An edition of Statistical methods for drug safety
(2016)
Statistical methods for drug safety
by Robert D. Gibbons
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Subjects
Pharmacoepidemiology, Statistical methods, Safety measures, Methods, Statistics as Topic, Drugs, Pharmacovigilance, Pharmacology, Medicine, Patients, Medical errors, Prevention, Medical care, Drug monitoring, Side effects, Reporting, Statistics, Postmarketing Product Surveillance, Drug Monitoring, Médecine, Sécurité, Mesures, Statistiques, HEALTH & FITNESS, Holism, Reference, MEDICAL, Alternative Medicine, Atlases, Essays, Family & General Practice, Holistic Medicine, Osteopathy| Edition | Availability |
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Statistical methods for drug safety
2016, CRC Press, Taylor & Francis Group, CRC Press, Chapman and Hall/CRC
in English
146656184X 9781466561847
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Book Details
Table of Contents
Machine generated contents note: 1.Introduction
1.1.Randomized Clinical Trials
1.2.Observational Studies
1.3.The Problem of Multiple Comparisons
1.4.The Evolution of Available Data Streams
1.5.The Hierarchy of Scientific Evidence
1.6.Statistical Significance
1.7.Summary
2.Basic Statistical Concepts
2.1.Introduction
2.2.Relative Risk
2.3.Odds Ratio
2.4.Statistical Power
2.5.Maximum Likelihood Estimation
2.5.1.Example with a Closed Form Solution
2.5.2.Example without a Closed Form Solution
2.5.3.Bayesian Statistics
2.5.4.Example
2.6.Non-linear Regression Models
2.7.Causal Inference
2.7.1.Counterfactuals
2.7.2.Average Treatment Effect
3.Multi-level Models
3.1.Introduction
3.2.Issues Inherent in Longitudinal Data
3.2.1.Heterogeneity
3.2.2.Missing Data
3.2.3.Irregularly Spaced Measurement Occasions
3.3.Historical Background
Note continued: 3.4.Statistical Models for the Analysis of Longitudinal and/or Clustered Data
3.4.1.Mixed-effects Regression Models
3.4.1.1.Random Intercept Model
3.4.1.2.Random Intercept and Trend Model
3.4.2.Matrix Formulation
3.4.3.Generalized Estimating Equation Models
3.4.4.Models for Categorical Outcomes
4.Causal Inference
4.1.Introduction
4.2.Propensity Score Matching
4.2.1.Illustration
4.2.2.Discussion
4.3.Marginal Structural Models
4.3.1.Illustration
4.3.2.Discussion
4.4.Instrumental Variables
4.4.1.Illustration
4.5.Differential Effects
5.Analysis of Spontaneous Reports
5.1.Introduction
5.2.Proportional Reporting Ratio
5.2.1.Discussion
5.3.Bayesian Confidence Propagation Neural Network (BCPNN)
5.4.Empirical Bayes Screening
5.5.Multi-item Gamma Poisson Shrinker
5.6.Bayesian Lasso Logistic Regression
5.7.Random-effect Poisson Regression
5.7.1.Rate Multiplier
5.8.Discussion
Note continued: 6.Meta-analysis
6.1.Introduction
6.2.Fixed-effect Meta-analysis
6.2.1.Correlation Coefficient
6.2.2.Mean Difference
6.2.3.Relative Risk
6.2.3.1.Inverse Variance Method
6.2.3.2.Mantel-Haenszel Method
6.2.4.Odds Ratio
6.2.4.1.Inverse Variance Method
6.2.4.2.Mantel-Haenszel Method
6.2.4.3.Peto Method
6.3.Random-effect Meta-analysis
6.3.1.Sidik-Jonkman Estimator of Heterogeneity
6.3.2.DerSimonian-Kacker Estimator of Heterogeneity
6.3.3.REML Estimator of Heterogeneity
6.3.4.Improved PM Estimator of Heterogeneity
6.3.5.Example
6.3.6.Issues with the Weighted Average in Meta-analysis
6.4.Maximum Marginal Likelihood/Empirical Bayes Method
