Course description

Biostatistics I - M. Pagano (Harvard University)

Introduces the fundamental principles of statistics applied to biomedicine. The topics to be covered include: descriptive statistics, measures of central tendency, probability, diagnostic testing, population and sample, comparison of proportions. At the end of the course, students will be able to understand the descriptive statistical methodologies which are used in clinical and epidemiological studies and to utilize the estimates obtained from suitably selected samples, in order to draw statistical inferences.

Applied Linear Regression - R. Bellocco (Karolinska Institutet and University of Milano-Bicocca)

The course introduces students to the practice and application of regression modeling. Through the use of Stata®, students will learn how to fit a regression, estimate, and test regression coefficients. Particular emphasis will be placed on the interpretation of the regression coefficients of continuous and categorical predictors. Analysis of variance models and their correspondence with regression models will also be covered together with procedures and issues in model selection, including confounding and interaction. Model building, goodness of fit, residual analysis, and appropriate regression diagnostics will be discussed.

Randomized Clinical Trials - S. Lagakos (Harvard University)

Randomized clinical trials play a central role in the advancement of knowledge about new and existing interventions for the prevention and treatment of disease, and the interpretation of the results of most clinical trials requires a careful balance of the apparent results and the limitations of the underlying study. With an aim of improving one's ability to critically evaluate published results about clinical trials, this course will provide an introduction to the methods used in the design, interim monitoring, and analysis of clinical trials, including the impact of patient exclusions and other causes of incomplete data. The course will emphasize concepts and principles, and the main focus will be on randomized Phase III (comparative) trials. The morning sessions will consist of lectures, while the afternoon sessions will discuss case studies from recently published trials, as well as practical experience in study design.

Block 2

Epidemiology - D. Trichopoulos (Harvard University)

The emphasis of this course is on principles and concepts that drive the computational formulas. Issues and methods in design, analysis and interpretation of epidemiological studies will be considered. Knowledge of basic statistical and epidemiological issues is desirable but not necessary. Topics include: confounding, bias, effect modification, and chance; measures of disease occurrence and measures of association; design and analysis of cohort studies; design and analysis of case-control studies. By the end of the course, students will comprehend substantive epidemiological papers in the medical literature and be comfortable with such issues as attributable fractions; and distinguish between alternative effect measures like rate ratios, rate difference, and risk ratios.

Applied Logistic Regression – D. Wypij (Harvard University)

This course introduces students to the practice and application of logistic regression modeling for binary outcomes. Students will fit, evaluate, and interpret binary data models arising from epidemiological studies, clinical trials, or other application areas. Topics include assessment of confounding and effect modification, use of indicator variables, model building methods, goodness-of-fit assessment, presentation of logistic regression models for reports and publications, and an introduction to conditional and ordinal logistic regression. Data sets from the medical and public health literature will be used as case studies to be analyzed using the Stata® statistical package.

Meta-analysis - Marc Buyse (IDDI and Hasselt University)

Meta-analysis refers to the analysis of data from different studies. Essential principles of meta-analysis include stratification by study and investigation of heterogeneity. Meta-analysis is an essential part of evidence-based medicine. The course will cover types of meta-analyses of randomized clinical trials (based on published data, summary statistics or individual patient data); statistical methods for different types of endpoints (binary, normal, survival); measures of treatment benefit; investigations of heterogeneity and interactions; methods and limitations of meta-regression; and further uses of meta-analysis (validation of surrogate endpoints and of predictive biomarkers). It will help students perform meta-analyses and evaluate results reported in the literature. Data sets from actual clinical trials will be used as case studies to be analyzed using the Stata® statistical package.

Block 3

Design of case-control and cohort studies - L. Mucci (Harvard University)

This course will focus on the design, analysis, and interpretation of cohort and case-control studies in epidemiological research. The morning sessions will delve into cohort and case-control sampling strategies, including prospective and retrospective cohorts, matched designs, nested case-control and case-cohort studies. We will discuss the strengths, limitations, and interpretation of the alternate approaches. Problems of exposure and disease definitions, confounding, and misclassification will be considered in the light of epidemiological data sources. We will use the afternoon sessions to review and discuss journal articles highlighting various aspects of the design of cohort and case-control studies. The lab sessions will emphasize analysis and interpretation of results in the context of research questions and study design. As a final project, the students will work together to design a hypothetical study using the principals learned in the course.

