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Alabama - Simple Descriptive Statistics George Howard, DrPH Infant mortality rates
Descriptive statistics
Analysis of variance
Histograms - intervals and scales
Measures of central tendency: mean, median
Measures of dispersion: range, percentile, standard deviation, variance
Outliers
Box and whisker plots
Skew
Alabama - Introduction to Random Variables George Howard, DrPH Dichotomous Outcome
Continuous outcome
Tossing coins
Logic and exclusion approach
Mathematical approach
Generalizations
Distributions
Binomial distribution
The Bell Shaped Curve
Alabama - Multivariate Statistics George Howard, DrPH Multivariate regression (joint effect)
Example 1: Age, SBP IMT, univariate, multivariable
Example 2: SBP and Age - confounding
Example 3: Age and SBP related to IMT - confounding
Example 4: Age and SBP inconsistently related to IMT - effect modification
Example 5: Both Age and SBP related to IMT - joint effects
Alabama - Sampling Distributions
George Howard, DrPH Estimation
Universe
Parameters
How to estimate parameters
Standard deviation
Reliability
Standard error
Confidence limits on the mean
Interpretation of the confidence limits on the mean
REGARDS Study data - mean
Binomial distribution
Standard error of a binomial
Alabama - Vocabulary of Uncertainty George Howard, DrPH Statistics = common sense;
Inference;
Sample;
Estimation;
Strength of Association;
Hypothesis Testing;
PValue;
Post-Hoc Relationships;
Error; PValue vs Power;
Confounder;
Fixing Confounders - Matching;
Confounders - What can go wrong?;
Confounders in RCT;
Selection of Statistical Tools;
Example Stat Tool - BP and Gender;
Example Stat Tool - BP and Age;
Types of Statistical Tests
Columbia - Statistics for the Basic Sciences Ivey Chen Assess data sources
probability and inference
deductive reasoning
systematic error
measurement error
hypothesis testing
type I and type II errors
confidence bounds
Duke - Evidence-Based Medicine Education Series Karen Pieper, MS Evidence-base medicine
Observational studies
Randomized clinical trials
Statistical issues in design of clinical trials
Randomized studies
Non-randomized studies
Randomization process
Endpoints
Multiple comparisons
Data validation
Modeling
Data safety monitoring boards
Duke - Lesson 1: Evidence-Based Medicine: The Basic Concepts Karen Pieper, MS, Robert A. Harrington, MD What is EBM?
Why does EBM matter?
Why is EBM important to drug development?
Observational studies
Randomized clinical trials
Types of trials
Concepts (randomization, sample sizes, etc.)
Regulatory issues on clinical trials
Duke - Lesson 2: Statistical Issues in the Design of a Trial, Part 1 Karen Pieper, MS Experimental design
Randomized
Nonrandomized studies
Simple randomization
Blocked randomization
Stratification
Randomization process
Group allocation design
Factorial design
Crossover design
Blinding
Phases of a study
Duke - Lesson 3: Statistical Issues in the Design of a Trial, Part 2 Karen Pieper, MS Endpoints
"Hard" versus "soft" endpoints
Timing of endpoints
Surrogate endpoints
Single versus composite endpoints
Intention to treat
Primary hypotheses
Secondary hypotheses
Multiple comparisons
Calculating sample size
Duke - Lesson 4: Interpretation of Medical Literature Statistics and Statistical Terms Karen Pieper, MS
Duke - Lesson 5: Statistical Methods for Analyses Karen Pieper, MS Data validation
Patients who do not meet eligibility criteria
Incorrect data
Missing data
Protocol violations
Modeling
C-index
Binary short-term outcomes
Binary long-term outcomes
Continuous outcomes
Parametric versus nonparametric tests
Independent versus dependent variables
Duke - Lesson 6: The Role of Independent Data Monitoring Karen Pieper, MS Data monitoring
Stopping rules
Data safety monitoring boards
Indiana - Analysis of Categorical Data Siu L. Hui, PhD Inference
Estimation
Hypothesis testing
Categorical variables
Binary variables
Examples of one-sample problems
Point estimate
Confidence interval
Example: Phase 2 clinical trial
