Essential Statistics for the Pharmaceutical Sciences

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Essential Statistics for the Pharmaceutical Sciences is targeted at all those involved in research in pharmacology, pharmacy or other areas of pharmaceutical science; everybody from undergraduate project students to experienced researchers should find the material they need.

This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results.  It deliberately avoids detailed calculation methodology.  Its key features are friendliness and clarity.   All methods are illustrated with realistic examples from within pharmaceutical science.

This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research.

  • a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences
  • all examples set in relevant pharmaceutical contexts.
  • key points emphasised in summary boxes and warnings of potential abuses in ‘pirate boxes’.
  • supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab – provided at www.ljmu.ac.uk/pbs/rowestats/

An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.

Author(s): Rowe, Philip
Edition: 2
Publisher: Wiley-Blackwell
Year: 2015

Language: English
Pages: 440
Tags: Медицинские дисциплины;Социальная медицина и медико-биологическая статистика;

Content: Data types --
Data presentation --
Descriptive statistics for interval scale data --
The normal distribution --
Sampling from populations. the standard error of the mean --
95% confidence interval for the mean and data transformation --
The two-sample t-test (1): introducing hypothesis tests --
The two-sample t-test (2): the dreaded P value --
The two-sample t-test (3): false negatives, power and necessary sample sizes --
The two-sample t-test (4): statistical significance, practical significance and equivalence --
The two-sample t-test (5): one-sided testing --
What does a statistically significant result really tell us? --
The paired t-test: comparing two related sets of measurements --
Analyses of variance: going beyond t-tests --
Correlation and regression relationships between measured values --
Analysis of covariance --
Describing categorised data and the goodness of fit chi-square test --
Contingency chi-square, Fisher's and McNemar's tests --
Relative risk, odds ratio and number needed to treat --
Logistic regression --
Ordinal and non-normally distributed data. transformations and non-parametric tests --
Measures of agreement --
Survival analysis --
Multiple testing --
Questionnaires.