Comparative poisson clinical trials of multiple experimental treatments vs a single control using the negative multinomial distribution

2021 ◽  
Author(s):  
Joseph A. Chiarappa ◽  
Donald R. Hoover
Keyword(s):  
Clinical Trials ◽  
Multinomial Distribution ◽  
Negative Multinomial Distribution ◽  
Experimental Treatments

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 Application of a Negative Multinomial Model Gives Insight into Rarity-Area Relationships

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 571
Author(s):  
Youhua Chen ◽  
Yongbin Wu ◽  
Weihua Chen ◽  
Tian Zhao ◽  
Wenyan Zhang ◽  
...  
Keyword(s):  
Poisson Distribution ◽  
Rare Species ◽  
Spatial Scales ◽  
Multinomial Distribution ◽  
Barro Colorado Island ◽  
Field Sampling ◽  
Local Sample ◽  
Negative Multinomial Distribution ◽  
Using Data ◽  
Insight Into

The distribution of individuals of different species across different sampling units is typically non-random. This distributional non-independence can be interpreted and modelled as a correlated multivariate distribution. However, this correlation cannot be modelled using a totally independent and random distribution such as the Poisson distribution. In this study, we utilized the negative multinomial distribution to overcome the problem encountered by the commonly used Poisson distribution and used it to derive insight into the implications of field sampling for rare species’ distributions. Mathematically, we derived, from the negative multinomial distribution and sampling theory, contrasting relationships between sampling area, and the proportions of locally rare and regionally rare species in ecological assemblages presenting multi-species correlated distribution. With the suggested model, we explored the cross-scale relationships between the spatial extent, the population threshold for defining the rarity of species, and the multi-species correlated distribution pattern using data from two 50-ha tropical forest plots in Barro Colorado Island (Panama) and Heishiding Provincial Reserve (Guangdong Province, China). Notably, unseen species (species with zero abundance in the studied local sample) positively contributed to the distributional non-independence of species in a local sample. We empirically confirmed these findings using the plot data. These findings can help predict rare species–area relationships at various spatial scales, potentially informing biodiversity conservation and development of optimal field sampling strategies.

What is the probability that new cancer treatments are better than standard treatments?

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6120-6120
Author(s):  
B. Djulbegovic ◽  
A. Kumar ◽  
H. P. Soares
Keyword(s):  
Clinical Trials ◽  
Treatment Success ◽  
Publication Rate ◽  
Phase Iii ◽  
Moral Principle ◽  
Absolute Difference ◽  
Cooperative Groups ◽  
Treatment Type ◽  
Experimental Treatments ◽  
Comparator Intervention

6120 Background: How often new treatments tested in phase III randomized controlled trials (RCT) are superior to standard treatments is not known. To accurately answer this question three factors have to be considered: publication rate, quality of trials and the choice of the adequate comparator intervention. Methods: All consecutive RCTs conducted by 6 National Cancer Institute sponsored cooperative groups from 1955 to 2000 were reviewed. Data on primary outcomes from published and unpublished trials were analyzed. The results were assessed for the possible effects of bias, random error and choice of comparator intervention. Results: We abstracted data from 444 trials, involving 566 comparisons, enrolling more than 150,000 patients. Hazard ratio (HR) for overall survival favored experimental treatments 0.96 [99%CI (0.94, 0.98), p<0.0001]. Treatment related mortality (TRM) was worse with innovative treatments (HR 1.45 [99%CI (1.24, 1.69)], p<0.0001). In absolute difference this amounts to <0.4% in survival and 0.38% in TRM. Although the distribution of successes was on average similar between experimental and standard treatments, we found that one in 14 trials lead to discovery of a treatment that improved survival by 50%. The findings were not affected by publication bias, methodological quality, treatment type, disease, or comparator. Conclusions: In clinical trials of new cancer drugs, experimental treatments in cancer which have progressed to testing in RCTs are, on average, as likely to be inferior as to be superior to standard treatments. Individual treatments may be more or less successful but this cannot be predicted and can only be known after a trial is conducted. This pattern of treatment success is not accidental, but is directly related to moral principle for conduct of clinical trials known as equipoise or uncertainty principle (BMJ2005;331:1295). Such uncertainty makes it easier for patients to decide whether to participate in such trials, and for researchers to justify the clinical trial system, which has led to advances in treatment of cancer. No significant financial relationships to disclose.

