## Ensk lýsing:

Score your highest in biostatistics Biostatistics is a required course for students of medicine, epidemiology, forestry, agriculture, bioinformatics, and public health. In years past this course has been mainly a graduate-level requirement; however its application is growing and course offerings at the undergraduate level are exploding. Biostatistics For Dummies is an excellent resource for those taking a course, as well as for those in need of a handy reference to this complex material.

Biostatisticians—analysts of biological data—are charged with finding answers to some of the world's most pressing health questions: how safe or effective are drugs hitting the market today? What causes autism? What are the risk factors for cardiovascular disease? Are those risk factors different for men and women or different ethnic groups? Biostatistics For Dummies examines these and other questions associated with the study of biostatistics.

## Annað

- Höfundur: John Pezzullo
- Útgáfa:1
- Útgáfudagur: 2013-06-28
- Hægt að prenta út 10 bls.
- Hægt að afrita 2 bls.
- Format:Page Fidelity
- ISBN 13: 9781118553954
- Print ISBN: 9781118553985
- ISBN 10: 1118553950

## Efnisyfirlit

- Title Page
- Copyright Page
- Table of Contents
- Introduction
- About This Book
- Conventions Used in This Book
- What You’re Not to Read
- Foolish Assumptions
- How This Book Is Organized
- Part I: Beginning with Biostatistics Basics
- Part II: Getting Down and Dirty with Data
- Part III: Comparing Groups
- Part IV: Looking for Relationships with Correlation and Regression
- Part V: Analyzing Survival Data
- Part VI: The Part of Tens

- Icons Used in This Book
- Where to Go from Here

- Part I: Beginning with Biostatistics Basics
- Chapter 1: Biostatistics 101
- Brushing Up on Math and Stats Basics
- Doing Calculations with the Greatest of Ease
- Concentrating on Clinical Research
- Drawing Conclusions from Your Data
- Statistical estimation theory
- Statistical decision theory

- A Matter of Life and Death: Working with Survival Data
- Figuring Out How Many Subjects You Need
- Getting to Know Statistical Distributions

- 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

- Focusing on Operations Found in Formulas
- Basic mathematical operations
- Powers, roots, and logarithms
- Factorials and 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

- Breaking Down the Basics of Mathematical Formulas
- 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
- Comparing odds versus probability

- Some Random Thoughts about Randomness
- Picking Samples from Populations
- Recognizing that sampling isn’t perfect
- Digging into probability distributions

- Introducing Statistical Inference
- Statistical estimation theory
- Statistical decision theory

- Homing 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

- Going Outside the Norm with Nonparametric Statistics

- Taking a Chance on Probability
- Chapter 4: Counting on Statistical Software
- Desk Job: Personal Computer Software
- Checking out commercial software
- Focusing on free software

- On the Go: Calculators and Mobile Devices
- Scientific and programmable calculators
- Mobile devices

- Gone Surfin’: Web-Based Software
- On Paper: Printed Calculators

- Desk Job: Personal Computer Software
- Chapter 5: Conducting Clinical Research
- Designing a Clinical Study
- Identifying aims, objectives, hypotheses, and variables
- Deciding who will be in the study
- Choosing the structure of the study
- Using randomization
- Selecting the analyses to use
- Defining analytical populations
- Determining how many subjects to enroll
- Putting together the protocol

- Carrying Out a Clinical Study
- Protecting your subjects
- Collecting and validating data

- Analyzing Your Data
- Dealing with missing data
- Handling multiplicity
- Incorporating interim analyses

- Designing a Clinical Study
- Chapter 6: Looking at Clinical Trials and Drug Development
- Not Ready for Human Consumption: Doing Preclinical Studies
- Testing on People during Clinical Trialsto Check a Drug’s Safety and Efficacy
- Phase I: Determining the maximum tolerated dose
- Phase II: Finding out about the drug’s performance
- Phase III: Proving that the drug works
- Phase IV: Keeping an eye on the marketed drug

- Holding Other Kinds of Clinical Trials
- Pharmacokinetics and pharmacodynamics (PK/PD studies)
- Bioequivalence studies
- Thorough QT studies

