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Biostatistics For Dummies

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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
    • 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
    • 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
    • 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
    • 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
  • 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
    • 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
    • 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
  • 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
    • 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
    • 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
    • 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
  • 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
    • 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
    • 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
  • 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
    • 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
    • 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
  • 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
  • Index
  • EULA

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Vörumerki: Dummies Series
Vörunúmer: 9781118553954
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Biostatistics For Dummies

Vörumerki: Dummies Series
Vörunúmer: 9781118553954
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Veldu vöru

1.640 kr.
Get the product now
1.640 kr.