Inference Principles for Biostatisticians

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A01=Ian C. Marschner
advanced probability concepts
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Author_Ian C. Marschner
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biostatistical theory
Biostatistics Methods
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Confidence Interval
Conjugate Prior Distribution
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Core Methodologies In Biostatistics
Credible Interval
Cumulative Distribution Function
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Exact Sampling Distribution
Generalized Linear Models
graduate level statistics
Graduate-Level Biostatistics Courses
health data analysis
High Salt Group
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Likelihood Function
Likelihood Ratio Test
Log Likelihood Difference
Log Likelihood Function
Longitudinal Methods
Minimal Sufficient
Minimal Sufficient Statistic
Non-informative Prior
Null Hypothesis
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Posterior Distribution
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Principles Of Statistical Inference
Prior Distribution
Profile Likelihood
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R Functions For Simulation
R simulation techniques
Random Variable T1
Randomized Trials
Sample Prevalence
Sampling Distribution
softlaunch
statistical inference for biomedical research
statistical inference methods
Stroke Incidence Rate
Survival Analysis
Test Statistic T1
Viral Load Reductions
Wald Test
Wilcoxon Signed Rank Test

Product details

  • ISBN 9781482222234
  • Weight: 530g
  • Dimensions: 156 x 234mm
  • Publication Date: 11 Dec 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Designed for students training to become biostatisticians as well as practicing biostatisticians, Inference Principles for Biostatisticians presents the theoretical and conceptual foundations of biostatistics. It covers the theoretical underpinnings essential to understanding subsequent core methodologies in the field.

Drawing on his extensive experience teaching graduate-level biostatistics courses and working in the pharmaceutical industry, the author explains the main principles of statistical inference with many examples and exercises. Extended examples illustrate key concepts in depth using a specific biostatistical context. In addition, the author uses simulation to reinforce the repeated sampling interpretation of numerous statistical concepts. Reducing the computational complexities, he provides simple R functions for conducting simulation studies.

This text gives graduate students with diverse backgrounds across the health, medical, social, and mathematical sciences a solid, unified foundation in the principles of statistical inference. This groundwork will lead students to develop a thorough understanding of biostatistical methodology.

Ian C. Marschner is head of the Department of Statistics and a professor of statistics at Macquarie University. He is also a professor of biostatistics in the National Health and Medical Research Council (NHMRC) Clinical Trials Centre at the University of Sydney. He has over 25 years of experience as a biostatistician working on health and medical research, particularly involving clinical trials and epidemiological studies of cardiovascular disease, cancer, and HIV/AIDS. He was previously director of the Asia Biometrics Centre with Pfizer and an associate professor of biostatistics at Harvard University.

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