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Statistical methods for drug safety / Robert D. Gibbons and Anup K. Amatya.

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC biostatistics seriesPublication details: Boca Raton : CRC Press, ©2016.Description: xix, 288 p. : illustrations ; 25 cmISBN:
  • 9781466561847
Subject(s): DDC classification:
  • 000SB:615.704 23 G441
Contents:
1. Introduction; 2. Basic Statistical Concepts; 3. Multi-level Models; 4. Causal Inference; 5. Analysis of Spontaneous Reports; 6. Meta-analysis; 7. Ecological Methods; 8. Discrete-time Survival Models; 9. Research Synthesis; 10. Analysis of Medical Claims Data; 11. Methods to be Avoided; 12. Summary and Conclusions; Bibliography; Index.
Summary: Explore Important Tools for High-Quality Work in Pharmaceutical SafetyStatistical Methods for Drug Safety presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data.
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Includes bibliographical references and index.

1. Introduction;
2. Basic Statistical Concepts;
3. Multi-level Models;
4. Causal Inference;
5. Analysis of Spontaneous Reports;
6. Meta-analysis;
7. Ecological Methods;
8. Discrete-time Survival Models;
9. Research Synthesis;
10. Analysis of Medical Claims Data;
11. Methods to be Avoided;
12. Summary and Conclusions;
Bibliography;
Index.

Explore Important Tools for High-Quality Work in Pharmaceutical SafetyStatistical Methods for Drug Safety presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data.

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