scholarly journals What Could We Confirm in Confirmatory Clinical Trials? —From the View Point of Statistical Power and Sample Size—

2008 ◽  
Vol 29 (Special_Issue_1) ◽  
pp. S99-S105
Author(s):  
Toshihiko Morikawa
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Statistical Power ◽  
View Point ◽  
Confirmatory Clinical Trials

Unequal Randomisation Can Improve the Economic Efficiency of Clinical Trials

1997 ◽  
Vol 2 (2) ◽  
pp. 81-85 ◽  
Author(s):  
David Torgerson ◽  
Marion Campbell
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Statistical Power ◽  
Cost Effective ◽  
Total Sample ◽  
Cost Ratio ◽  
Treatment Groups ◽  
Total Sample Size ◽  
Effectiveness Analysis ◽  
Research Resources

Objectives: In the majority of clinical trials patients are randomised equally between treatment groups. This approach maximises statistical power for a given total sample size. The objectives of this paper were to determine if, when research costs between treatments differ, it is more economically efficient to randomise additional patients to the cheaper treatment, and how the optimum randomisation ratio can be estimated. Methods: Estimation of the most economically efficient randomisation ratio for four hypothetical clinical trials using cost-effectiveness analysis. Results: When research costs differ between treatments, and there is no constraint on total sample size, it is always more cost-effective to randomise more patients to the cheaper treatment. For example, a cost ratio between the lesser and more expensive treatment of ten, results in a randomisation ratio of 3.2:1. Conclusions: Unequal randomisation ratios should be more widely used as this will achieve optimum statistical power for the lowest expenditure of research resources.

Statistical power of negative randomized clinical trials (RCTs) presented at the American Society of Clinical Oncology (ASCO) Annual Meetings

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6516-6516
Author(s):  
P. Bedard ◽  
M. K. Krzyzanowska ◽  
M. Pintilie ◽  
I. F. Tannock
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Annual Meeting ◽  
Primary Endpoint ◽  
Effect Size ◽  
Statistical Power ◽  
Randomized Clinical Trials ◽  
Medium Effect ◽  
Post Hoc ◽  
Adequate Sample Size

6516 Background: Underpowered randomized clinical trials (RCTs) may expose participants to risks and burdens of research without scientific merit. We investigated the prevalence of underpowered RCTs presented at ASCO annual meetings. Methods: We surveyed all two-arm parallel phase III RCTs presented at the ASCO annual meeting from 1995–2003 where differences for the primary endpoint were non-statistically significant. Post hoc calculations were performed using a power of 80% and a=0.05 (two-sided) to determine the sample size required to detect a small, medium, and large effect size between the two groups. For studies reporting a proportion or time to event as a primary endpoint, effect size was expressed as an odds ratio (OR) or hazard ratio (HR) respectively, with a small effect size defined as OR/HR=1.3, medium effect size OR/HR=1.5, and large effect OR/HR=2.0. Logistic regression was used to identify factors associated with lack of statistical power. Results: Of 423 negative RCTs for which post hoc sample size calculations could be performed, 45 (10.6%), 138 (32.6%), and 333 (78.7%) had adequate sample size to detect small, medium, and large effect sizes respectively. Only 35 negative RCTs (7.1%) reported a reason for inadequate sample size. In a multivariable model, studies presented at plenary or oral sessions (p<0.0001) and multicenter studies supported by a co-operative group were more likely to have adequate sample size (p<0.0001). Conclusion: Two-thirds of negative RCTs presented at the ASCO annual meeting do not have an adequate sample to detect a medium-sized treatment effect. Most underpowered negative RCTs do not report a sample size calculation or reasons for inadequate patient accrual. No significant financial relationships to disclose.

scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research

10.2196/26718 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e26718
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research (Preprint)

2020 ◽  
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

UNSTRUCTURED This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

The Sumatriptan/Naratriptan Aggregated Patient (SNAP) Database: Aggregation, Validation and Application

Cephalalgia ◽  
2004 ◽  
Vol 24 (7) ◽  
pp. 586-595 ◽  
Author(s):  
C Barrows ◽  
W Saunders ◽  
R Austin ◽  
G Putnam ◽  
H Mansbach ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Pharmaceutical Company ◽  
Statistical Power ◽  
Individual Patient Data ◽  
Patient Data ◽  
Small Sample ◽  
Medical Decision ◽  
Large Sample Size