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 Vaccines and Treatment of Coronavirus Disease 2019

2020 ◽  
Vol 95 (6) ◽  
pp. 364-369
Author(s):  
Pyoeng Gyun Choe
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Antiviral Agents ◽  
World Health ◽  
Rna Dependent Rna Polymerase ◽  
Global Pandemic ◽  
The World ◽  
Polymerase Inhibitor ◽  
Experimental Treatments ◽  
Health Organization

In December 2019, a new strain of betacoronavirus, severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19), emerged in Wuhan, China. Subsequently, the virus quickly spread worldwide and the World Health Organization declared COVID-19 a global pandemic on March 11, 2020. In response to the pandemic, many researchers are working on repurposing existing drugs to alter the course of severe COVID-19, and are testing experimental treatments. Among antiviral agents, remdesivir, an RNA-dependent RNA polymerase inhibitor, showed clinical benefit in a randomized clinical trial. In October 2020, the Food and Drug Administration approved remdesivir for treating hospitalized patients with COVID-19, making it the first drug approved for the disease. The race to produce safe, effective vaccines is also progressing at unprecedented speed, with over 200 under development and 45 candidates already being tested in human clinical trials (as of October 2020).

Success in Meeting the Primary Endpoint in Phase III Trials: A Comparison of Industry and Cooperative Group Trials

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 508-508
Author(s):  
Benjamin Djulbegovic ◽  
Ambuj Kumar ◽  
Branko Miladinovic ◽  
Asmita Mhaskar ◽  
Tea Reljic ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Public Sector ◽  
Success Rate ◽  
Odds Ratio ◽  
Phase Iii ◽  
High Likelihood ◽  
Specific Cancer ◽  
Experimental Treatments ◽  
New Treatments ◽  
Design Bias

Abstract Abstract 508 Background: Evaluation of research effort, especially estimation of the proportion of treatment successes in randomized clinical trials (RCTs), has important ethical, scientific, and public policy implications. Whether commercial or public sector research programs generate higher discovery rate of new successful treatments when tested in cancer RCTs is not known. These research programs are postulated to be governed by two competing hypotheses. The “equipoise/uncertainty hypothesis” assumes that investigators cannot predict trial results in advance, and as a consequence, the rate of discovering new treatments is about 50%. In contrast, the “design bias hypothesis” assumes that researchers conduct only those RCTs which have high likelihood of success. We hypothesize that the public sector RCTs are governed by the equipoise hypothesis while the industry-sponsored (IS) RCTs are based on the design bias hypothesis. Here we conduct the comparative systematic assessment to investigate if IS RCTs are associated with higher success rates than publicly-sponsored trials (PS) according to design bias versus equipoise/uncertainty hypothesis, respectively. Methods: All consecutive, published and unpublished, phase III cancer RCTs assessing treatment superiority and conducted by Canada's NCIC Clinical Trials Group (NCIC CTG) and GlaxoSmithKline (GSK) from 1980 to June 2010 were included. All trial protocols from GSK and NCIC CTG were reviewed independently by two reviewers to determine their eligibility. Two reviewers independently extracted data from eligible study protocols and publications using a standardized form. Three metrics were extracted to determine treatment successes: (1) the proportion of statistically significant trials favoring new or standard treatments, (2) the proportion of the trials in which new treatments were considered superior according to the original investigators, and (3) quantitative synthesis of data for primary outcomes as defined in each trial. An experimental regimen (drug compound or combinations or procedures), which was not tested previously in an RCT involving a specific cancer population or for alleviation of symptoms was classified as a major innovation. If a drug or regimen was already tested in a specific cancer population and testing involved dose modifications or changes in route of administration, it was classified as a minor innovation. Results: Between1980 to 2010 NCIC CTG conducted 77 RCTs enrolling 33,260 patients while GSK conducted 40 cancer RCTs accruing 19,889 patients. Forty two percent (99%CI 24 to 60) of the results were statistically significant favoring experimental treatments in GSK versus 25% (99%CI 13 to 37) in the NCIC CTG cohort (p=0.04). Investigators concluded that new treatments were superior to standard treatments in 80% of GSK versus 44% of NCIC CTG RCTs (p<0.0001) The GSK investigators deemed 32% (99%CI 14 to 50; 14/44) of interventions as “breakthroughs” versus 10% (99%CI 1 to 18; 8/82) by NCIC CTG investigators (p=0.002). Pooled analysis for the primary outcome indicated higher success rate in GSK trials (odds ratio: 0.61 [99%CI 0.47–0.78]) versus NCIC trials (odds ratio: 0.86 [99%CI 0.74–1.00]) (p=0.003). Experimental treatments were considered as major innovations in 32% (99%CI 15 to 49; 16/50) of GSK vs. 93% (99%CI 86 to 100; 78/84) of NCIC CTG trials (p<0.0001). Increased success rate in IS RCTs was mainly due to testing of new palliative agents, while the research program of NCIC CTG largely focused on development of therapies to improve survival. Conclusions: This first study evaluating the treatment success and pattern of therapeutic discoveries in IS versus PS research showed that industry discovers more successful new treatments compared with public sector. However, industry appears to undertake RCTs with high likelihood of success. PS research had significantly high proportion of major innovations compared with IS research. Disclosures: No relevant conflicts of interest to declare.

