Analyzing Discrete Data: Describing discrete (categorical) data; graphical display; measure of association for binary outcomes; risk difference, relative risk, odds ratios; tests of association; Chisquare tests; Fisher’s exact test; introduction to logistic regression model


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ANOVA: Comparing More Than Two Treatments: Techniques for comparing the mean responses of two or more treatment groups (one way ANOVA); comparison of two factors (two way ANOVA); multiple comparisons


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Basic Concepts of Bayesian Statistics: What is Bayesian statistics; interpretation and assignment of probability; calibration of probability; conditional probability; Bayes Theorem; prior, posterior and predictive distributions; inference and prediction conditioned on full information; when Bayesian methods are particularly useful; individualized medicine; hierarchical and complex stochastic modeling; pooling of information; substantial extradata information; adaptive clinical trials.


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Biostatistics Questions & Database Basics: Resources available in the Biostatistics Consulting Service; basic questions when planning a study; basic concepts of how to create a research database; types of data; spreadsheet vs. database


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Choosing Statistical Software: Introduction to some of the most commonly used statistical packages; comparison of the capabilities, availability, cost, and ease of use of the packages


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Common Errors in Linear Regression: Linear regression components, use graphs to check linear regression assumptions, linearity, constant variance, normality, outliers, multicollinearity, remedies


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Concepts on the Way from Data to Decisions: Hypothesis formulation; studytostudy variation; tests of hypothesis; type I and II errors; confidence intervals; planning a study


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Designing Clinical Trials: Dosefinding phase I designs; phase II designs; determination of sample size; the use of two stage designs


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Getting Help for Your Biostatistics Questions & Database Basics: Resources available in the Biostatistics Consulting Service; basic questions when planning a study; basic concepts of how to create a research database; types of data; spreadsheet vs. database


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Introduction to Survival Analysis: Censoring and truncation; KaplanMeier estimators; log rank tests; competing risks; cumulative incidence functions


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Introduction to Survival Analysis (11.8.13): Survival analysis; timetoevent data; event of interest in studies (e.g. death, recurrence of disease, development of complications after treatment); methods for partial data; regression methods; methods used to analyze competing risks data


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Logistical Regression: Simple logistic regression models for binary data; interpretation of regression coefficients in simple logistic regression; multiple logistic regression models; estimation and inference for logistic regression models; odds ratios; models for association; models for prediction/classification


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Logistic regression:


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Longitudinal Analysis: Longitudinal studies; design for longitudinal studies; analysis for longitudinal studies; correlation between measurements; statistical methods to address correlation; missing data mechanism


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Matched Studies in Medical Research: The use of retrospective and prospective matching; Methods for comparison of treatment in matched studies with binary or continuous outcome; paired vs. unpaired ttests; McNemar’s test


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Multiple Comparisons: Problem of multiple testing; familywise error rates; false discovery rates; multiple testing strategies; subgroup analysis; interim analysis


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Paired Data Analysis: Paired ttest; Sign Test; Wilcoxon Sign rank test; McNemar’s test


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Propensity Scores: Definition of propensity score; the background and motivation of developing propensity score method; propensity score estimation method; propensity score matching; estimating treatment effect using propensity including matched pair analysis and regression adjustment and stratification


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Reading Medical Literature: The main parts of a statistical paper; review of basic statistical concepts; reading the statistical methods and results section


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Methods for Equivalence and Non‐Inferiority Testing: Equivalence tests: tests designed to show two treatments have the same mean outcome; noninferiority tests: tests to show a new treatment performs no worse than an existing test; confidence interval based tests


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Simple Statistics & Graphics in Excel: Data entry; descriptive statistics (mean, median, etc.); statistical inference (ttest, ANOVA, regression); how to create and enhance graphs (line, pie, bar) in Excel


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Simple Statistics in Excel: Entering and managing data in Excel; descriptive statistics; statistical inference in Excel including ttest, simple regression models; data analysis addin


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Accompanying Spreadsheets (XLSX)




Statistical Consideration in Grant Writing:
Specific aims and hypotheses; specifying the study population; selecting a study design; defining outcome measures; sample size calculations; data analysis plan


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Statistical Graphics in Excel: Types of graphs in Excel: pie charts, bar charts, histograms, line graphs, scatter diagrams; properties of a good graph; modifying default graphs


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Statistics, Probability & Diagnostic Medicine: Sensitivity; specificity, positive and negative predictive value; the likelihood ratio; the ROC curve


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Simple Linear Regression: Simple linear regression; fitting a line to data; interpretation and prediction; confidence intervals and hypothesis testing; measuring the strength of association; model checking; binary predictions


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Uses and Abuses of Non‐parametric Statistics: The sign and sign rank test for paired data; the MannWhitney Wilcoxon test for unpaired data


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WebBased Sample Size Calculation: Statistical hypotheses, significance level and statistical power; free online website for sample size and power calculations


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Writing a Protocol: Protocol rational for prospective and retrospective studies; elements of a protocol


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