scholarly journals Using numerical modeling and simulation to assess the ethical burden in clinical trials and how it relates to the proportion of responders in a trial sample

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258093
Author(s):  
Jean-Pierre Boissel ◽  
David Pérol ◽  
Hervé Décousus ◽  
Ingrid Klingmann ◽  
Marc Hommel
Keyword(s):  
Clinical Trials ◽  
Statistical Power ◽  
Controlled Trial ◽  
False Negative ◽  
Experimental Treatment ◽  
Chi Square ◽  
Randomized Controlled Clinical Trials ◽  
Numerical Modeling And Simulation ◽  
Event Rates ◽  
Trial Participants

In order to propose a more precise definition and explore how to reduce ethical losses in randomized controlled clinical trials (RCTs), we set out to identify trial participants who do not contribute to demonstrating that the treatment in the experimental arm is superior to that in the control arm. RCTs emerged mid-last century as the gold standard for assessing efficacy, becoming the cornerstone of the value of new therapies, yet their ethical grounds are a matter of debate. We introduce the concept of unnecessary participants in RCTs, the sum of non-informative participants and non-responders. The non-informative participants are considered not informative with respect to the efficacy measured in the trial in contrast to responders who carry all the information required to conclude on the treatment’s efficacy. The non-responders present the event whether or not they are treated with the experimental treatment. The unnecessary participants carry the burden of having to participate in a clinical trial without benefiting from it, which might include experiencing side effects. Thus, these unnecessary participants carry the ethical loss that is inherent to the RCT methodology. On the contrary, responders to the experimental treatment bear its entire efficacy in the RCT. Starting from the proportions observed in a real placebo-controlled trial from the literature, we carried out simulations of RCTs progressively increasing the proportion of responders up to 100%. We show that the number of unnecessary participants decreases steadily until the RCT’s ethical loss reaches a minimum. In parallel, the trial sample size decreases (presumably its cost as well), although the trial’s statistical power increases as shown by the increase of the chi-square comparing the event rates between the two arms. Thus, we expect that increasing the proportion of responders in RCTs would contribute to making them more ethically acceptable, with less false negative outcomes.

scholarly journals Using numerical modelling and simulation to assess the ethical burden in clinical trials and how it relates to the proportion of responders in a trial sample

2021 ◽  
Author(s):  
Jean-Pierre Boissel ◽  
David Pérol ◽  
Hervé Décousus ◽  
Ingrid Klingmann ◽  
Marc Hommel
Keyword(s):  
Clinical Trials ◽  
Statistical Power ◽  
Controlled Trial ◽  
False Negative ◽  
Experimental Treatment ◽  
Chi Square ◽  
Randomized Controlled Clinical Trials ◽  
Randomized Controlled ◽  
Event Rates ◽  
Trial Participants

ABSTRACTIn order to explore how to reduce ethical losses in randomized controlled clinical trials (RCTs), we set out to identify trial participants, including non-responders, who do not contribute to demonstrating that the treatment in the experimental arm is superior to that in the control arm. RCTs emerged mid last century as the gold standard for assessing efficacy, becoming the cornerstone of the value of new therapies, yet their ethical grounds are still debated. We introduce the concept of unnecessary participants, defined as those included in RCTs considered not informative with respect to efficacy, in contrast to responders who carry all the information required to conclude on the treatment’s efficacy. Non-informative participants carry the burden of having to participate in a clinical trial without benefiting from it, which might include experiencing side effects. Non-informative participants can experience the outcome even if allocated to the experimental arm, or not present it even if allocated to the control arm. Thus, these unnecessary participants carry the ethical loss that is inherent to the RCT methodology. On the contrary, responders to the experimental treatment bear its entire efficacy in the RCT. Starting from the proportions observed in a real placebo-controlled trial from the literature, we carried out simulations of RCTs progressively increasing the proportion of responders up to 100%. We show that the number of unnecessary participants decreases steadily until the RCT’s ethical loss reaches a minimum. In parallel, the trial sample size decreases (presumably its cost as well), although the trial’s statistical power increases as shown by the increase of the chi-square comparing the event rates between the two arms. Thus, we expect that increasing the proportion of responders in RCTs would contribute to making them more ethically acceptable, with less false negative outcomes.

Antioxidants and toxicity of cancer chemotherapy: A systematic review of the evidence from randomized controlled trials

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 9130-9130
Author(s):  
K. I. Block ◽  
A. C. Koch ◽  
M. N. Mead ◽  
P. Tothy ◽  
R. A. Newman ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cancer Chemotherapy ◽  
Statistical Power ◽  
Meta Analysis ◽  
Controlled Clinical Trials ◽  
Randomized Controlled Clinical Trials ◽  
Randomized Controlled ◽  
Standardized Protocol ◽  
Concurrent Use ◽  
Relapsed Disease