6.4.1.Example: Percutaneous Coronary Intervention Based Strategy versus Medical Treatment Strategy
6.5.Bayesian Meta-analysis
6.5.1.WinBugs Example
6.6.Confidence Distribution Framework for Meta-analysis
6.6.1.The Framework
6.6.1.1.Fixed-effects Model
Note continued: 6.6.1.2.Random-effects Model
6.6.2.Meta-analysis of Rare Events under the CD Framework
6.7.Discussion
7.Ecological Methods
7.1.Introduction
7.2.Time Series Methods
7.2.1.Generalized Event Count Model
7.2.2.Tests of Serial Correlation
7.2.3.Parameter-driven Generalized Linear Model
7.2.4.Autoregressive Model
7.3.State Space Model
7.4.Change-point Analysis
7.4.1.The u-chart
7.4.2.Estimation of a Change-point
7.4.3.Change-point Estimator for the INAR(1) Model
7.4.3.1.Change-point Estimator for the Rate Parameter
7.4.3.2.Change-point Estimator for the Dependence Parameter
7.4.4.Change-point of a Poisson Rate Parameter with Linear Trend Disturbance
7.4.5.Change-point of a Poisson Rate Parameter with Level and Linear Trend Disturbance
7.4.6.Discussion
7.5.Mixed-effects Poisson Regression Model
8.Discrete-time Survival Models
8.1.Introduction
8.2.Discrete-time Ordinal Regression Model
Note continued: 8.3.Discrete-time Ordinal Regression Frailty Model
8.4.Illustration
8.5.Competing Risk Models
8.5.1.Multinomial Regression Model
8.5.2.Mixed-Effects Multinomial Regression Model
8.6.Illustration
8.6.1.Model Parameterization
8.6.2.Results
8.6.3.Discussion
9.Research Synthesis
9.1.Introduction
9.2.Three-level Mixed-effects Regression Models
9.2.1.Three-level Linear Mixed Model
9.2.1.1.Illustration: Efficacy of Antidepressants
9.2.2.Three-level Non-linear Mixed Model
9.2.3.Three-level Logistic Regression Model for Dichotomous Outcomes
9.2.3.1.Illustration: Safety of Antidepressants
10.Analysis of Medical Claims Data
10.1.Introduction
10.2.Administrative Claims
10.3.Observational Data
10.4.Experimental Strategies
10.4.1.Case-control Studies
10.4.2.Cohort Studies
10.4.3.Within-subject Designs
10.4.3.1.Self-controlled Case Series
10.4.4.Between-subject Designs
Note continued: 10.5.Statistical Strategies
10.5.1.Fixed-effects Logistic and Poisson Regression
10.5.2.Mixed-effects Logistic and Poisson Regression
10.5.3.Sequential Testing
10.5.4.Discrete-time Survival Models
10.5.5.Stratified Cox Model
10.5.6.Between and Within Models
10.5.7.Fixed-effect versus Random-effect Models
10.6.Illustrations
10.6.1.Antiepileptic Drugs and Suicide
10.6.2.Description of the Data, Cohort, and Key Design and Outcome Variables
10.6.3.Statistical Methods
10.6.4.Between-subject Analyses
10.6.5.Within-subject Analysis
10.6.6.Discrete-time Analysis
10.6.7.Propensity Score Matching
10.6.8.Self-controlled Case Series and Poisson Hybrid Models
10.6.9.Marginal Structural Models
10.6.10.Stratified Cox and Random-effect Survival Models
10.6.11.Conclusion
10.7.Conclusion
11.Methods to be Avoided
11.1.Introduction
11.2.Spontaneous Reports
11.3.Vote Counting
Note continued: 11.4.Simple Pooling of Studies
11.5.Including Randomized and Non-randomized Trials in Meta-analysis
11.6.Multiple Comparisons and Biased Reporting of Results
11.7.Immortality Time Bias
12.Summary and Conclusions
12.1.Final Thoughts.
Edition Notes
"A Chapman & Hall book."
Includes bibliographical references (page 255-273) and index.
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