Biostatistics for Dummies.

Format:

Book

Author/Creator:

Wahi, Monika.

Contributor:

Pezzullo, John C.

Series:

--For dummies.

For dummies

Language:

English

Subjects (All):

Biometry.

Physical Description:

1 online resource (403 pages)

Edition:

2nd ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

This comprehensive guide by Monika Wahi and John C. Pezzullo provides an accessible introduction to biostatistics for students and professionals in the medical and health sciences. It covers fundamental concepts, such as basic statistics, statistical software, and clinical research methodologies. The book also delves into data manipulation, summarization, and visualization techniques, as well as more advanced topics like regression analysis and epidemiologic inference. Designed for those with math anxiety, it aims to simplify complex mathematical expressions and statistical methods, making them approachable for a wide audience. The second edition offers updated content to reflect the latest advancements in biostatistics, with a focus on practical application in research settings. Generated by AI.

Contents:

Intro

Title Page

Copyright Page

Table of Contents

Introduction

About This Book

Foolish Assumptions

Icons Used in This Book

Beyond the Book

Where to Go from Here

Part 1 Getting Started with Biostatistics

Chapter 1 Biostatistics 101

Brushing Up on Math and Stats Basics

Doing Calculations with the Greatest of Ease

Concentrating on Epidemiologic Research

Drawing Conclusions from Your Data

Statistical estimation theory

Statistical decision theory

Comparing groups

Looking for relationships between variables

A Matter of Life and Death: Working with Survival Data

Getting to Know Statistical Distributions

Figuring Out How Many Participants You Need

Chapter 2 Overcoming Mathophobia: Reading and Understanding Mathematical Expressions

Breaking Down the Basics of Mathematical Formulas

Displaying formulas in different ways

Checking out the building blocks of formulas

Constants

Variables

Focusing on Operations Found in Formulas

Basic mathematical operations

Addition and subtraction

Multiplication

Division

Powers, roots, and logarithms

Raising to a power

Taking a root

Looking at logarithms

Factorials and absolute values

Factorials

Absolute values

Functions

Simple and complicated formulas

Equations

Counting on Collections of Numbers

One-dimensional arrays

Higher-dimensional arrays

Arrays in formulas

Sums and products of the elements of an array

Chapter 3 Getting Statistical: A Short Review of Basic Statistics

Taking a Chance on Probability

Thinking of probability as a number

Following a few basic rules of probabilities

Comparing odds versus probability

Some Random Thoughts about Randomness

Selecting Samples from Populations

Recognizing that sampling isn't perfect.

Digging into probability distributions

Distributions that describe your data

Distributions important to statistical testing

Introducing Statistical Inference

Accuracy and precision

Sampling distributions and standard errors

Confidence intervals

Honing In on Hypothesis Testing

Getting the language down

Testing for significance

Understanding the meaning of "p value" as the result of a test

Examining Type I and Type II errors

Grasping the power of a test

Power, sample size, and effect size relationships

How to do power calculations

Going Outside the Norm with Nonparametric Statistics

Part 2 Examining Tools and Processes

Chapter 4 Counting on Statistical Software

Considering the Evolution of Statistical Software

Comparing Commercial to Open-Source Software

Checking Out Commercial Software

SAS

SPSS

Microsoft Excel

Online analytics platforms

Focusing on Open-Source and Free Software

Open-source software

Other free statistical software

Software that performs many functions

Software for calculating sample size

Choosing Between Code-based and Non-Code-Based Methods

Storing Data in the Cloud

Chapter 5 Conducting Clinical Research

Designing a Clinical Trial

Identifying aims, objectives, hypotheses, and variables

Deciding who is eligible for the study

Choosing the structure of a clinical trial

Using randomization

Selecting the analyses to use

Determining how many participants to enroll in a clinical trial

Assembling the study protocol

Carrying Out a Clinical Trial

Protecting clinical trial participants

Surveying regulatory agencies

Working with Institutional Review Boards

Getting informed consent

Considering data safety monitoring boards and committees.