9130 Background: Much debate has focused on whether or not antioxidants interfere with the efficacy of cancer chemotherapy. The objective of this study is to systematically review the medical literature to compile results from randomized, controlled clinical trials evaluating the effects of concurrent use of antioxidants with chemotherapy on toxic side effects. Methods: We performed a search of literature from 1966-December 2006 using MEDLINE, Cochrane, CinAhl, AMED, AltHealthWatch and EMBASE databases. Randomized, controlled clinical trials reporting mitigation of chemotherapy toxicity were included in the final tally. The searches were performed in duplicate following a standardized protocol. No meta-analysis was performed due to heterogeneity of cancers and chemotherapy regimens. Results: Of 848 articles considered, 32 trials met the inclusion criteria. Antioxidants evaluated were: glutathione (9), melatonin (6), vitamin A (1), an antioxidant mixture (3), N-acetylcysteine (3), vitamin E (5), selenium (1), L-carnitine (1), Co-Q10 (2) and ellagic acid (1). Subjects of most studies had advanced or relapsed disease. Conclusions: One of the 32 studies included reported evidence of significant increase in toxicity from the concurrent use of antioxidants with chemotherapy. In 18 studies, antioxidant groups experienced significantly lower toxicity than control groups. Statistical power and poor study quality were concerns with some of the studies. We have reported elsewhere that randomized trials of various antioxidants given with chemotherapy did not demonstrate an adverse effect on treatment response or survival. Well-designed studies evaluating larger populations of patients given antioxidants concurrently with chemotherapy are thus warranted. 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.

scholarly journals Lessons from Randomised Clinical Trials for Triiodothyronine Treatment of Hypothyroidism: Have They Achieved Their Objectives?

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Rudolf Hoermann ◽  
John E. M. Midgley ◽  
Rolf Larisch ◽  
Johannes W. Dietrich
Keyword(s):  
Clinical Trials ◽  
Statistical Power ◽  
Treatment Options ◽  
Drug Efficacy ◽  
Experimental Treatment ◽  
Current Treatment ◽  
Treatment Protocols ◽  
Novel Studies ◽  
Randomised Controlled ◽  
Based Medicine

Randomised controlled trials are deemed to be the strongest class of evidence in evidence-based medicine. Failure of trials to prove superiority of T3/T4 combination therapy over standard LT4 monotherapy has greatly influenced guidelines, while not resolving the ongoing debate. Novel studies have recently produced more evidence from the examination of homeostatic equilibria in humans and experimental treatment protocols in animals. This has exacerbated a serious disagreement with evidence from the clinical trials. We contrasted the weight of statistical evidence against strong physiological counterarguments. Revisiting this controversy, we identify areas of improvement for trial design related to validation and sensitivity of QoL instruments, patient selection, statistical power, collider stratification bias, and response heterogeneity to treatment. Given the high individuality expressed by thyroid hormones, their interrelationships, and shifted comfort zones, the response to LT4 treatment produces a statistical amalgamation bias (Simpson’s paradox), which has a key influence on interpretation. In addition to drug efficacy, as tested by RCTs, efficiency in clinical practice and safety profiles requires reevaluation. Accordingly, results from RCTs remain ambiguous and should therefore not prevail over physiologically based counterarguments. In giving more weight to other forms of valid evidence which contradict key assumptions of historic trials, current treatment options should remain open and rely on personalised biochemical treatment targets. Optimal treatment choices should be guided by strict requirements of organizations such as the FDA, demanding treatment effects to be estimated under actual conditions of use. Various improvements in design and analysis are recommended for future randomised controlled T3/T4 combination trials.

L-arginine effect on inflammatory mediators: A systematic review of randomized controlled clinical trials

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Seyed Reza Mirhafez ◽  
Mitra Hariri
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Inflammatory Mediators ◽  
Randomized Clinical Trials ◽  
Randomized Controlled Clinical Trials ◽  
Patients With Cancer ◽  
Reactive Protein ◽  
Randomized Controlled ◽  
Human Adults ◽  
Factor Α

Abstract. L-arginine is an important factor in several physiological and biochemical processes. Recently, scientists studied L-arginine effect on inflammatory mediators such as C-reactive protein (CRP), tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). We conducted a systematic review on randomized controlled trials assessing L-arginine effect on inflammatory mediators. We searched data bases including Google scholar, ISI web of science, SCOPUS, and PubMed/Medline up to April 2019. Randomized clinical trials assessing the effect of L-arginine on inflammatory mediators in human adults were included. Our search retrieved eleven articles with 387 participants. Five articles were on patients with cancer and 6 articles were on adults without cancer. L-arginine was applied in enteral form in 5 articles and in oral form in 6 articles. Eight articles were on both genders, two articles were on women, and one article was on men. L-arginine could not reduce inflammatory mediators among patients with and without cancer except one article which indicated that taking L-arginine for 6 months decreased IL-6 among cardiopathic nondiabetic patients. Our results indicated that L-arginine might not be able to reduce selected inflammatory mediators, but for making a firm decision more studies are needed to be conducted with longer intervention duration, separately on male and female and with different doses of L-arginine.

Planning the Size of Clinical Trials with Allowance for Patient Noncompliance

1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Event Rate ◽  
Survival Functions ◽  
Time Intervals ◽  
Patient Noncompliance ◽  
Event Rates ◽  
Larger Sample

AbstractVarious approaches are considered for adjustment of clinical trial size for patient noncompliance. Such approaches either model the effect of noncompliance through comparison of two survival distributions or two simple proportions. Models that allow for variation of noncompliance and event rates between time intervals are also considered. The approach that models the noncompliance adjustment on the basis of survival functions is conservative and hence requires larger sample size. The model to be selected for noncompliance adjustment depends upon available estimates of noncompliance and event rate patterns.

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