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Predictive Probability Of Success
Predictive probability of success (PPOS) is a statistics concept commonly used in the pharmaceutical industry including by health authorities to support decision making. In clinical trials, PPOS is the probability of observing a success in the future based on existing data. It is one type of probability of success. A Bayesian means by which the PPOS can be determined is through integrating the data's likelihood over possible future responses (posterior distribution). Types of PPOS * Classification based on type of end point: Normal, binary, time to event. * Classification based on the relationship between the trial providing data and the trial to be predicted # Cross trial PPOS: using data from one trial to predict the other trial # Within trial PPOS: using data at interim analysis to predict the same trial at final analysis * Classification based on the relationship between the end point(s) with data and the end point to be predicted # 1 to 1 PPOS: using one end point to predict ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pharmaceutical Industry
The pharmaceutical industry is a medical industry that discovers, develops, produces, and markets pharmaceutical goods such as medications and medical devices. Medications are then administered to (or self-administered by) patients for curing or preventing disease or for alleviating symptoms of illness or injury. Pharmaceutical companies may deal in generic drugs, branded drugs, or both, in different contexts. Generic materials are without the involvement of intellectual property, whereas branded materials are protected by chemical patents. The industry's various subdivisions include distinct areas, such as manufacturing biologics and total synthesis. The industry is subject to a variety of laws and regulations that govern the patenting, efficacy testing, safety evaluation, and marketing of these drugs. The global pharmaceutical market produced treatments worth a total of $1,228.45 billion in 2020. The sector showed a compound annual growth rate (CAGR) of 1.8% in 2021, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Probability
Bayesian probability ( or ) is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses; that is, with propositions whose truth or falsity is unknown. In the Bayesian view, a probability is assigned to a hypothesis, whereas under frequentist inference, a hypothesis is typically tested without being assigned a probability. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies a prior probability. This, in turn, is then updated to a posterior probability in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedur ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
False Positive
A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a and a ). They are also known in medicine as a false positive (or false negative) diagnosis, and in statistical classification as a false positive (or false negative) error. In statistical hypothesis testing, the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to the differences between medical testing and sta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Type I Error
Type I error, or a false positive, is the erroneous rejection of a true null hypothesis in statistical hypothesis testing. A type II error, or a false negative, is the erroneous failure in bringing about appropriate rejection of a false null hypothesis. Type I errors can be thought of as errors of commission, in which the status quo is erroneously rejected in favour of new, misleading information. Type II errors can be thought of as errors of omission, in which a misleading status quo is allowed to remain due to failures in identifying it as such. For example, if the assumption that people are ''innocent until proven guilty'' were taken as a null hypothesis, then proving an innocent person as guilty would constitute a Type I error, while failing to prove a guilty person as guilty would constitute a Type II error. If the null hypothesis were inverted, such that people were by default presumed to be ''guilty until proven innocent'', then proving a guilty person's innocence would ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Apples And Oranges
A comparison of apples and oranges occurs when two items or groups of items are compared that cannot be practically compared, typically because of inherent or fundamental differences between the objects. The idiom, ''comparing apples and oranges'', refers to the differences between items which are popularly thought to be incomparable or incommensurable, such as apples and oranges. The idiom may also indicate that a false analogy has been made between two items, such as where an ''apple'' is faulted for not being a good ''orange''. Variants The idiom is not only used in English. In European French the idiom is (to compare apples and pears) or (to compare cabbages and carrots). The former is the same as the German In Latin American Spanish, it is (to compare potatoes and sweet potatoes) or, for all varieties of Spanish, (to compare pears and apples) or (to add pears and apples). In Peninsular Spanish, ''juntar churras con merinas'' (mix Churras with Merinos, two b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clinical Trial
Clinical trials are prospective biomedical or behavioral research studies on human subject research, human participants designed to answer specific questions about biomedical or behavioral interventions, including new treatments (such as novel vaccines, pharmaceutical drug, drugs, medical nutrition therapy, 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 institutional review board, 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 experiment, pi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Credible Interval
In Bayesian statistics, a credible interval is an interval used to characterize a probability distribution. It is defined such that an unobserved parameter value has a particular probability \gamma to fall within it. For example, in an experiment that determines the distribution of possible values of the parameter \mu, if the probability that \mu lies between 35 and 45 is \gamma=0.95, then 35 \le \mu \le 45 is a 95% credible interval. Credible intervals are typically used to characterize posterior probability distributions or predictive probability distributions. Their generalization to disconnected or multivariate sets is called credible set or credible region. Credible intervals are a Bayesian analog to confidence intervals in frequentist statistics. The two concepts arise from different philosophies: Bayesian intervals treat their bounds as fixed and the estimated parameter as a random variable, whereas frequentist confidence intervals treat their bounds as random varia ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Power
In frequentist statistics, power is the probability of detecting a given effect (if that effect actually exists) using a given test in a given context. In typical use, it is a function of the specific test that is used (including the choice of test statistic and significance level), the sample size (more data tends to provide more power), and the effect size (effects or correlations that are large relative to the variability of the data tend to provide more power). More formally, in the case of a simple hypothesis test with two hypotheses, the power of the test is the probability that the test correctly rejects the null hypothesis (H_0) when the alternative hypothesis (H_1) is true. It is commonly denoted by 1-\beta, where \beta is the probability of making a type II error (a false negative) conditional on there being a true effect or association. Background Statistical testing uses data from samples to assess, or make inferences about, a statistical population. Fo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Health Authority
Between 1996 and 2002, the National Health Service in England and Wales was organised under health authorities (HAs). There were 95 HAs at the time of their abolition in England in 2002, and they reported to the eight regional offices of the NHS Executive. They generally covered groups of one or more complete local authority districts (LADs), but there were cases where LADs were split. They were established in 1996 by the Health Authorities Act 1995. They took on the functions of the abolished District health authority, district health authorities (DHAs) and Family health services authority, family health services authorities (FHSAs). There were five HAs in Wales, reporting to the National Assembly. The HAs were divided into a total of 22 local health groups (LHGs), one in each of the Welsh unitary authorities. These HAs and LHGs were abolished when the Welsh NHS was restructured on 1 April 2003. The HAs in England were themselves divided into Primary Care Organisations (PCOs) c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Frequentist
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 Frequentism is based on the presumption that statistics represent probabilistic frequencies. This view was primarily developed by Ronald Fisher and the team of Jerzy Neyman and Egon Pearson. Ronald Fisher contributed to frequentist statistics by developing the frequentist concept of "significance testing", which is the study of the significance of a measure of a statistic when compared to the hypothesis. Neyman-Pearson extended Fisher's ideas to apply to multiple hypotheses. They posed that the ratio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clinical Endpoint
Clinical endpoints or clinical outcomes are outcome measures referring to occurrence of disease, symptom, sign or laboratory abnormality constituting a target outcome in clinical research trials. The term may also refer to any disease or sign that strongly motivates withdrawal of an individual or entity from the trial, then often termed a ''humane (clinical) endpoint''. The primary endpoint of a clinical trial is the endpoint for which the trial is powered. Secondary endpoints are additional endpoints, preferably also pre-specified, for which the trial may not be powered. Surrogate endpoints are trial endpoints that have outcomes that substitute for a clinical endpoint, often because studying the clinical endpoint is difficult, for example using an increase in blood pressure as a surrogate for death by cardiovascular disease, where strong evidence of a causal link exists. Scope In a general sense, a clinical endpoint is included in the entities of interest in a trial. The re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