Getting certified in human subjects protection

Collecting and validating data

Analyzing Your Data

Dealing with missing data

Handling multiplicity

Chapter 6 Taking All Kinds of Samples

Making Forgivable (and Non-Forgivable) Errors

Framing Your Sample

Sampling for Success

Taking a simple random sample

Taking a stratified sample

Engaging in systematic sampling

Sampling clusters

Sampling at your convenience

Sampling in multiple stages

Chapter 7 Having Designs on Study Design

Presenting the Study Design Hierarchy

Describing what we see

Getting analytical

Going from observational to experimental

Climbing the Evidence Pyramid

Starting at the base: Expert opinion

Making the case with case studies

Making statements about the population

Going from case series to case-control

Following a cohort over time

Advancing to the clinical trial stage

Reaching the top: Systematic reviews and meta-analyses

Part 3 Getting Down and Dirty with Data

Chapter 8 Getting Your Data into the Computer

Looking at Levels of Measurement

Classifying and Recording Different Kinds of Data

Dealing with free-text data

Assigning participant study identification (ID) numbers

Organizing name and address data in the study ID crosswalk

Collecting categorical data in your research database

Carefully coding categories

Dealing with more than two levels in a category

Recording numerical data

Entering date and time data

Checking Your Entered Data for Errors

Creating a File that Describes Your Data File

Chapter 9 Summarizing and Graphing Your Data

Summarizing and Graphing Categorical Data

Summarizing Numerical Data

Locating the center of your data

Arithmetic mean

Median

Mode

Considering some other "means" to measure central tendency.

Describing the spread of your data

Standard deviation, variance, and coefficient of variation

Range

Centiles

Numerically expressing the symmetry and shape of the distribution

Skewness

Kurtosis

Structuring Numerical Summaries into Descriptive Tables

Graphing Numerical Data

Showing the distribution with histograms

Log-normal distributions

Summarizing grouped data with bars, boxes, and whiskers

Bar charts

Box-and-whiskers charts

Depicting the relationships between numerical variables with other graphs

Chapter 10 Having Confidence in Your Results

Feeling Confident about Confidence Interval Basics

Defining confidence intervals

Understanding and interpreting confidence levels

Taking sides with confidence intervals

Calculating Confidence Intervals

Before you begin: Formulas for confidence limits in large samples

The confidence interval around a mean

The confidence interval around a proportion

The confidence interval around an event count or rate

Relating Confidence Intervals and Significance Testing

Part 4 Comparing Groups

Chapter 11 Comparing Average Values between Groups

Grasping Why Different Situations Need Different Tests

Comparing the mean of a group of numbers to a hypothesized value

Comparing the mean of two groups of numbers

Comparing the means of three or more groups of numbers

Comparing means in data grouped on several different variables

Adjusting for a confounding variable when comparing means

Comparing means from sets of matched numbers

Comparing means of matched pairs

Using Statistical Tests for Comparing Averages

Surveying Student t tests

Understanding the general approach to a t test

Executing a t test

Interpreting the output from a t test

Assessing the ANOVA

Grasping how the ANOVA works.

Picking through post-hoc tests

Running an ANOVA

Interpreting the output of an ANOVA

Executing and interpreting post-hoc t tests

Running nonparametric tests

Estimating the Sample Size You Need for Comparing Averages

Using formulas for manual calculation

Software and web pages

Chapter 12 Comparing Proportions and Analyzing Cross-Tabulations

Examining Two Variables with the Pearson Chi-Square Test

Understanding how the chi-square test works

Calculating observed and expected counts

Summarizing and combining scaled differences

Determining the p value

Putting it all together with some notation and formulas

Pointing out the pros and cons of the chi-square test

Modifying the chi-square test: The Yates continuity correction

Focusing on the Fisher Exact Test

Understanding how the Fisher Exact test works

Noting the pros and cons of the Fisher Exact test

Calculating Power and Sample Size for Chi-Square and Fisher Exact Tests

Chapter 13 Taking a Closer Look at Fourfold Tables

Focusing on the Fundamentals of Fourfold Tables

Choosing the Correct Sampling Strategy

Producing Fourfold Tables in a Variety of Situations

Describing the association between two binary variables

Quantifying associations

Relative risk and the risk ratio

Odds ratio

Evaluating diagnostic procedures

Overall accuracy

Sensitivity and specificity

Positive predictive value and negative predictive value

Investigating treatments

Looking at inter- and intra-rater reliability

Chapter 14 Analyzing Incidence and Prevalence Rates in Epidemiologic Data

Understanding Incidence and Prevalence

Prevalence: The fraction of a population with a particular condition

Incidence: Counting new cases

Understanding how incidence and prevalence are related

Analyzing Incidence Rates.

Expressing the precision of an incidence rate.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Includes index.

ISBN:

9781394251483

1394251483

OCLC:

1477809595