Survival Analysis - P. Dickman (Karolinska Institutet)

The course aim is to introduce statistical methods for survival analysis, that is, the analysis of studies where the outcome is a time-to-event. We will study methods for estimating patient survival (life table and Kaplan-Meier methods), comparing survival between patient subgroups (log-rank test), and modelling survival (primarily Poisson regression and the Cox proportional hazards model). Emphasis will be placed on describing how epidemiological cohort studies can be analysed in the framework of survival analysis. We will study the concept of 'time' as a potential confounder or effect modifier and discuss various approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification. The course will consist of lectures, classroom exercises, and computing exercises (using Stata®).

Monitoring and Evaluation of Health Programs - M. Pagano (Harvard University)

This course covers the basic statistical tools necessary for monitoring and evaluation of health programs. The range of topics includes assessing modern and rapid testing methods; an overview of various methodologies and designs for estimating coverage and changes for a region; methods for evaluating subregional performance (ie the health districts of a region); and comprehensive M&E approaches that allow for both local and regional assessment. Emphasis will be on the practical aspects of design, analysis and presentation (including mapping), with a large number of examples presented, and training on the use of software.

Block 4

Biostatistics II - M. Bonetti (Bocconi University)

A course designed to provide the student with an understanding of the foundations of biostatistics and how useful the discipline is in tackling problems in the health sciences. Students will be introduced to graphical techniques, probability models, hypothesis testing, confidence intervals, correlation, regression. Topics include: comparison of two samples (t-test and non parametric tests), regression and correlation, analysis of contingency tables, life table and survival analysis. The afternoon sessions are devoted to discussion and learning to use Stata® to implement materials covered in the morning lectures.

Applied Longitudinal Analysis – G. Fitzmaurice (Harvard University)

This course focuses on methods for analyzing longitudinal and repeated measures data. The defining feature of longitudinal studies is that measurements of the same individuals are taken repeatedly through time, thereby allowing the direct study of change over time. This type of study design encompasses epidemiological follow-up studies as well as clinical trials. The course covers many well-established methods for the analysis of longitudinal data when the response variable is continuous. Methods for discrete response variables (e.g., repeated binary responses and counts) are introduced, but not emphasized. An introductory course in biostatistics and a good background in linear regression analysis are prerequisites for this course.

Evidence Based Public Health - E. Savoia (Harvard University)

This course will introduce the core concepts of Evidence Based Public Health and present studies conducted in Europe, the United States and in the Developing World assessing the effectiveness of public health interventions and their relationship with the organizational structures, financing systems, workforce characteristics and delivery mechanisms of various practice settings. Topics will include: definition of Evidence Based Public Health, identification of sources and databases to search for scientific evidence in public health, study designs issues, development and validation of surveys to be used in public health practice (including teaching how to create online surveys), causality in public health research, methods for the assessment of public health programs and policies, assessment of performance in public health practice. Knowledge of basic concepts and methods in epidemiology and biostatistics is required for this course.

Stata 1

Introduction to Stata® - R. Bellocco (Karolinska Institutet and University of Milano-Bicocca)

This course is designed to introduce students to the basics of Stata. It will focus on the minimum set of commands everyone should know to organize their own work. Specific topics include data-reporting, data-management, formatting and graphics. By the end of this one-day course, the student should be capable of using Stata proficiently.

Epidemiology with Stata® - N. Orsini (Karolinska Institutet)

This course is designed to introduce students to basic commands useful in epidemiologic research (epitab). Specific topics include tables and regression models for epidemiologists (cumulative-incidence, case-control and incidence rate data). By the end of this one-day course, the student will be more familiar with epidemiologic methods using Stata®.

Stata 2

Introduction to Stata® - N. Orsini (Karolinska Institutet)

Analysis of prospective studies with Stata® - R. Bellocco (Karolinska Institutet and University of Milano-Bicocca)

This course is designed to introduce student to the analysis of cohort studies, managing person-times, estimating counts and incidence rate ratios of both fixed and time-varying exposures and fitting count regression models. By the end of the course, the student will be familiar these epidemiogical techniques using Stata®.

postmaster@biostatepi.org