- Chapter 1: Biostatistics 101
- Part II: Getting Down and Dirty with Data
- Chapter 7: Getting Your Data into the Computer
- Looking at Levels of Measurement
- Classifying and Recording Different Kinds of Data
- Dealing with free-text data
- Assigning subject identification (ID) numbers
- Organizing name and address data
- Collecting categorical data
- Recording numerical data
- Entering date and time data

- Checking Your Entered Data for Errors
- Creating a File that Describes Your Data File

- Chapter 8: Summarizing and Graphing Your Data
- Summarizing and Graphing Categorical Data
- Summarizing Numerical Data
- Locating the center of your data
- Describing the spread of your data
- Showing the symmetry and shape of the distribution

- Structuring Numerical Summaries into Descriptive Tables
- Graphing Numerical Data
- Showing the distribution with histograms
- Summarizing grouped data withbars, boxes, and whiskers
- Depicting the relationships between numerical variables with other graphs

- Chapter 9: Aiming for Accuracy and Precision
- Beginning with the Basics of Accuracy and Precision
- Getting to know sample statistics and population parameters
- Understanding accuracy and precision in terms of the sampling distribution
- Thinking of measurement as a kind of sampling
- Expressing errors in terms of accuracy and precision

- Improving Accuracy and Precision
- Enhancing sampling accuracy
- Getting more accurate measurements
- Improving sampling precision
- Increasing the precision of your measurements

- Calculating Standard Errors for Different Sample Statistics
- A mean
- A proportion
- Event counts and rates
- A regression coefficient

- Beginning with the Basics of Accuracy and Precision
- Chapter 10: Having Confidence in Your Results
- Feeling Confident about Confidence Interval Basics
- Defining confidence intervals
- Looking at 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
- The confidence interval around a regression coefficient

- Relating Confidence Intervals and Significance Testing

- Feeling Confident about Confidence Interval Basics
- Chapter 11: Fuzzy In Equals Fuzzy Out: Pushing Imprecision through a Formula
- Understanding the Concept of Error Propagation
- Using Simple Error Propagation Formulas for Simple Expressions
- Adding or subtracting a constantdoesn’t change the SE
- Multiplying (or dividing) by a constant multiplies (or divides) the SE by the same amount
- For sums and differences: Add the squares of SEs together
- For averages: The square root law takes over
- For products and ratios: Squares of relative SEs are added together
- For powers and roots: Multiply the relative SE by the power

- Handling More Complicated Expressions
- Using the simple rules consecutively
- Checking out an online calculator
- Simulating error propagation — easy, accurate, and versatile

- Chapter 7: Getting Your Data into the Computer
- Part III: Comparing Groups
- Chapter 12: Comparing Average Values between Groups
- Knowing That Different Situations Need Different Tests
- Comparing the mean of a group of numbers to a hypothesized value
- Comparing two groups of numbers
- Comparing three or more groups of numbers
- Analyzing data grouped on several different variables
- Adjusting for a “nuisance variable” when comparing numbers
- Comparing sets of matched numbers
- Comparing within-group changes between groups

- Trying the Tests Used for Comparing Averages
- Surveying Student t tests
- Assessing the ANOVA
- Running Student t tests and ANOVAs from summary data
- Running nonparametric tests

- Estimating the Sample Size You Need for Comparing Averages
- Simple formulas
- Software and web pages
- A sample-size nomogram

- Knowing That Different Situations Need Different Tests
- Chapter 13: Comparing Proportions and Analyzing Cross-Tabulations
- Examining Two Variables with the Pearson Chi-Square Test
- Understanding how the chi-square test works
- 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
- Analyzing Ordinal Categorical Data with the Kendall Test
- Studying Stratified Data with the Mantel-Haenszel Chi-Square Test

- Examining Two Variables with the Pearson Chi-Square Test
- Chapter 14: Taking a Closer Look at Fourfold Tables
- Focusing on the Fundamentals of Fourfold Tables
- Choosing the Right Sampling Strategy
- Producing Fourfold Tables in a Variety of Situations
- Describing the association between two binary variables
- Assessing risk factors
- Evaluating diagnostic procedures
- Investigating treatments
- Looking at inter- and intra-rater reliability

- Chapter 15: 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
- Comparing incidences with the rate ratio
- Calculating confidence intervals for a rate ratio
- Comparing two event rates
- Comparing two event counts with identical exposure

- Estimating the Required Sample Size

- Understanding Incidence and Prevalence
- Chapter 16: Feeling Noninferior (Or Equivalent)
- Understanding the Absence of an Effect
- Defining the effect size: How different are the groups?
- Defining an important effect size: How close is close enough?
- Recognizing effects: Can you spot a difference if there really is one?