Example: Aminocentesis and pregnancy loss
Prospective randomized controlled trial
Retrospective case control study
Cross-sectional study
Example: Randomized study to reduce antibiotic use
Chi-Square Tests: Large Sample (RxC)
Chi-Square Tests: Fisher's exact test
Chi-Square Tests: McNemar's test
Indiana - Basics of Clinical Data Management Tim Breen, PhD, MS, CCDM informatics process
data quality
research proposal
protocol
ICH GCP
stages of clinical trial
data management plan
data collection tools
CRF content
CRF layout
database validation
quality assurance
quality control
Indiana - Comparison of Means Susan M. Perkins, PhD Choice of Test: Study Design, Distribution, Number of Groups
Student's T-Test: Paired Design
Student's T-Test for Independent Groups
Assumptions: T-Tests
Two-Groups - Non-Normal Data: Paired or Independent
Sample size calculations
Negative results
Choice of sample size depends on: Question, variability, and analysis
Analysis of Variance (ANOVA)
Assumptions for ANOVA: Normality, equal variances, independent observations
Example of ANOVA - CD4 Counts and PTSD in HIV Patients
Multiple Comparison Techniques
Indiana - Correlation and Simple Linear Regression Chandon Saha, PhD Correlation analysis
Pearson correlation
Spearman rank correlation
Simple linear regression
Scatterplots for correlation
Example of correlation for height and weight
Interpretation of the size of r - explain variability
When not to use r
Fitting the linear model
Principle of Least Squares
Extrapolation
Causality
Simple linear regression
Residuals
Normality assumption
Indiana - Design of Genetic Studies Dan Koller, PhD Molecular markers
Microsatellite markers
Single Nucleotide Polymorphisms (SNPs)
Single gene (Mendelian) disorders
Genetically complex disorders
Twin studies
Familial aggregation
Genome screen approach
LOD score
Potential candidate approach
Association Studies
Population based approach - case control design
Family based approach - Transmission Disequilibrium Test (TDT)
Indiana - Evaluation of Diagnostic Tests Siu L. Hui, PhD Screening test
Diagnostic test
Accuracy of a diagnostic test
Measure of agreement of two tests
Example: Staging of Prostate Cancer with MRI
True disease status
Test result
Sensitivity
Specificity
False positive rate
Predictive value positive
Predictive value negative
Example: Screening Test for a Rare Disease
Continuous test results
Example: Blood Test for a Disease
Reliability
Kappa
Intraclass Correlation (ICC)
Example: Biphasic Radiography vs Fiberoptic Endoscopy in Gastric Ulcers
Indiana - Hypothesis Testing and Confidence Interval Estimation Susan M. Perkins, PhD Statistical inference
Example of children with new onset seizures and neuropsychological functioning
Example of women of child bearing age and caffeine consumption
Hypothesis testing
Null and alternative hypothesis
Test statistic
Rejection Region
Type I Error
Type II Error
Sample size and power
One sample test of the mean
Central Limit Theorem
Steps in hypothesis testing
Significance level
Rejection region
Relationship of clinical and statistical significance
Confidence interval
Indiana - Introduction to Longitudinal Studies Zhangsheng Yu Longitudinal studies
Cohort study
Intra-individual correlation
Inter-individual variation
Changes over time
Cross-sectional studies
Exploratory analysis
Confirmatory analysis
Random effect models
Marginal models
Markov transition models
Graphical explanation of the random effects model
Indiana - Introduction to Survival Analysis Zhangsheng Yu Survival analysis
Time to event analysis
Logistic regression
Censored observations
Staggered entry of patients
Right censoring
Left censoring
Interval censoring
Example of children with acute leukemia
Mean follow-up
Mean survival
Median survival
Kaplan-Meier curves
Crook study of prostate cancer
Cox proportional hazards model
Indiana - Issues in Clinical Trials Chandon Saha, PhD Definition of a clinical trial
Phase I clinical trial
Phase II clinical trial
Phase III clinical trial
Phase IV clinical trial
Dose-finding trial
Feasibility