Log-Linear Modeling with the Negative Multinomial Distribution

Biometrics ◽  
1997 ◽  
Vol 53 (3) ◽  
pp. 971 ◽  
Author(s):  
Lance A. Waller ◽  
Daniel Zelterman
Keyword(s):  
Multinomial Distribution ◽  
Linear Modeling ◽  
Negative Multinomial Distribution ◽  
Log Linear

scholarly journals The effects of releasing early results from ongoing clinical trials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Steffen Ventz ◽  
Sergio Bacallado ◽  
Rifaquat Rahman ◽  
Sara Tolaney ◽  
Jonathan D. Schoenfeld ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Trial Data ◽  
Simulation Models ◽  
Trial Data ◽  
Operating Characteristics ◽  
Trial Duration ◽  
The Public ◽  
Early Results ◽  
Statistical Validity ◽  
Experimental Treatments

AbstractMost trials do not release interim summaries on efficacy and toxicity of the experimental treatments being tested, with this information only released to the public after the trial has ended. While early release of clinical trial data to physicians and patients can inform enrollment decision making, it may also affect key operating characteristics of the trial, statistical validity and trial duration. We investigate the public release of early efficacy and toxicity results, during ongoing clinical studies, to better inform patients about their enrollment options. We use simulation models of phase II glioblastoma (GBM) clinical trials in which early efficacy and toxicity estimates are periodically released accordingly to a pre-specified protocol. Patients can use the reported interim efficacy and toxicity information, with the support of physicians, to decide which trial to enroll in. We describe potential effects on various operating characteristics, including the study duration, selection bias and power.

scholarly journals A simulation study of outcome adaptive randomization in multi-arm clinical trials

2017 ◽  
Vol 14 (5) ◽  
pp. 432-440 ◽  
Author(s):  
J Kyle Wathen ◽  
Peter F Thall
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Simulation Study ◽  
Treatment Success ◽  
Lower Probability ◽  
Ethical Considerations ◽  
Established Method ◽  
Adaptive Randomization ◽  
Experimental Treatments ◽  
Randomization Method

Randomizing patients among treatments with equal probabilities in clinical trials is the established method to obtain unbiased comparisons. In recent years, motivated by ethical considerations, many authors have proposed outcome adaptive randomization, wherein the randomization probabilities are unbalanced, based on interim data, to favor treatment arms having more favorable outcomes. While there has been substantial controversy regarding the merits and flaws of adaptive versus equal randomization, there has not yet been a systematic simulation study in the multi-arm setting. A simulation study was conducted to evaluate four different Bayesian adaptive randomization methods and compare them to equal randomization in five-arm clinical trials. All adaptive randomization methods included an initial burn-in with equal randomization and some combination of other modifications to avoid extreme randomization probabilities. Trials either with or without a control arm were evaluated, using designs that may terminate arms early for futility and select one or more experimental treatments at the end. The designs were evaluated under a range of scenarios and sample sizes. For trials with a control arm and maximum same size 250 or 500, several commonly used adaptive randomization methods have very low probabilities of correctly selecting a truly superior treatment. Of those studied, the only adaptive randomization method with desirable properties has a burn-in with equal randomization and thereafter randomization probabilities restricted to the interval 0.10–0.90. Compared to equal randomization, this method has a favorable sample size imbalance but lower probability of correctly selecting a superior treatment. In multi-arm trials, compared to equal randomization, several commonly used adaptive randomization methods give much lower probabilities of selecting superior treatments. Aside from randomization method, conducting a multi-arm trial without a control arm may lead to very low probabilities of selecting any superior treatments if differences between the treatment success probabilities are small.

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