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Two or more elements or molecules which are chemically bound to each other. The term compound™ is often used to refer to a medicine which is being developed.
Something that exists or occurs at the same time as something else. It can be a natural event, but in medicine is used when referring to:
A confidence interval is an estimated range of values in which all data (results) are likely to lie. For a given treatment effect measured in a trial on a sample of a population, the confidence interval can be calculated to give a 'best estimate' range of the treatment effect that will be seen in the whole population.
The likelihood that the confidence interval will contain the value is called the confidence level. Traditionally, confidence levels are set at 95% or 99%. This means that researchers are 95% (or 99%) certain that the measured effect lies within the true range.
For example, instead of estimating the mean age of a population as 15 years, researchers say that the mean age is between 14 and 16. This confidence interval contains the true value being estimated.
These are studies conducted in Phase III of the clinical development of a medicine. They aim to confirm the efficacy and safety in a large patient population. They can involve thousands of patients, can be run in many countries, require a huge amount of expertise to be run effectively, and are therefore resource intense and very time consuming. They are the largest, most complicated, and most expensive part of the development of a medicine.
A confounding variable is something, other than the treatment being studied, that can affect the measured outcome of a trial . For example, imagine that a medicine to prevent the common cold is tested by administering it to 1,000 men, while a placebo is administered to a group of 1,000 women. The trial results show that far fewer men caught a cold during the trial period. It would not, however, be possible to conclude that the medicine had an effect because all of the placebo group were women, and therefore gender is a confounding factor. The trial results could have a plausible alternative explanation - for example, that women are more susceptible to the cold viruses circulating at the time of the study.
Well-designed trials take account of potential confounding variables and allow the elimination of plausible alternative explanations for study findings. In the example given above, men and women could be randomly assigned to the intervention and placebo groups to remove gender as a confounding variable.
A measurement, often expressed in numbers, collected in a clinical trial that represents a specific variable. Unlike binary endpoints which are expressed by yes™ or no™ (e.g. survived™ against dead™), continuous endpoints are expressed by measurement on a continuum of possible values over time (e.g. blood pressure or months of survival).
Contract research organisation
Coordination Group for Mutual Recognition and Decentralised procedures – human
Coordination Group for Mutual Recognition and Decentralised procedures – human (CMDh) http://www.hma.eu/cmdh.html"
In the context of pharmacoeconomics, cost effectiveness is studied by looking at the results of different interventions by measuring a single outcome, usually in 'natural' units (for example, life-years gained, deaths avoided, heart attacks avoided, or cases detected).
Alternative interventions are then compared in terms of cost per (natural) unit of effectiveness in order to assess how it provides value for money. This economic evaluation helps decision-makers to determine where to allocate limited healthcare resources.
Cost effectiveness, however, is only one of a number of criteria that should be used to determine whether or not interventions are made available. Other issues, such as equity, needs, and priorities should also be part of the decision-making process.