Treatment-efficacy
Comparative efficacy
Post-marketing observational studies
Three basic ethical principles:
Autonomy
Beneficence
Justice
Clinical trial design
Features of well-designed clinical trials
Phase I design
Comparative Study (Phase III) design:
Two groups parallel study
Cross-over trials
Randomization
Stratification
Randomization using permuted blocks
Sample size determination
Power
Monitoring a clinical trial
Group-sequential methods:
Pocock method
O'brien-Fleming design
Descriptive statistics
Statistical inference
Components of a study report
Deficiencies in trial reporting
Deficiencies in reporting of results
Deficiencies of editorial standards
Indiana - Multiple Linear Regression and Logistic Regression Patrick Monahan, PhD Simple linear regression
Independent variable
Dependent variable
Predictor or explanatory variable
Outcome or response variable
Multiple linear regression
Assumptions of linear regression
Assumptions of multiple linear regression
Example - multiple linear regression - Perceived barriers to mammography screening
F test for significance
Effect sizes
Partial t-test
Individual covariates
Semi-partial correlation coefficient
Graphical model checking
Non-normality
Non-linearity
Non-constant variance
Residuals
Outliers
Logistic regression
Coding
Wald chi-square tests
Likelihood ratio chi-square tests
Model selection - explanatory
Model selection - prediction
Indiana - Study Design Patrick Monahan, PhD Observation
Informal experimentation
Formal experimentation: Evidence based medicine and Causal inference
Elements of a study
Question and Testable Hypothesis
Outcome variable and its measurement
Experimental study design
Population
Protocol
Analyses
Epidemiologic study designs
Case Series
Assess association between risk factors and disease
Ecologic
Cross-Sectional
Case-Control
Prospective and Historical Perspective
Measures of Disease Frequency
Prevalence
Cumulative Incidence
Incidence Rate
Risk vs Rate
Ecologic Studies
Example of Ecologic Study - Breast Cancer
Cross-Sectional Studies
Example of Cross-Sectional Study - NHANES
Case-Control Studies
Measures of Association - Case-Control: Odds Ratio
2x2 Table
Example of Case-Control Study - Smoking
Example of Case-Control Study - Febrile Seizures
Prospective Studies
Example of Prospective Study - British Male Doctors and Smoking
Measure of Association - Prospective Studies: Relative Risk, Attributable Risk, and Population Attributable Risk
Historical Prospective Studies
Example of Historical Prospective Study - Allegheny Steelworkers
Case Control vs Cohort Studies
Outcome Studies
Common Outcome Measures: Morbidity, mortality, disease severity (PASI, NYHA), health status (SF-36, SIP), Quality of Life (QWB)
Lecture 1: Introduction to Stata   Introduction to Stata
Software packages used in clinical research
Why use Stata?
Why not SAS or R?
Stata basic functionality
Descriptive statistics - continuous data
Graphical exploration - continuous data
Descriptive statistics - categorical data
Analytical statistics - 2 categorical variables
Analytical statistics - 1 categorical, 1 continuous
Analytical statistics - 2 continuous
Lecture 2: Do files, log files, and workflow in Stata   Do files
Log files
Using Do and Log files together
Lecture 3: Generating new variables and manipulating data with STATA   Importing into Stata
Making a new variable
Manipulating values of a variable
Getting rid of variables
Labeling variables
Missing values

Lecture 4: Using Excel
Lecture 5: Basic epidemiologic analysis with Stata   Statistical analysis
Epidemiologic analysis
Confounding
Interaction
Causal diagrams
2x2 or Contingency tables
Effect modification
Lecture 6 - Part 1: Making a figure with Stata or Excel   Figures
Photographs
Diagrams
Pie charts
Scatter plots
Bar graphs
Line Graphs
Lecture 6-2: Mostly Dates\x85 and a few other useful STATA commands   Date conversion
Date formatted
Compare dates
Literal dates
Programming loops
Merging datasets
Lecture 7: Organizing a project, making a table   Organizing Stata files
