| Title | Contributor/Contact | Keywords |
|---|---|---|
| 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; Fixing Confounders - Adjust; 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 Adjudication of 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 Assumptions about the random error 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 Meiosis and linkage Genome screen approach LOD score Potential candidate approach Linkage vs association Linkage Disequilibrium Studies 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 Receiver Operating Characteristic (ROC) Curve 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 Reading ability example Exploratory analysis Confirmatory analysis Ad hoc 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 Adjust odds ratio 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 Conclusion should answer question 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 Advantages and Disadvantages of Ecologic Studies Cross-Sectional Studies Example of Cross-Sectional Study - NHANES Advantages and Disadvantages of Cross-Sectional Studies 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 Advantages and Disadvantages of Case-Control Studies Prospective Studies Example of Prospective Study - British Male Doctors and Smoking Measure of Association - Prospective Studies: Relative Risk, Attributable Risk, and Population Attributable Risk Advantages and Disadvantages of Prospective Studies Historical Prospective Studies Example of Historical Prospective Study - Allegheny Steelworkers Advantages and Disadvantages of Historical Prospective Studies 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) Advantages and Disadvantages of Health Care Delivery Studies |
| 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 Menu vs command line Describing your data 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 |
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| Lecture 2: Do files, log files, and workflow in Stata | Do files Comments Advantages of Do files Log files Advantages of Log files Using Do and Log files together |
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| Lecture 3: Generating new variables and manipulating data with STATA | Importing into Stata Exploring your data Cleaning your data Making a new variable Manipulating values of a variable Getting rid of variables Labeling variables Missing values |
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| 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 Multivariable adjustment |
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| Lecture 6 - Part 1: Making a figure with Stata or Excel | Figures Photographs Diagrams Pie charts Scatter plots Bar graphs Line Graphs |
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| 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 |
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| Lecture 7: Organizing a project, making a table | Organizing Stata files Table components Stata to Word Stata to Excel |
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| 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 |
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| 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 |
| MCW - Reading Medical Literature | Ruta Brazauskas, PhD | Reading Medical Literature 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 adaptive clinical trials |
| 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 spreadsheet vs database 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 Blinded/Masked or not 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 Adaptive randomization Intent to treat Parallel group Sequential trials Group sequential trials Cross-over Factorial designs Adaptive 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 Misleading and vague phrasing Speculation about low power Exclusive reliance on intent-to-treat analysis Reliance on omnibus tests and Overuse of multiple comparisons adjustments 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. |
