for simulations estimating the sample sizes of trials using this as an outcome, and for comparison with the size of trials using the annualized relapse rate (ARR) as the primary outcome. Results: Trials based on time to first relapse were feasible, requiring sample sizes that were similar or even smaller than if the study was based on ARR instead. In the case of low ARR (0.4 relapses/year), as is expected in future trials, the 1-year trials designed to detect a treatment effect of 30%, with 90% power, require fewer patients when based on time to first relapse (470 patients/arm) than if based on ARR (540 patients/arm). Conclusions: Our simulations show that time to first relapse is not less powerful than ARR in MS trials; thus, this measure would be a potentially useful primary outcome offering the advantage of an ethically sound design, as the patients randomized to placebo can then switch to the active drug, once they relapse. A potential drawback is the loss of information for other endpoints collected at fixed time points.

Sample Sizes for Clinical Trials by JULIOUS, S. A.

Biometrics ◽  
2012 ◽  
Vol 68 (1) ◽  
pp. 327-328
Author(s):  
Janet T. Wittes
Keyword(s):  
Clinical Trials ◽  
Sample Sizes

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]

A Two-Stage Design for Choosing among Several Experimental Treatments and a Control in Clinical Trials

Biometrics ◽  
1989 ◽  
Vol 45 (2) ◽  
pp. 537 ◽  
Author(s):  
Peter F. Thall ◽  
Richard Simon ◽  
Susan S. Ellenberg
Keyword(s):  
Clinical Trials ◽  
Two Stage ◽  
Stage Design ◽  
Two Stage Design ◽  
Experimental Treatments

scholarly journals Tool for filtering PubMed search results by sample size

2018 ◽  
Vol 25 (7) ◽  
pp. 774-779
Author(s):  
Carlos Baladrón ◽  
Alejandro Santos-Lozano ◽  
Javier M Aguiar ◽  
Alejandro Lucia ◽  
Juan Martín-Hernández
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Search Engine ◽  
Sample Sizes ◽  
Search Results ◽  
Estimate Sample Size ◽  
Search Tool ◽  
Pubmed Search ◽  
Meta Analyses ◽  
Validation Tests

Abstract Objective The most used search engine for scientific literature, PubMed, provides tools to filter results by several fields. When searching for reports on clinical trials, sample size can be among the most important factors to consider. However, PubMed does not currently provide any means of filtering search results by sample size. Such a filtering tool would be useful in a variety of situations, including meta-analyses or state-of-the-art analyses to support experimental therapies. In this work, a tool was developed to filter articles identified by PubMed based on their reported sample sizes. Materials and Methods A search engine was designed to send queries to PubMed, retrieve results, and compute estimates of reported sample sizes using a combination of syntactical and machine learning methods. The sample size search tool is publicly available for download at http://ihealth.uemc.es. Its accuracy was assessed against a manually annotated database of 750 random clinical trials returned by PubMed. Results Validation tests show that the sample size search tool is able to accurately (1) estimate sample size for 70% of abstracts and (2) classify 85% of abstracts into sample size quartiles. Conclusions The proposed tool was validated as useful for advanced PubMed searches of clinical trials when the user is interested in identifying trials of a given sample size.

Independence Estimating Equations for Controlled Clinical Trials with Small Sample Sizes

2006 ◽  
Vol 45 (04) ◽  
pp. 430-434 ◽  
Author(s):  
G. Dahmen ◽  
A. Ziegler
Keyword(s):  
Clinical Trials ◽  
Estimating Equations ◽  
Small Sample ◽  
Repeated Measurements ◽  
Robust Estimator ◽  
Variance Estimator ◽  
Sample Sizes ◽  
Controlled Clinical Trials ◽  
Parallel Group ◽  
Small Sample Sizes

Summary Objectives: The application of independence estimating equations (IEE) for controlled clinical trials (CCTs) has recently been discussed, and recommendations for its use have been derived for testing hypotheses. The robust estimator of variance has been shown to be liberal for small sample sizes. Therefore a series of modifications has been proposed. In this paper we systematically compare confidence intervals (CIs) proposed in the literature for situations that are common in CCTs. Methods: Using Monte-Carlo simulation studies, we compared the coverage probabilities of CIs and non-convergence probabilities for the parameters of the mean structure for small samples using modifications of the variance estimator proposed by Mancl and de Rouen [7], Morel et al. [8] and Pan [3]. Results: None of the proposed modifications behave well in each investigated situation. For parallel group designs with repeated measurements and binary response the method proposed by Pan maintains the nominal level. We observed non-convergence of the IEE algorithm in up to 10% of the replicates depending on response probabilities in the treatment groups. For comparing slopes with continuous responses, the approach of Morel et al. can be recommended. Conclusions: Results of non-convergence probabilities show that IEE should not be used in parallel group designs with binary endpoints and response probabilities close to 0 or 1. Modifications of the robust variance estimator should be used for sample sizes up to 100 clusters for CI estimation.

scholarly journals Antibacterial-Integrated Collagen Wound Dressing for Diabetes-Related Foot Ulcers: An Evidence-Based Review of Clinical Studies

Polymers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2168
Author(s):  
Ibrahim N. Amirrah ◽  
Mohd Farhanulhakim Mohd Razip Wee ◽  
Yasuhiko Tabata ◽  
Ruszymah Bt Hj Idrus ◽  
Abid Nordin ◽  
...  
Keyword(s):  
Wound Healing ◽  
Clinical Trials ◽  
Clinical Studies ◽  
Wound Dressing ◽  
Chronic Wounds ◽  
Gentian Violet ◽  
Antibacterial Properties ◽  
Small Sample ◽  
Wound Dressings ◽  
Sample Sizes

Diabetic foot ulcer (DFU) is a chronic wound frequently delayed from severe infection. Wound dressing provides an essential barrier between the ulcer and the external environment. This review aimed to analyse the effectiveness of antibacterial collagen-based dressing for DFU treatment in a clinical setting. An electronic search in four databases, namely, Scopus, PubMed, Ovid MEDLINE(R), and ISI Web of Science, was performed to obtain relevant articles published within the last ten years. The published studies were included if they reported evidence of (1) collagen-based antibacterial dressing or (2) wound healing for diabetic ulcers, and (3) were written in English. Both randomised and non-randomised clinical trials were included. The search for relevant clinical studies (n) identified eight related references discussing the effectiveness of collagen-based antibacterial wound dressings for DFU comprising collagen impregnated with polyhexamethylene biguanide (n = 2), gentamicin (n = 3), combined-cellulose and silver (n = 1), gentian violet/methylene blue mixed (n = 1), and silver (n = 1). The clinical data were limited by small sample sizes and multiple aetiologies of chronic wounds. The evidence was not robust enough for a conclusive statement, although most of the studies reported positive outcomes for the use of collagen dressings loaded with antibacterial properties for DFU wound healing. This study emphasises the importance of having standardised clinical trials, larger sample sizes, and accurate reporting for reliable statistical evidence confirming DFU treatment efficiency.

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