Table components
Stata to Word
Stata to Excel
Lesson 2: Statistical Issues in the Design of a Trial, Part 1   Experimental design
Randomized
Nonrandomized studies
Simple randomization
Blocked randomization
Stratification
Randomization process
Group allocation design
Factorial design
Crossover design
Blinding
Phases of a study
MCW - ANOVA: Comparing More Than Two Sergey Tarima, PhD ANOVA
More than two treatments
MCW - All Biostatistics YouTube- Videos MCW Biostatistics
MCW - Analyzing Discrete Data Kwang Woo Ahn, PhD Discrete data
MCW - Choosing Statistical Software Brent Logan, PhD Choosing Statistical Sofware
SAS
SPSS
STATA
Cost of software packages
MCW - Concepts in Biostatistics Prakash Laud, PhD Concepts
Data
Decisions
Hypotheses formulation
Confidence interval
MCW - Designing Clinical Trials Brent R. Logan, PhD Designing Clinical trials
Phase I Trials
Phase II Trials
Phase III Trials
MCW - Discrete Data Analysis Tao Wang, PhD
MCW - Equivalence and Non-Inferiority Testing Gisela Tunes da Silva, PhD Equivalence
Non-Inferiority Testing
Methods
Same
Not worse
MCW - Introduction to Survival Analysis John Klein, PhD Survival Analysis
Survival Data
Univariate Statistics
Kaplan-Meier Estimator
Competeing Risks
MCW - Logistic regression Sergey Tarima, PhD Logistic regression
MCW - Matched Studies in Medical Research John P. Klein, PhD Matched Studies in Medical Research
Matched Pairs Design
Examples of Biological Matching
John Klein
Medical College of Wisconsin
MCW - Multiple Comparisons Brent R. Logan, PhD Multiple comparisons
MCW - Paired Data Ruta Bajorunaite, PhD Paired data
MCW - Paired Data Analysis Jennifer Le-Rademacher, PhD paired data analysis
independent vs. paired data
paired t-test
sign test
Wilcoxon Sign rnk test
McNemar's test
Parts of a paper
Medical College of Wisconsin
Ruta Brazauskas
MCW - Simple Linear Regression Mei-Jie Zhang, PhD Simple linear regression
Equation of a line
Fitting line to data
Strength of Association
Assumptions of Linear Regression
MCW - Simple Statistics in Excel Aniko Szabo, PhD Descriptives
Pivot Table
Correlation
T-test
Paired T-test
Regression
ANOVA
MCW - Statistical Considerations in Grant Writing Aniko Szabo, PhD Statistical Considerations
Grant Writing
Mistakes and pitfalls in grant writing
statistical issues in grant writing
MCW - Statistics, Probability & Diagnostic Medicine Jennifer Le-Rademacher, PhD Diagnostic Accuracy
Statistics for Qualitative Tests
Sensitivity
Specificity
Probability
MCW - Uses and Abuses of Non-Parametric Statistics John Klein, PhD Non-parametric statistics
Medical Research
Nonparametric tests
Medical College of Wisconsin
John Klein
MCW - Web-Based Sample Size Calculation Kwang Woo Ahn, PhD Sample size calculation
Basic power calculations
Sample size analysis
MCW - Writing a Protocol John Klein, PhD Protocol
MCW- Basic Concepts of Bayesian Statistics Prakash Laud, PhD Bayesian statistics
calibration of probability
Bayes Theorem
Bayesian methods
MCW- Common Errors in Linear Regression Kwang Woo Ahn, PhD
MCW- Getting Help for Your Biostatistics Questions and Database Basics Dan Eastwood, MS MCW
statistical help
Medical College of Wisconsin
Milwaukee
Dan Eastwood
database basics
biostatistics questions
organize data
types of data
data management
MCW- Propensity Scores Mei-Jie Zhang, PhD propensity score
propensity score method
propensity score estimation method
propensity score matching
estimating treatment effect using propensity including matched pair analysis and regression adjustment and stratification
Mayo - Statistics in Clinical Research Felicity Enders, PhD
NIH - A Conceptual Approach to Survival Analysis Laura Lee Johnson, PhD Survival or time to event
Median survival
Outcome variable
Event time
Time origin
Time scale
Sample size
Truncation
Censoring
Right censoring: withdrawals
Independent censoring
Covariates
Survival
Hazard fundtions
Competing risks
Independence assumption
Kaplan Meier
Inference
Log rank
Stratified log rank
Cox Proportional Hazards Model
Time-Dependent Survival Curves
