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Introduction Statistics Pharmaceutical Clinical Trials by Turner Rick Durham Todd - AbeBooks

Covers a range of non-medicinal products suitable for use at home. A practical guide to the use of pharmacokinetic principles in clinical practice. Includes case studies with questions and answers. Want to keep up with the latest news, comment and CPD articles in pharmacy and science? Subscribe to our free alerts. Skip to main content Skip to navigation.

Drug Supply Modelling in Clinical Trials (Statistical Methodology)

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Durham, Todd A; Turner, J Rick

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Introduction to statistics in pharmaceutical clinical trials book review. ISBN 0 5 As suggested by the title, this textbook highlights the intertwined paths of clinical trials and statistical testing.

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Patient Care in Community Practice Patient Care in Community Practice is a unique, practical guide for healthcare professionals or carers. Clinical Pharmacokinetics A practical guide to the use of pharmacokinetic principles in clinical practice. Top Stories. Print Share. Related articles. Jobs you might like. Similar topics. The goal of this note is to introduce a way to describe the results of clinical trials using concepts that are easy to communicate to non-statisticians. To take into account uncertainty, we should be conservative and estimate the smallest treatment effect that we would expect to observe over many repeated studies.

I introduce an estimate called the Smallest Effect over Repeated Studies SERS which is defined as the smallest treatment effect we would expect to observe if we repeated an experiment M times. For example, the following statements describe the results of the same hypothetical clinical trial:. To be concrete, I derive the SERS estimate for the difference in the mean outcome in the treatment group and the mean outcome in the control group.

In an RCT, a well-defined group of subjects are randomly assigned to receive the treatment or the control. At the end of the trial, the subjects who received the treatment are compared to those who received the control to estimate the effect of the treatment. Therefore, researchers use statistical tools to determine if an observed effect is reliable. One of the main tools that researchers use to assess the reliability of an observed effect is based on a statistical concept known as significance. To assess the statistical significance of an observed treatment effect, researchers estimate the probability of observing an equal or larger effect in similarly designed studies if, in reality, the treatment is no more effective than the control.

This probability is called a p-value. Opponents say that relying on statistical significance alone can lead to poor decisions because it provides a black-and-white answer to a problem with shades of grey; e. My main complaints with null-hypothesis significance testing are twofold:. In fact, the second point is worth a closer look given the first. There is essentially no such thing as a treatment effect that is exactly zero. If the treatment only consisted of an extra glass of water per day, one would expect it to have a least some effect, even if that effect is extremely small.

The theory underlying NHST is based on controlling the probabilities of two types of errors [7]. The type I error rate is the probability that we reject the null-hypothesis given that it is true. The type II error rate is the probability that we fail to reject the null hypothesis even if the true treatment effect is non-zero.

But this is a straw-man. The treatment effect is surely not exactly zero. What we are really worried about are treatments with effects that are so small they are practically zero.

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Researchers achieve this last goal by running experiments for which the sample size is too small to reliably estimate such small effects i. When studies that are underpowered show significant effects, these effects will be generally be overestimates [8]. These points combine to create the following situation. The treatment is declared effective and given to patients.

What Are Clinical Trial Phases?

Follow-up studies show that the treatment is not as effective as suggested by the original study.