Invited Response on: “Statistical P values have No Guiding Significance in Clinical Trials of Plastic Surgery with Small Sample Sizes”

2021 ◽  
Author(s):  
Osama A. Samargandi ◽  
Connor McGuire
Keyword(s):  
Clinical Trials ◽  
Plastic Surgery ◽  
Small Sample ◽  
Sample Sizes ◽  
P Values ◽  
Small Sample Sizes

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.

Comparison of Four Methods of Allocation for Clinical Trials with Small Sample Sizes

Physiotherapy ◽  
2002 ◽  
Vol 88 (12) ◽  
pp. 722-729 ◽  
Author(s):  
Ian Davidson ◽  
Valerie F Hillier
Keyword(s):  
Clinical Trials ◽  
Small Sample ◽  
Sample Sizes ◽  
Small Sample Sizes

scholarly journals Small Sample Sizes and a False Economy for Psychiatric Clinical Trials

2019 ◽  
Vol 76 (7) ◽  
pp. 676 ◽  
Author(s):  
Shitij Kapur ◽  
Marcus Munafò
Keyword(s):  
Clinical Trials ◽  
Small Sample ◽  
Sample Sizes ◽  
Small Sample Sizes

Small sample sizes in clinical trials: a statistician’s perspective

2012 ◽  
Vol 2 (7) ◽  
pp. 655-657 ◽  
Author(s):  
Lucinda Billingham ◽  
Kinga Malottki ◽  
Neil Steven
Keyword(s):  
Clinical Trials ◽  
Small Sample ◽  
Sample Sizes ◽  
Small Sample Sizes

Assessing the impact of restricted follow-up and small sample sizes on survival estimations in prostate cancer using registry data.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 294-294 ◽  
Author(s):  
Dhvani Shah ◽  
Victoria Paly ◽  
Andrew Briggs ◽  
Manpreet Sidhu ◽  
Esprit Ma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Trials ◽  
Small Sample ◽  
Sample Sizes ◽  
Reference Case ◽  
Term Survival ◽  
Small Sample Sizes ◽  
Log Normal

294 Background: Economic evaluations in oncology aim to assess the value of new therapies in the long term based on clinical trial data that often have restricted follow-up times (< 5 years) and small sample sizes (< 500 patients). This requires the use of extrapolation assumptions on long-term survival that go beyond the observed data. In this analysis, differences in survival extrapolation methods are tested in samples of sizes and follow-up reflecting typical clinical trials against a background of known survival in prostate cancer from a US based cancer registry. Methods: Data from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) registry on long-term survival in patients with stage IV prostate cancer were employed. The data set comprised those patients diagnosed between 1988 and 2003, with follow-up data available until 2012. Additional survival for those who received surgery (compared to those who did not), was estimated based on extrapolations using standard parametric statistical models (exponential, Weibull, log-logistic, log-normal, Gamma) fitted to the observed data. Survival analyses were run for 5 sample size scenarios (n = 27,670, 1000, 500, 200, 50) and 6 follow-up scenarios (follow-up years = 25, 20, 10, 5, 2, 1) yielding 30 combination scenarios. Performance of the methods was tested relative to the maximum follow-up, maximum sample size scenario (i.e. reference case) from the SEER registry. Results: Log-logistic and log-normal models were associated with flat tails which led to inflated survival estimations. For scenarios with smaller sizes, gamma models often did not converge. Exponential models were the most frequently reported as best model fit (in approximately 50% of scenarios). Also, gains in OS were consistent when exponential models were selected, and closely matched gain in OS from the reference case. Conclusions: Since clinical trials in oncology are often associated with small patient sample sizes and restricted follow-up, selecting an exponential model may lead to the most consistent and stable results based on the experiment constructed here. Further research should confirm these results for other types of cancer.

scholarly journals Sample Size Requirements for Calibrated Approximate Credible Intervals for Proportions in Clinical Trials

2021 ◽  
Vol 18 (2) ◽  
pp. 595
Author(s):  
Fulvio De Santis ◽  
Stefania Gubbiotti
Keyword(s):  
Clinical Trials ◽  
Posterior Probability ◽  
Interval Estimation ◽  
Small Sample ◽  
Sample Sizes ◽  
Posterior Density ◽  
Unknown Parameters ◽  
Credible Intervals ◽  
Small Sample Sizes ◽  
Highest Posterior Density

In Bayesian analysis of clinical trials data, credible intervals are widely used for inference on unknown parameters of interest, such as treatment effects or differences in treatments effects. Highest Posterior Density (HPD) sets are often used because they guarantee the shortest length. In most of standard problems, closed-form expressions for exact HPD intervals do not exist, but they are available for intervals based on the normal approximation of the posterior distribution. For small sample sizes, approximate intervals may be not calibrated in terms of posterior probability, but for increasing sample sizes their posterior probability tends to the correct credible level and they become closer and closer to exact sets. The article proposes a predictive analysis to select appropriate sample sizes needed to have approximate intervals calibrated at a pre-specified level. Examples are given for interval estimation of proportions and log-odds.

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