- Proving Equivalence and Noninferiority
- Using significance tests
- Using confidence intervals
- Some precautions about noninferiority testing

- Understanding the Absence of an Effect

- Chapter 12: Comparing Average Values between Groups
- Part IV: Looking for Relationships with Correlation and Regression
- Chapter 17: Introducing Correlation and Regression
- Correlation: How Strongly Are Two Variables Associated?
- Lining up the Pearson correlation coefficient
- Analyzing correlation coefficients

- Regression: What Equation Connects the Variables?
- Understanding the purpose of regression analysis
- Talking about terminology and mathematical notation
- Classifying different kinds of regression

- Correlation: How Strongly Are Two Variables Associated?
- Chapter 18: Getting Straight Talk on Straight-Line Regression
- Knowing When to Use Straight-Line Regression
- Understanding the Basics of Straight-Line Regression
- Running a Straight-Line Regression
- Taking a few basic steps
- Walking through an example

- Interpreting the Output of Straight-Line Regression
- Seeing what you told the program to do
- Looking at residuals
- Making your way through the regression table
- Wrapping up with measures of goodness-of-fit
- Scientific fortune-telling with the prediction formula

- Recognizing What Can Go Wrong with Straight-Line Regression
- Figuring Out the Sample Size You Need

- Chapter 19: More of a Good Thing: Multiple Regression
- Understanding the Basics of Multiple Regression
- Defining a few important terms
- Knowing when to use multiple regression
- Being aware of how the calculations work

- Running Multiple Regression Software
- Preparing categorical variables
- Recoding categorical variables as numerical
- Creating scatter plots before you jumpinto your multiple regression
- Taking a few steps with your software

- Interpreting the Output of a Multiple Regression
- Examining typical output from most programs
- Checking out optional output available from some programs
- Deciding whether your data is suitable for regression analysis
- Determining how well the model fits the data

- Watching Out for Special Situations that Arise in Multiple Regression
- Synergy and anti-synergy
- Collinearity and the mystery ofthe disappearing significance

- Figuring How Many Subjects You Need

- Understanding the Basics of Multiple Regression
- Chapter 20: A Yes-or-No Proposition: Logistic Regression
- Using Logistic Regression
- Understanding the Basics of Logistic Regression
- Gathering and graphing your data
- Fitting a function with an S shape to your data
- Handling multiple predictors in your logistic model

- Running a Logistic Regression with Software
- Interpreting the Output of Logistic Regression
- Seeing summary information about the variables
- Assessing the adequacy of the model
- Checking out the table of regression coefficients
- Predicting probabilities with the fitted logistic formula
- Making yes or no predictions

- Heads Up: Knowing What Can Go Wrong with Logistic Regression
- Don’t fit a logistic function to nonlogistic data
- Watch out for collinearity and disappearing significance
- Check for inadvertent reverse-coding of the outcome variable
- Don’t misinterpret odds ratios for numericalpredictors
- Don’t misinterpret odds ratios for categorical predictors
- Beware the complete separation problem

- Figuring Out the Sample Size You Need for Logistic Regression

- Chapter 21: Other Useful Kinds of Regression
- Analyzing Counts and Rates with Poisson Regression
- Introducing the generalized linear model
- Running a Poisson regression
- Interpreting the Poisson regression output
- Discovering other things that Poisson regression can do

- Anything Goes with Nonlinear Regression
- Distinguishing nonlinear regression from other kinds
- Checking out an example from drug research
- Running a nonlinear regression
- Interpreting the output
- Using equivalent functions to fit the parameters you really want

- Smoothing Nonparametric Data with LOWESS
- Running LOWESS
- Adjusting the amount of smoothing

- Analyzing Counts and Rates with Poisson Regression

- Chapter 17: Introducing Correlation and Regression
- Part V: Analyzing Survival Data
- Chapter 22: Summarizing and Graphing Survival Data
- Understanding the Basics of Survival Data
- Knowing that survival times are intervals
- Recognizing that survival times aren’t normally distributed
- Considering censoring

- Looking at the Life-Table Method
- Making a life table
- Interpreting a life table
- Graphing hazard rates and survival probabilities from a life table

