GCSRT | Foundation Courses

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Foundation Courses


Introduction to Biostatistics

This course provides a thorough introduction to the most commonly used biostatistics techniques for clinical research. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; and issues of power and sample size in study designs. There is an introduction to simple linear regression and survival analysis.

Introduction to Epidemiology

This introductory course in epidemiology presents an overview but not a detailed discussion of the basic methods of epidemiology and their applications to clinical research. Lectures explore such basic principles of epidemiology as the importance of measurement, including types of outcome measures and measures of association; diverse array of study designs available in clinical research, including cross-sectional studies, cohort studies, case-control studies and experimental designs; types of potential biases, including selection bias and measurement bias; confounding and methods for its avoidance and control; and effect modification.

Biostatistical Computing

The ability to import data into a statistical package from a database or excel spreadsheet is considered essential in clinical research. Introductory lectures will consist of teaching the basic functions of the Stata program, including learning key commands, creating a do-file, getting data into the shape needed for analysis, and checking for errors. More advanced lectures will focus on using Stata for regression and survival analysis. Lastly, there are lectures on developing polished manuscript-ready tables and figures.

Ethics and Regulation

This course reviews some common challenges in the conduct and review of biomedical human subjects research. Lectures examine the history and evolution of ethical codes and regulations; the role and responsibility of physicians as investigators; the preparation of research protocol applications and informed consent documents; and the challenges of conducting research involving children and adolescents.​

Applied Regression

The course covers sampling distributions; one and two sample tests for means and proportions; correlation and basic linear and multiple regression model building. Initially, lectures will explore general concepts in linear regression and consider residual analysis and data transformations. Lectures will address multiple linear regression, including consideration of confounding and effect modification. Model building will be emphasized. Lastly, several lectures will explore topics in logistical regression including, 2x2 Tables and stratification, model building and assessment of goodness of fit, and smoothing and generalized additive models.

Survival Analysis

It builds on the basic concepts of survival analysis discussed in Introduction to Biostatistics, including hazard functions, survival functions, types of censoring and truncation, Kaplan-Meier estimates, log-rank tests and their generalization. The course introduces statistical models and methods useful for analyzing univariate and multivariate failure time data. After completing this course, students will be able to describe time-to-event data and compare groups with a time-to-event outcome; interpret the coefficients and control for confounding using a Cox proportional hazards model; interpret interaction terms and incorporate time varying covariates in a Cox model as well as assess the proportional hazards assumption. Lastly, students will learn how to complete a sample size calculation for a survival study.

Longitudinal and Correlated Data

A longitudinal study refers to an investigation where outcomes and possibly treatments or exposures are collected at multiple follow-up times. a longitudinal study generally yields multiple or “repeated” measurements on each subject, which may correlate over time. With correlated outcomes, it is useful to understand the strength and pattern of correlations. Characterizing correlation can be approached using mixed-effects models or generalized estimating equations (GEE). This course covers methods to analyze longitudinal data, including the use of linear regression models. Topics will include polynomial trends for time (e.g., linear or quadratic) and linear mixed-effects models. at the end of the course, students will be able to interpret the results from a multilevel model and understand how to incorporate multiple random effects into the model. Students will be able to understand the types of missing data that occur in longitudinal and cross-sectional analysis as well as understand the assumptions associated with each analysis approach.

Causal Design

Causal inference is an overarching objective of most forms of medical and epidemiological investigation. Key questions usually consist of whether an intervention works and the extent of the benefit and whether it causes harm. While a randomized controlled trial design is considered the most powerful way to infer causality, such studies may not be possible or feasible and an observational approach may be necessary to attain causal inference. At the end of the course, students will have a deeper understanding of observational approaches, especially from the perspective of overcoming the problem of confounding. Students will be able to define confounding and develop approaches toward identifying confounders. DAGs, as a structural approach to identifying confounders, will be highlighted. Other topics will include the rules of D-separation and conditioning on common effects. Propensity scores will be introduced. The differences between randomized trials and observational studies are considered and quasi-experimental designs introduced.

"The GCSRT program has achieved a perfect blend of international exposure and flexibility. It has been able to fulfill my desire to tap into a global wealth of knowledge without compromising on professional growth. I am confident its impact on my career goals will be phenomenal."


Ewemade Igbinedion, MBBS, MPH, FRSPH, ACIEH
Igbinedion University Teaching Hospital
Benin, Nigeria