Quick Stats: Basics for Medical Literature Evaluation

Quick Stats: Basics for Medical Literature Evaluation
by Charles Herring, PharmD, BCPS, CPP

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About this book This book is a primer for basic medical literature evaluation. It is not intended to be used as a complete critical literature evaluation text. It is simply a “survival guide” for students and clinicians trying to learn the basics of literature evaluation. About the author Dr Herring graduated from the University of North Carolina in 1992 with a Bachelor of Science in Pharmacy and in 1994 with a Doctor of Pharmacy. His post-doctoral residency in pharmacy practice was completed at the Medical University of South Carolina in Charleston, South Carolina in 1995. Dr. Herring is currently an Assistant Professor of Pharmacy Practice at the Campbell University School of Pharmacy in Buies Creek, North Carolina. He is also the Adult Medicine Team Clinical Pharmacist Practitioner (CPP) for the Downtown Health Plaza of Wake Forest University Baptist Medical Center in Winston-Salem, North Carolina.
Table of Contents Basic Concepts Sample vs Population Randomization Variables Random Independent Dependent Analysis Univariable Bivariable Multivariable Types of Data Non-parametric Nominal Ordinal Parametric Interval Ratio Distributions Normal Bimodal Rectangular Skewed Positive Skew Negative Skew Kurtosis Descriptive Statistics Measures of Central Tendency Mean Median Mode Gaussian or Normally distributed data graph Positively skewed data graph Negatively skewed data graph Measures of Variability Range Interquartile Range Variance Standard Deviation Coefficient of Variation Standard Error of the Mean (aka standard error) Confidence Intervals Interpretation of Superiority Traits Interpretation for Differences Interpretation for Ratios Interpretation of Equivalence Trials Interpretation of Non-Inferiority Trial Sensitivity, Specificity, Predictive Value, and Hypothesis Testing Sensitivity Specificity Positive Predictive Value Negative Predictive Value Prevalence Incidence Hypothesis Testing Null Hypothesis Alternative Hypothesis Type 1 or Alpha Error p-value Type 2 or Beta Error Power Sample size calculations Correcting for estimated Drop-out Rate Delta One-tailed vs Two-tailed tests Effect of change in Alpha Effect of change in Beta Effect of change in Delta Effect of One-tailed vs Two-tailed design Effect of change in expected incidence Statistical Inference Techniques for Hypothesis Testing w/ Parametric Data T-test Non-Paired Paired One-Tailed Two-Tailed Bonferroni Dunn-Sidak ANOVA ANOVAs for Non-Paired samples 1-way ANOVA Multifactorial ANOVAs 2-way 3-way 4-way etc ANOVAs for Paired samples Repeated Measures ANOVA 2-way Repeated Measures ANOVA Repeated Measures Regression Multiple Comparison Methods (Post Hoc Analysis) Least Significant Difference Layer (aka stepwise) Methods Newman-Keuls Duncan Experiment-wise Methods Dunnett Dunn Tukey Scheffe Statistical Inference Techniques for Hypothesis Testing w/ Non-Parametric Data Non-Parametric Tests for Nominal Data Chi-Square Tests Test of Independence Goodness of Fit McNemar Chi-Square Mantel-Haenszel Chi-Square Log-Linear Analysis Post Hoc Analysis Fisher's Exact Rows by Columns Test Table Noting Proper Test to Use Non-Parametric Tests for Ordinal Data Mann Whitney U and Wilcoxon Rank Sum Kolmogorov-Smirnov Wilcoxon Signed Rank Kruskal-Wallis Friedman Table Noting Proper Test to Use Correlation and Regression Correlation r-value population correlation coefficient coefficient of determination (r2) sample correlation coefficient Regression Multiple regression Linear regression Logistic regression Cox Proportion Hazards Error vs Bias Bias in the Publication Process Funding Sources Publication Negative Study Investigator Reviewer Small study Compliance Prevalence Selection Bias Inclusion in study Participation in study Admission rate Nonresponse Membership Diagnostic or Detection Insensitive Measure Procedure Bias that falsely identify or inflate risk factors Recall Observer Interviewer Misclassification Random Misclassification Channeling Controlling for Bias Design of Trial Randomization Selection Criteria Blinding Matching Using Controls Analysis Controlled vs Non-Controlled Various Types of Controls Placebo Historical Crossover Standard or Active Treatment Within Patient Comparison Blinding in Trial Design Non-blinded Single-blinded Double-blinded Double-dummy Triple-blind Total-blind Note concerning trials that claim blinding, but may not be blinded. Run-In-Phase Confounding Arrow Chart Examples Co-linearity Controlling for Confounding Design of Trial Randomization Selection Criteria Blinding Matching Analysis Stratification Multivariate Regression Analysis Note concerning various types of ANOVA used Validity Internal External Prospective vs Retrospective General Considerations for Assessing Causality Relative strength of causal relationships Retrospective Trials Case-Control Benefits Problems Cohorts Retrospective Cohort Prospective Cohort (retrospective) True Prospective Cohort Benefits Problems Odds Ratios, Relative Risks (aka Risk Ratios, Rate Ratio, or Incidence Rate Ratio) Odds Ratio (see below for further explanation of examples) Relative Risk (see below for further explanation of examples) Hazard Ratio Interpretation Absolute Risk Absolute Risk Reduction or Increase Numbers Needed to Treat or Harm Relative Risk Examples for RCTs and Cohort Trials Example 1: Relative Risk Reduction example w/ Calculations and Interpretations Absolute Risk Absolute Risk Reduction Numbers Needed to Treat Relative Risk Relative Risk Reduction Difference vs Cohort Trial Example 2: Relative Risk Increase example with Calculations and Interpretations Absolute Risk Absolute Risk Increase Numbers Needed to Harm Relative Risk Relative Risk Increase Difference vs Cohort Trial Odds Ratio Examples for Case Control Trials Example 1 w/ Calculations and Interpretation Example 2 w/ Calculations and Interpretation Note concerning change in Incidence's effect on estimation of Relative Risk Randomized Controlled Trials Parallel Design Cross-over Design Considerations Disadvantages Various Types of Analyses Intention of Treat (ITT) As Treated Per Protocol Post Hoc Note concerning trials that claim to be ITT, but may not truly be ITT. Run-In-Phase Interim Analysis Subgroup Analysis Survival Analysis Hazard Life table Actuarial Method Kaplan-Meier Statistical tests Log-Rank Wilcoxon-type Cox Proportion Hazards Meta-Analysis Combined Endpoints Miscellaneous Breslow Day test Rule of Four Abbreviations References Index
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