Statistical Rules of Thumb, 2nd Edition

by Gerald van Belle (Univ.of Washington, Seattle, Wa )

• ISBN13: 9780470377956
• ISBN10: 047037795X
• Format: eBook
• Copyright: 2008-09-01
• Publisher: Wiley-Interscience

Summary

Statistical Rules of Thumb, Second Edition compiles simple rules that are widely applicable, robust, and elegant, and each captures key statistical concepts. This handbook provides a framework for considering statistical questions such as sample size and design of experiments. Explaining the justification for each rule, this book conveys the various possibilities that statisticians must think of when designing and conducting a study or analyzing its data. It provides a framework for considering such aspects of statistical work such as: randomness and statistical models; sample size; covariation; epidemiology; environmental studies; designing, conducting, and analyzing studies; words, tables, and graphs; and consulting. New rules of thumb are included such as: Sample size for ratios of means; Very non-significant P-values are very significant; Dichotomize continuous variables for odds ratio analysis; and Correlations need to be substantial to gain advantage in ANCOVA. Some rules have been revised for the new edition, i.e. sample size for relative risk and sample size for percentage change. In addition, the references have been completed updated and expanded. A related website www.vanbelle.org provides additional rules, author presentations and more.

Table of Contents

Preface to the Second Edition

Preface to the First Edition

Acronyms

The Basics.

Four Basic Questions

Observation is Selection

Replicate to Characterize Variability

Variability Occurs at Multiple Levels

Invalid Selection is the Primary Threat to Valid Inference

There is Variation in Strength of Inference

Distinguish Randomized and Observational Studies

Beware of Linear Models

Keep Models As Simple As Possible, But Not More Simple

Understand Omnibus Quantities

Do Not Multiply Probabilities More Than Necessary

Use Two-sided p-Values

p-Values for Sample Size, Confidence Intervals for Results

At Least Twelve Observations for a Confidence Interval

Estimate ± Two Standard Errors is Remarkably Robust

Know the Unit of the Variable

Be Flexible About Scale of Measurement Determining Analysis

Be Eclectic and Ecumenical in Influence

Sample Size.

Begin with a Basic Formula for Sample Size-LehrÆs Equation

Calculating Sample Size Using the Coefficient of Variation

No Finite Population Correction for Survey Sample Size

Standard Deviation and Sample Range

Do Not Formulate a Study Solely in Terms of Effect Size

Overlapping Confidence Intervals Do Not Imply Nonsignificance

Sample Size Calculation for the Poisson Distribution

Sample Size for Poisson with Background Rate

Sample Size Calculation for the Binomial Distribution

When Unequal Sample Sizes Matters; When They DonÆt

Sample Size With Different Costs for the Two Samples

The Rule of Threes for 95% Upper Bounds When There Are No Events

Sample Size Calculations Are Determined by the Analysis

Observational Studies.

The Model for an Observational Study is the Sample Survey

Large Sample Size Does Not Guarantee Validity

Good Observational Studies Are Designed

To Establish Cause Effect Requires Longitudinal Data

Make Theories Elaborate to Establish Cause and Effect

The Hill Guidelines Are a Useful Guide to Show Cause Effect

Sensitivity Analyses Assess Models Uncertainty and Missing Data

Covariation.

Assessing and Describing Covariation

DonÆt Summarize Regression Sampling Schemes

Do Not Correlate Rates or Ratios Indiscriminately

Determining Sample Size to Estimate a Correlation

Pairing Data is not Always Good

Go Beyond Correlation in Drawing Conclusions

Agreement As Accuracy, Scale Differential, and Precision

Assess Test Reliability by Means of Agreement

Range of the Predictor Variable and Regression

Measuring Change: Width More Important than Numbers

Environmental Studies.

Begin with the Lognormal Distributions in Environmental Studies

Differences Are More Symmetrical

Know the Sample Space for Statements of Risk

Beware of Pseudoreplication

Think Beyond Simple Random Sampling

The Size of the Population and Small Effects

Models of Small Effects Are Sensitive to Assumptions

Distinguish Between Variability and Uncertainty

Description of the Database is As Important as Its Data

Always Assess the Statistical Basis for an Environmental Standard

Measurement of a Standard and Policy

Parametric Analyses Make Maximum Use of the Data

Confidence, Prediction, and Tolerance Intervals

Statistics and Risk Assessment

Exposure Assessment is the Weak Link in Assessing Health Effects of Pollutants

Assess the Errors in Calibration Due to Inverse Regression

Epidemiology.

Start with the Poisson to Model Incidence or Prevalence

The Odds Ratio Approximates the Relative Risk Assuming the Disease is Rare

The Number of Events is Crucial in Estimating Sample Size

Use a Logarithmic Formulation to Calculate Sample Size

Take No More than Four or Five Controls per Case

Obtain at Least Ten Subjects for Every Variable Investigated

Begin with Two Exponential Distribution to Model Time to Event

Begin with Two Exponentials for Comparing Survival Times

Be Wary of Surrogates

Prevalence Dominates in Screening Rare Diseases

Do Not Dichotomize Unless Absolutely Necessary

Additive and Multiplicative Models

Evidence-Based Medicine.

Strength of Evidence

Relevance of Information: POEM vs. DOE

Begin with Absolute Risk Reduction, then follow with Relative Risk

The Number Needed to Treat (NNT) is Clinically Useful

Variability in Response to Treatment Needs to be Considered

Safety is the Weak Component of EBM

Intent to Treat is the Default Analysis

Use Prior Information but not Priors

The Four Key Questions for Meta-analysis

Design, Conduct, and Analysis.

Randomization Puts Systematic Effects into the Error Term

Blocking is the Key to Reducing Variability

Factorial Designs and Joint Effects

High-Order Interactions Occur Rarely

Balanced Designs Allow Easy Assessment of Joint Effects

Analysis Follows Designs

Independence, Equal Variance, and Normality

Plan to Graph the Results of an Analysis

Distinguish Between Design Structure and Treatment Structure

Make Hierarchical Analyses the Default Analysis

Distinguish Between Nested and Crossed Designs-Not Always Easy

Plan for Missing Data

Address Multiple Comparisons Before Starting the Study

Know Properties Preserved When Transforming Units

Consider Bootstrapping for Complex Relationships

Words, Tables, and Graphs.

Use Text for a Few Numbers, Tables for Many Numbers, Graphs and Complex Relationships

Arrange Information in a Table to Drive Home the Message

Always Graph the Data

Always Graph Results of An Analysis of Variance

Never Use a Pie Chart

Bar Graphs Waste Ink; They DonÆt Illuminate Complex Relationships

Stacked Bar Graphs Are Worse Than Bar Graphs

Three-Dimensional Bar Graphs Constitute Misdirected Artistry

Identify Cross-sectional and Longitudinal Patterns in Longitudinal Data

Use Rendering, Manipulation, and Linking in High-Dimensional Data

Consulting.

Session Has Beginning, Middle, and End

Ask Questions

Make Distinctions

Know Yourself, Know the Investigator

Tailor Advice to the Level of the Investigator

Use Units the Investigator is Comfortable With

Agee on Assignment of Responsibilities

Any Basic Statistical Computing Package Will Do

Ethics Precedes, Guides, and Follows Consultation

Be Proactive in Statistical Consulting

Use the Web for Reference, Resource, and Education

Listen to, and Heed the Advice of Experts in the Field

Epilogue

Reference

Author Index

Topic Index

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