- Digging Deeper with the Kaplan-Meier Method
- Heeding a Few Guidelines for Life Tables and the Kaplan-Meier Method
- Recording survival times the right way
- Recording censoring information correctly
- Interpreting those strange-looking survivalcurves

- Doing Even More with Survival Data

- Understanding the Basics of Survival Data
- Chapter 23: Comparing Survival Times
- Comparing Survival between Two Groups with the Log-Rank Test
- Understanding what the log-rank test is doing
- Running the log-rank test on software
- Looking at the calculations
- Assessing the assumptions

- Considering More Complicated Comparisons
- Coming Up with the Sample Size Needed for Survival Comparisons

- Comparing Survival between Two Groups with the Log-Rank Test
- Chapter 24: Survival Regression
- Knowing When to Use Survival Regression
- Explaining the Concepts behind Survival Regression
- The steps of Cox PH regression
- Hazard ratios

- Running a Survival Regression
- Interpreting the Output of a Survival Regression
- Testing the validity of the assumptions
- Checking out the table of regression coefficients
- Homing in on hazard ratios and their confidence intervals
- Assessing goodness-of-fit and predictive ability of the model
- Focusing on baseline survival and hazard functions

- How Long Have I Got, Doc? Constructing Prognosis Curves
- Running the proportional-hazards regression
- Finding h

- Estimating the Required Sample Size for a Survival Regression

- Chapter 22: Summarizing and Graphing Survival Data
- Part VI: The Part of Tens
- Chapter 25: Ten Distributions Worth Knowing
- The Uniform Distribution
- The Normal Distribution
- The Log-Normal Distribution
- The Binomial Distribution
- The Poisson Distribution
- The Exponential Distribution
- The Weibull Distribution
- The Student t Distribution
- The Chi-Square Distribution
- The Fisher F Distribution

- Chapter 26: Ten Easy Ways to Estimate How Many Subjects You Need
- Comparing Means between Two Groups
- Comparing Means among Three, Four, or Five Groups
- Comparing Paired Values
- Comparing Proportions between Two Groups
- Testing for a Significant Correlation
- Comparing Survival between Two Groups
- Scaling from 80 Percent to Some Other Power
- Scaling from 0.05 to Some Other Alpha Level
- Making Adjustments for Unequal Group Sizes
- Allowing for Attrition

- Chapter 25: Ten Distributions Worth Knowing
- Index
- EULA

## UM RAFBÆKUR Á HEIMKAUP.IS

Bókahillan þín er þitt svæði og þar eru bækurnar þínar geymdar. Þú kemst í bókahilluna þína hvar og hvenær sem er í tölvu eða snjalltæki. Einfalt og þægilegt!**Þú kemst í bækurnar hvar sem er**

Þú getur nálgast allar raf(skóla)bækurnar þínar á einu augabragði, hvar og hvenær sem er í bókahillunni þinni. Engin taska, enginn kyndill og ekkert vesen (hvað þá yfirvigt).

**Auðvelt að fletta og leita**

Þú getur flakkað milli síðna og kafla eins og þér hentar best og farið beint í ákveðna kafla úr efnisyfirlitinu. Í leitinni finnur þú orð, kafla eða síður í einum smelli.

**Glósur og yfirstrikanir**

Þú getur auðkennt textabrot með mismunandi litum og skrifað glósur að vild í rafbókina. Þú getur jafnvel séð glósur og yfirstrikanir hjá bekkjarsystkinum og kennara ef þeir leyfa það. Allt á einum stað.

**Hvað viltu sjá? / Þú ræður hvernig síðan lítur út**

Þú lagar síðuna að þínum þörfum. Stækkaðu eða minnkaðu myndir og texta með multi-level zoom til að sjá síðuna eins og þér hentar best í þínu námi.

**Fleiri góðir kostir**

- Þú getur prentað síður úr bókinni (innan þeirra marka sem útgefandinn setur)

- Möguleiki á tengingu við annað stafrænt og gagnvirkt efni, svo sem myndbönd eða spurningar úr efninu

- Auðvelt að afrita og líma efni/texta fyrir t.d. heimaverkefni eða ritgerðir

- Styður tækni sem hjálpar nemendum með sjón- eða heyrnarskerðingu

**Eiginleikar**