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Platform Trial
A platform trial is a type of prospective, disease-focused, Adaptive clinical trial, adaptive, Clinical trial, randomized clinical trial (RCT) that compares multiple, simultaneous and possibly differently-timed interventions against a single, constant Treatment and control groups, control group. As a disease-focused trial design (compared to an intervention-focused), platform trials attempt to answer the question "which therapy will best treat this disease". Platform trials are unique in their utilization of both: a common control group and their opportunity to alter the therapies it investigates during its active enrollment phase. Platform trials commonly take advantage of Bayesian inference, Bayesian statistics, but may incorporate elements of Frequentist inference, frequentist statistics and/or machine learning. Purpose Platform trials can be a particularly useful design when researchers predict that multiple therapies that would become available at different times require inves ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Adaptive Clinical Trial
In an adaptive design of a clinical trial, the parameters and conduct of the trial for a candidate approved drug, drug or vaccine may be changed based on an interim analysis. Adaptive design typically involves advanced statistics to interpret a clinical trial clinical endpoint, endpoint. This is in contrast to traditional single-arm (i.e. non-randomized) clinical trials or randomized clinical trials (RCTs) that are static in their protocol and do not modify any parameters until the trial is completed. The adaptation process takes place at certain points in the trial, prescribed in the trial protocol. Importantly, this trial protocol is set before the trial begins with the adaptation schedule and processes specified. Adaptions may include modifications to: dosage, sample size, drug undergoing trial, patient selection criteria and/or "cocktail" mix. The PANDA (A Practical Adaptive & Novel Designs and Analysis toolkit) provides not only a summary of different adaptive designs, but also ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clinical Trial
Clinical trials are prospective biomedical or behavioral research studies on human participants designed to answer specific questions about biomedical or behavioral interventions, including new treatments (such as novel vaccines, drugs, dietary choices, dietary supplements, and medical devices) and known interventions that warrant further study and comparison. Clinical trials generate data on dosage, safety and efficacy. They are conducted only after they have received health authority/ethics committee approval in the country where approval of the therapy is sought. These authorities are responsible for vetting the risk/benefit ratio of the trial—their approval does not mean the therapy is 'safe' or effective, only that the trial may be conducted. Depending on product type and development stage, investigators initially enroll volunteers or patients into small pilot studies, and subsequently conduct progressively larger scale comparative studies. Clinical trials can vary i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Treatment And Control Groups
In the design of experiments, hypotheses are applied to experimental units in a treatment group. In comparative experiments, members of a control group receive a standard treatment, a placebo, or no treatment at all. There may be more than one treatment group, more than one control group, or both. A placebo control group can be used to support a double-blind study, in which some subjects are given an ineffective treatment (in medical studies typically a sugar pill) to minimize differences in the experiences of subjects in the different groups; this is done in a way that ensures no participant in the experiment (subject or experimenter) knows to which group each subject belongs. In such cases, a third, non-treatment control group can be used to measure the placebo effect directly, as the difference between the responses of placebo subjects and untreated subjects, perhaps paired by age group or other factors (such as being twins). For the conclusions drawn from the results of an e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". Introduction to Bayes' rule Formal explanation Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a "likelihood function" derived from a statistical model for the observed data. Bayesian inference computes the posterior probability according to Bayes' theorem: ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Frequentist Inference
Frequentist inference is a type of statistical inference based in frequentist probability, which treats “probability” in equivalent terms to “frequency” and draws conclusions from sample-data by means of emphasizing the frequency or proportion of findings in the data. Frequentist-inference underlies frequentist statistics, in which the well-established methodologies of statistical hypothesis testing and confidence intervals are founded. History of frequentist statistics The history of frequentist statistics is more recent than its prevailing philosophical rival, Bayesian statistics. Frequentist statistics were largely developed in the early 20th century and have recently developed to become the dominant paradigm in inferential statistics, while Bayesian statistics were invented in the 19th century. Despite this dominance, there is no agreement as to whether frequentism is better than Bayesian statistics, with a vocal minority of professionals studying statistical infer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
COVID-19 Pandemic
The COVID-19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel virus was first identified in an outbreak in the Chinese city of Wuhan in December 2019. Attempts to contain it there failed, allowing the virus to spread to other areas of Asia and later worldwide. The World Health Organization (WHO) declared the outbreak a public health emergency of international concern on 30 January 2020, and a pandemic on 11 March 2020. As of , the pandemic had caused more than cases and confirmed deaths, making it one of the deadliest in history. COVID-19 symptoms range from undetectable to deadly, but most commonly include fever, dry cough, and fatigue. Severe illness is more likely in elderly patients and those with certain underlying medical conditions. COVID-19 transmits when people breathe in air contaminated by droplets and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Institutional Review Board
An institutional review board (IRB), also known as an independent ethics committee (IEC), ethical review board (ERB), or research ethics board (REB), is a committee that applies research ethics by reviewing the methods proposed for research to ensure that they are ethical. Such boards are formally designated to approve (or reject), monitor, and review biomedical and behavioral research involving humans. They often conduct some form of risk-benefit analysis in an attempt to determine whether or not research should be conducted. The purpose of the IRB is to assure that appropriate steps are taken to protect the rights and welfare of humans participating as subjects in a research study. Along with developed countries, many developing countries have established national, regional or local Institutional Review Boards in order to safeguard ethical conduct of research concerning both national and international norms, regulations or codes. A key goal of IRBs is to protect human subjects ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dunnett's Test
In statistics, Dunnett's test is a multiple comparison procedure developed by Canadian statistician Charles Dunnett to compare each of a number of treatments with a single control. Multiple comparisons to a control are also referred to as many-to-one comparisons. History Dunnett's test was developed in 1955; an updated table of critical values was published in 1964.Dunnett C. W. (1964.) "New tables for multiple comparisons with a control", ''Biometrics'', 20:482491. Multiple Comparisons Problem The multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values. The major issue in any discussion of multiple-comparison procedures is the question of the probability of Type I errors. Most differences among alternative techniques result from different approaches to the question of how to control these errors. The problem is in part technical; b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clinical Study Design
Clinical study design is the formulation of trials and experiments, as well as observational studies in medical, clinical and other types of research (e.g., epidemiological) involving human beings. The goal of a clinical study is to assess the safety, efficacy, and / or the mechanism of action of an investigational medicinal product (IMP) or procedure, or new drug or device that is in development, but potentially not yet approved by a health authority (e.g. Food and Drug Administration). It can also be to investigate a drug, device or procedure that has already been approved but is still in need of further investigation, typically with respect to long-term effects or cost-effectiveness. Some of the considerations here are shared under the more general topic of design of experiments but there can be others, in particular related to patient confidentiality and ethics. Outline of types of designs for clinical studies Treatment studies * Randomized controlled trial ** Blind trial ** ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Experimental Design
Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the observations/data acquired during the experiment. This allows accounting for both any prior knowledge on the parameters to be determined as well as uncertainties in observations. The theory of Bayesian experimental design is to a certain extent based on the theory for making optimal decisions under uncertainty. The aim when designing an experiment is to maximize the expected utility of the experiment outcome. The utility is most commonly defined in terms of a measure of the accuracy of the information provided by the experiment (e.g. the Shannon information or the negative of the variance), but may also involve factors such as the financial cost of performing the experiment. What will be the optimal experiment design depends on the particular utility criterion chosen. Relations to more ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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