Clinical Trials are Often False Positive: A Review of Simple Methods to Control This Problem

2006 ◽  
Vol 1 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Ton Cleophas ◽  
Aeilko Zwinderman
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Don van Ravenzwaaij ◽  
John P. A. Ioannidis

Abstract Background Until recently a typical rule that has often been used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive rates (endorsement of an ineffective treatment). Methods In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, thresholds for what constitutes a clinically meaningful treatment effect, and number of trials conducted. Results Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when the number of trials conducted is high and when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large. Conclusions Thinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.


2019 ◽  
Author(s):  
Don van Ravenzwaaij ◽  
John P A Ioannidis

Abstract Background: Until recently a typical rule that has often been used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive rates (endorsement of an ineffective treatment). Methods: In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, thresholds for what constitutes a clinically meaningful treatment effect, and number of trials conducted. Results: Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when the number of trials conducted is high and when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large. Conclusions: Thinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.


Author(s):  
Ton J. Cleophas ◽  
Aeilko H. Zwinderman ◽  
Toine F. Cleophas

2010 ◽  
Vol 28 (11) ◽  
pp. 1936-1941 ◽  
Author(s):  
Hui Tang ◽  
Nathan R. Foster ◽  
Axel Grothey ◽  
Stephen M. Ansell ◽  
Richard M. Goldberg ◽  
...  

PurposeTo improve the understanding of the appropriate design of phase II oncology clinical trials, we compared error rates in single-arm, historically controlled and randomized, concurrently controlled designs.Patients and MethodsWe simulated error rates of both designs separately from individual patient data from a large colorectal cancer phase III trials and statistical models, which take into account random and systematic variation in historical control data.ResultsIn single-arm trials, false-positive error rates (type I error) were 2 to 4 times those projected when modest drift or patient selection effects (eg, 5% absolute shift in control response rate) were included in statistical models. The power of single-arm designs simulated using actual data was highly sensitive to the fraction of patients from treatment centers with high versus low patient volumes, the presence of patient selection effects or temporal drift in response rates, and random small-sample variation in historical controls. Increasing sample size did not correct the over optimism of single-arm studies. Randomized two-arm design conformed to planned error rates.ConclusionVariability in historical control success rates, outcome drifts in patient populations over time, and/or patient selection effects can result in inaccurate false-positive and false-negative error rates in single-arm designs, but leave performance of the randomized two-arm design largely unaffected at the cost of 2 to 4 times the sample size compared with single-arm designs. Given a large enough patient pool, the randomized phase II designs provide a more accurate decision for screening agents before phase III testing.


2019 ◽  
Author(s):  
Don van Ravenzwaaij ◽  
John P A Ioannidis

Abstract Background: Until recently a typical rule that has often been often used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive (endorsement of an ineffective treatment) rates. Methods: In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p -values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, and thresholds for what constitutes a clinically meaningful treatment effect. Results: Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large. Conclusions: Thinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.


2007 ◽  
Vol 28 (7) ◽  
pp. 892-895 ◽  
Author(s):  
Anurag Malani ◽  
Kim Trimble ◽  
Vikas Parekh ◽  
Carol Chenoweth ◽  
Samuel Kaufman ◽  
...  

False-positive blood culture results may lead to prolonged hospitalization, inappropriate antibiotic administration, and increased healthcare costs. We conducted a review of the literature to assess the effect of skin antiseptic agents on the rate of false-positive blood culture Results. We found no clear evidence to suggest which antiseptic should be used to prevent false-positive Results. Studies suggest, however, a possible benefit from the use of prepackaged skin antiseptic kits and alcohol-containing antiseptics.


Sign in / Sign up

Export Citation Format

Share Document