Pooled data from multiple clinical trials can provide information for medical decision-making that typically cannot be derived from a single clinical trial. By increasing the sample size beyond that achievable in a single clinical trial, pooling individual-patient data from multiple trials provides additional statistical power to detect possible effects of study medication, confers the ability to detect rare outcomes, and facilitates evaluation of effects among subsets of patients. Data from pharmaceutical company-sponsored clinical trials lend themselves to data-pooling, meta-analysis, and data mining initiatives. Pharmaceutical company-sponsored clinical trials are arguably among the most rigorously designed and conducted of studies involving human subjects as a result of multidisciplinary collaboration involving clinical, academic and/or governmental investigators as well as the input and review of medical institutional bodies and regulatory authorities. This paper describes the aggregation, validation and initial analysis of data from the sumatriptan/naratriptan aggregate patient (SNAP) database, which to date comprises pooled individual-patient data from 128 clinical trials conducted from 1987 to 1998 with the migraine medications sumatriptan and naratriptan. With an extremely large sample size (>28000 migraineurs, >140000 treated migraine attacks), the SNAP database allows exploration of questions about migraine and the efficacy and safety of migraine medications that cannot be answered in single clinical trials enrolling smaller numbers of patients. Besides providing the adequate sample size to address specific questions, the SNAP database allows for subgroup analyses that are not possible in individual trial analyses due to small sample size. The SNAP database exemplifies how the wealth of data from pharmaceutical company-sponsored clinical trials can be re-used to continue to provide benefit.

Sample Size Re-estimation Designs In Confirmatory Clinical Trials—Current State, Statistical Considerations, and Practical Guidance

2015 ◽  
Vol 7 (4) ◽  
pp. 309-321 ◽  
Author(s):  
Yili L. Pritchett ◽  
Sandeep Menon ◽  
Olga Marchenko ◽  
Zoran Antonijevic ◽  
Eva Miller ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Current State ◽  
Practical Guidance ◽  
Confirmatory Clinical Trials

scholarly journals The race to find a SARS-CoV-2 drug can only be won by a few chosen drugs: a systematic review of registers of clinical trials of drugs aimed at preventing or treating COVID-19

2020 ◽  
Author(s):  
Vicente Martinez-Vizcaino ◽  
Arthur E. Mesas ◽  
Iván Cavero-Redondo ◽  
Alicia Saz-Lara ◽  
Irene Sequí-Dominguez ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Sample Size ◽  
Statistical Power ◽  
Pharmacological Therapy ◽  
Pharmacological Treatments ◽  
Huge Amount ◽  
Clinical Trials Registry ◽  
Respiratory Recovery ◽  
Effective Drugs

ABSTRACTConsidering the massive amount of clinical trial registers aimed to find effective drugs for the prevention and treatment of COVID-19, it is challenging to have a comprehensive view of which drugs are being studied more extensively and when is expected that we will have consistent results regarding their effectiveness. This systematic review included all clinical trials on pharmacological therapy related to COVID-19 and SARS-CoV-2 registered at the International Clinical Trials Registry Platform (WHO-ICTRP) up to April 22, 2020. Clinical trials characteristics (country, design, sample size, main outcomes, expected completion data, type of participants, length of the interventions, main outcomes). How many trials and he accumulated sample size by drug or combination of drugs, and by month in 2020 was depicted. We identified 412 clinical trials registers addressing the effect of pharmacological treatments on COVID-19, predominantly from Asia and Europe (42.2% and 31.1% of clinical trials registers, respectively). The most main outcomes studied were clinical recovery (54.4% of the clinical trials registers, respiratory recovery (28.2%) mortality (27.4%), viral load/negativity (20.4%). During 2020, a huge amount of clinical trials are expected to be completed: 41 trials (60,366 participants) using hydroxychloroquine, 20 trials (1,588 participants) using convalescent’s plasma, 18 trials (6,830 participants) using chloroquine, 12 trials (9,938 participants using lopinavir/ritonavir, 11 trials (1,250 participants) using favipiravir, 10 trials ( 2,175 participants) using tocilizumab and 6 trials (13,540 participants) using Remdesivir. The distribution of the number of registered clinical trials among the different therapeutic options leads to an excess of sample size for some and a lack for others. Our data allow us to conclude that by the end of June we will have results of almost 20 trials involving 40000 patients for hydroxychloroquine and 5 trials with 4500 patients for remdesivir; however, low statistical power is expected from the 9 clinical trials testing the efficacy of favipiravir or the 5 testing tocilizumab, since they will recruit less than 1000 patients each one.

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