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Statistics at Square Two Understanding Modern Statistical Applications in Medicine 2nd Edition by Michael J Campbell ISBN 1405134909 9781405134903

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Statistics at Square Two Understanding Modern Statistical Applications in Medicine 2nd Edition Michael J. Campbell Digital Instant Download

Author(s): Michael J. Campbell
ISBN(s): 9781405134903, 1405134909
Edition: 2
File Details: PDF, 1.73 MB
Year: 2006
Language: english
SKU: EB-1377636 Category: Tag:

Statistics at Square Two Understanding Modern Statistical Applications in Medicine 2nd Edition by Michael J Campbell – Ebook PDF Instant Download/Delivery: 1405134909, 9781405134903
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Product details:

ISBN 10: 1405134909 
ISBN 13: 9781405134903
Author: Michael J Campbell

Statistics at Square Two, Second Edition, helps you evaluate the many statistical methods in current use. Going beyond the basics of SS1, it covers sophisticated methods and highlights misunderstandings. Easy to read, it includes annotated computer outputs and keeps formulas to a minimum.

Worked examples of methods such as multiple and logical regression reinforce the text. Each chapter concludes with exercises to stimulate learning.

All those who need to understand statistics in clinical research papers and apply them in their own research will value this compact and coherent guide.

Statistics at Square Two Understanding Modern Statistical Applications in Medicine 2nd Table of contents:

Part I: Introduction to Medical Statistics

  1. Introduction: The Role of Statistics in Modern Medicine

    • The Importance of Evidence-Based Medicine

    • A Brief History of Statistical Applications in Medicine

    • Key Statistical Concepts and Terminology

  2. Fundamental Statistical Principles in Medicine

    • Types of Data and Measurement Scales

    • Descriptive Statistics in Medical Research

    • Inferential Statistics: From Samples to Populations

  3. The Scientific Method and Hypothesis Testing

    • Formulating Research Questions and Hypotheses

    • P-values, Significance Levels, and Confidence Intervals

    • Types of Errors: Type I and Type II

  4. Data Collection and Study Design in Medical Research

    • Observational vs. Experimental Studies

    • Randomized Controlled Trials (RCTs)

    • Cohort, Case-Control, and Cross-Sectional Studies

Part II: Statistical Techniques in Medical Research

  1. Regression Analysis in Medicine

    • Simple Linear Regression and Prediction Models

    • Multiple Regression and Adjusting for Confounders

    • Logistic Regression and Odds Ratios in Clinical Studies

  2. Analysis of Variance (ANOVA) and Non-Parametric Methods

    • One-Way and Two-Way ANOVA

    • Assumptions of ANOVA and Alternatives

    • Non-Parametric Tests: When to Use Them

  3. Survival Analysis and Time-to-Event Data

    • The Kaplan-Meier Estimator and Survival Curves

    • Cox Proportional Hazards Model

    • Competing Risks and Censoring in Medical Studies

  4. Multivariate and Advanced Statistical Techniques

    • Factor Analysis and Principal Component Analysis

    • Multivariate Analysis of Covariance (MANCOVA)

    • Structural Equation Modeling (SEM) in Medicine

Part III: Statistical Applications in Clinical Medicine

  1. Epidemiology and Public Health Statistics

    • Epidemiological Measures: Incidence, Prevalence, and Risk Ratios

    • The Role of Bias and Confounding in Epidemiological Studies

    • Statistical Tools for Public Health Research

  2. Clinical Trial Design and Analysis

    • Key Design Considerations: Randomization, Blinding, and Sample Size

    • Analyzing Clinical Trial Data: Intention-to-Treat vs. Per Protocol

    • Handling Missing Data and Dropouts in Clinical Trials

  3. Genetic and Genomic Data in Medicine

    • Statistical Approaches to Genetic Association Studies

    • Genome-Wide Association Studies (GWAS)

    • Bioinformatics and High-Dimensional Data Analysis

  4. Precision Medicine and Personalized Healthcare

    • Statistical Methods in Genomic Medicine

    • Risk Prediction Models and Their Clinical Application

    • Ethical and Statistical Challenges in Precision Medicine

Part IV: Statistical Challenges and Emerging Areas in Medical Research

  1. Dealing with Big Data in Medicine

    • High-Dimensional Data and Machine Learning Techniques

    • Integrating Electronic Health Records and Statistical Models

    • Challenges in Data Cleaning and Validation

  2. Statistical Methods for Longitudinal Data

    • Repeated Measures and Growth Curve Modeling

    • Handling Missing Data in Longitudinal Studies

    • Advanced Methods: Mixed-Effects Models and Generalized Estimating Equations (GEE)

  3. Statistical Modelling in Real-World Data and Health Economics

    • Real-World Evidence and Observational Data

    • Cost-Effectiveness Analysis in Medical Decision Making

    • Bayesian Statistics in Health Economics

Part V: Ethical, Legal, and Practical Considerations

  1. Ethical Issues in Medical Statistics

    • Ensuring Data Integrity and Reproducibility

    • Informed Consent and Ethical Concerns in Data Collection

    • Statistical Transparency and Avoiding Misleading Conclusions

  2. The Role of Statistics in Medical Decision Making

    • Evidence-Based Medicine and Decision Support Tools

    • Risk Communication and Clinical Guidelines

    • The Impact of Statistical Literacy on Patient Care

  3. Challenges in Reporting and Interpreting Statistical Results

    • Common Pitfalls in Statistical Analysis

    • Communicating Results to Non-Statistical Audiences

    • The Role of Peer Review and Replication in Medical Statistics

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Tags: Michael J Campbell, Statistics, Medicine