NIH - Design of Epidemiological Studies Laura Lee Johnson, PhD Questions
Hypothesis
Experimental design
Samples
Data
Analyses
Conclusions
Epidemiology
Relative risk
Odds ration
Risk difference
Dose-response relationships
Temporal sequence
Consistency of the association (internal "validity")
Replication of results (external validity)
Biological plausibility
Experimental evidence
Variable
Covariate
Coefficient
Association
Confounders
Effect modifiers
Example: Coffee and pancreatic cancer
Randomization
Prevalence
Incidence
Sensitivity
Specificity
Selection bias
Interviewer bias
Randomized vs non-randomized study
Treatment vs observational study
Prospective vs retrospective study
Longitudinal vs cross-sectional study
Case reports
Mean
Standard deviation
Proportions
Confidence limits or intervals
Case-control study
Cohort study
NIH - Issues in Randomization Laura Lee Johnson, PhD Randomized Study Design
Sample size
Mean
Median
Variance
Standard deviation
Standard error
Odds ratio
Sampling distribution
Quasi experimental studies
Blocked randomization
Stratified randomization
Cluster randomization
Intent to treat
Parallel group
Sequential trials
Group sequential trials
Cross-over
Factorial designs

NIH - Principles of Hypothesis Testing for Public Health Laura Lee Johnson, PhD Estimation
Statistical Inference
Hypothesis testing
Distributions
Sides and tails
Clinically important difference
Point estimation
Interval estimation
Scatter plots
Box plots
Histograms
Bar plots
Null hypothesis
Alternative hypothesis
Two-sided test
One-sided test
Type I error
Type II error
Significance level
Power
Confidence interval
Linear regression
Repeated measures
Positive predictive value
Negative predictive value
Sensitivity
Specificity

Rochester - Basic Statistics: MBI-540 (for Virologists) Sally Thurston, PhD Experimental Design
Mean
Standard Deviation
Standard Error
M&M Data
2-Sample T-test
Importance of Plotting the Data
1-Way ANOVA
UCI - A Brief Introduction to R Babak Shahbaba (babaks@uci.edu) R
Objects
Vectors
Matrices
Data frames
Loops
UCLA - Online Biostatistics Courses and Seminars UCLA Biostatistics Consulting (See individual classes)
UCSF - Case Control Study Design Jeff Martin, MD
UCSF - Clinical Trial Design Video Deborah Grady, MD
UCSF - Cohort Study Design Jeff Martin, MD Cohort study design
Threats to validity
Framingham Study
HCV/HIV Study
UCSF - Common Biostatistical Problems PeterBacchetti P-values for establishing negative conclusions
Exclusive reliance on intent-to-treat analysis
Reliance on omnibus tests and
Entangled outcomes and predictors
UCSF - Cross-Sectional Study Design Jeff Martin, MD Cross-sectional study
Point Prevalence
Period Prevalence
UCSF - Data Exploration David Glidden, PhD Multiple predictor regression
Date Types: Numerical (Continuous and Discrete), Categorical (Ordinal and Nominal)
Hierarchy of data types
Outliers
Boxplots
Histograms
UCSF - Disease Association-Risk Ratio Jeff Martin, MD Disease association
Risk ratio
Odds ratio
Log Rank Test
Wilcoxon
UCSF - Intro Cohort Study Design Jeff Martin, MD Cohort study design
Human subjects studies
Study base
UCSF - Multi-Predictor Linear Model David Glidden, PhD Linear Regression
Predictors
Normality
Regression dilution bias
Regression coefficients
Negative mediation
Log-transformed predictors
Confidence intervals
UCSF - Simple Linear Regression David Glidden, PhD Scatterplot
Mean
95% CI
Linear regression
Least squares
Outlier
Bias
Confounding
UCSF - Stata Course Mark Pletcher, MD, MPH (See individual lectures.)
Vanderbilt - Biostatistics II Lecture Notes for 2010 William C. Dupont, PhD Simple Linear Regression
Multiple Linear Regression
Simple Logistic Regression
Multiple Logistic Regression
Survival Analysis
Hazard Regression
Simple Poisson Regression
Multiple Poisson Regression
Analysis of Variance
Mixed Effect
Analysis of Variance
Vanderbilt - Teaching Methods - Biostatistics Vanderbilt Biostatistics (See individual courses)
www.SampleSizeShop.org Keith Muller/Deb Glueck Sample size, mixed models.
Number of topics: 82