Biostatistics for Biomedical Research

Author
Affiliation

Department of Biostatistics
School of Medicine
Vanderbilt University

Published

September 22, 2023

flowchart LR
Q[Research<br>Question] --> M[Measurements] --> D[Design] --> Ac[Data<br>Acquisition] --> Des[Description] --> A[Analysis] --> I[Interpretation] & Pred[Prediction]
Pred --> V[Validation]
I --> K[New Knowledge] & Dec[Decisions]

Preface

The book is aimed at exposing biomedical researchers to modern biostatistical methods and statistical graphics, highlighting those methods that make fewer assumptions, including nonparametric statistics and robust statistical measures. In addition to covering traditional estimation and inferential techniques, the course contrasts those with the Bayesian approach, and also includes several components that have been increasingly important in the past few years, such as challenges of high-dimensional data analysis, modeling for observational treatment comparisons, analysis of differential treatment effect (heterogeneity of treatment effect), statistical methods for biomarker research, medical diagnostic research, and methods for reproducible research. A glossary of statistical terms for non-statisticians is here. R Workflow is a useful companion to this book, especially for those needing to manipulate data in preparation for analysis and for those interested in embedding statistical analyses in state-of-the-art reproducible reports.

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Symbols Used in the Right Margin of the Text

  • Blue symbols in the right margin starting with ABD designate section numbers (and occasionally page numbers preceeded by \(p\)) in The Analysis of Biological Data, Second Edition by MC Whitlock and D Schluter, Greenwood Village CO, Roberts and Company, 2015.
  • Right blue symbols starting with RMS designate section numbers in Regression Modeling Strategies, 2nd ed. by FE Harrell, Springer, 2015.
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Other Information

Acknowledgement

This material grew largely out of teaching clinical scholars and in Master of Science in Clinical Investigation programs at Duke University, University of Virginia, and Vanderbilt University. I benefitted immensely from lecture notes from colleagues such as Kerry Lee of Duke University. Thanks also goes to Vanderbilt Biostatistics colleague James C. Slaughter who made several contributions to an earlier version of the book at hbiostat.org/doc/bbr.pdf.

Date Sections Changes Thanks To
2023-09-22 20.3.4 New section on bootstrapping importantance ranks using one-at-a-time feature modeling
2023-09-16 20.3.3 New section on estimation of correlation matrices
2023-07-28 7.9 New section on using models for paired data
2023-07-26 7.8 Added example of ordinal model for 2-way ANOVA
2023-06-22 13 Added big picture
2023-06-16 13.5 Added more to section on how many covariates to add
2023-04-27 7.12 New section on sample size for ECDF
2023-04-05 14.4.2 Added confidence bands
2023-03-30 20.3.1 Fixed bug in simulation graphics
2023-03-29 3.2.1 New link to clinical trial design resource
2023-03-13 21 New subsection on the decline effect
2023-02-19 3.9 Added link to resources for learning probability
2022-12-29 4.3.5 Added single-axis nomogram example
2022-12-28 Started to add old study questions to end of selected chapters
2022-12-03 14.4.2 New section with real example of misleading change score
2022-11-27 14.4.4 New section on importance of current status vs. baseline status and irrelevance of change for patients
2022-08-02 19 Quote about weaknesses in sens and spec; link to CrossValidated discussion
2022-08-31 8.5.2 New material on sample size vs. P(correct sign on r)