Expand your understanding of statistical concepts for answering scientific questions. Design experiments or clinical studies and apply appropriate statistical methods to analyze data sets from scientific and medical literature. Learn via a convenient online format, and use the R statistical computing platform to read data, describe data and perform statistical analyses.
Who should Apply:
- Scientists and researchers who need more extensive training or review in the use of statistical tools and experimental design as they are applied to biological problems.
- Professionals in the pharmaceutical industry, working in either research and development or clinical trials.
- Researchers carrying out biological studies.
- Students with undergraduate or master’s level training in chemistry, biochemistry, biology, microbiology, forestry, fisheries, nursing or other biological or clinical fields.
- Select and interpret appropriate graphical displays and numerical summaries for both quantitative and categorical data;
- Explain the difference between observational and experimental studies;
- Identify and describe cohort sampling, case-control sampling and cross-sectional sampling;
- Recognize and explain the concepts of confounding and effect modification;
- Describe the assumptions underlying the Binomial, Poisson and Normal probability models;
- Define sensitivity, specificity and predictive values in the context of a binary screening test for a disease;
- Translate scientific questions into appropriate null and alternative hypotheses;
- Describe the assumptions underlying z-tests, t-tests and chi-square tests and use these tests to statistically compare two samples;
- Explain and interpret p-values and confidence intervals;
- Describe the assumptions underlying simple linear regression and be able to fit and interpret a regression model;
- Make predictions with a simple linear regression model;
- Select and apply appropriate statistical methods to analyze their own data (for scientific questions appropriate to the tools taught in the course) and develop an analysis plan;
- Critique the use of statistical methods in the published biomedical literature ;
- Describe how the scientific goals of analysis affects the strategy to select and use appropriate multiple regression models;
- Describe a coherent strategy for analyzing data using multiple regression models;
- Carry out the analysis of data using multiple regression models in Stata;
- Interpret the results of a multiple regression analysis to a statistically untrained colleague;
- Describe how well a multiple regression model fits the data;
- Examine multiple regression models and assess if there are important model violations;
- Describe how one and two-way analysis of variance and analysis of covariance are related to multiple regression analysis;
- Perform standard tests of homogeneity and trend with data from 2 x C tables;
- Compute odds ratio estimates and confidence intervals from 2 x 2 and stratified 2 x 2 tables;
- Perform logistic regression to estimate model coefficients and odds ratios (adjusted), compute and interpret coefficient and odds ratio confidence intervals, and test hypotheses that one or more coefficients in the logistic regression model are zero;
- Compute and interpret the product limit estimate (Kaplan-Meier) estimate of survival and associated confidence intervals;
- Perform and interpret the log-rank test for differences between survival curves with right censored survival data;
- Perform Cox regression to estimate proportional hazards model coefficients, interpret coefficient estimates and confidence intervals, and test hypotheses that one or more coefficients in the regression model are zero; and
- Interpret and critique the results of application of these statistical techniques as found in the health sciences literature.