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.

Applied Quantile Regression - M. Bottai (Karolinska Institutet)

Quantile regression is a statistical method for the analysis of the median and other percentiles of an outcome of interest. This increasingly popular method provides great insight in medical research and an alternative approach when other traditional methods may not be valid. The course offers an introduction to quantile regression and its extensions through a series of real-life examples from clinical and epidemiological studies. The focus is on interpretation and practical relevance. Based on lectures and computer activities, the course enables the participants to utilize quantile regression as a simple tool for the analysis of outcomes of interest with emphasis on its flexibility in the presence of non-normal outcomes, outlying values, survival and longitudinal data.

Block 2

Principles of Epidemiology - J. Buring (Harvard University)

This course provides an introduction to the skills needed by public health professionals and clinicians to critically interpret the epidemiologic literature. It will provide participants with the basic principles and practical experience needed to develop these skills. This will be accomplished by covering the basic principles and methods of the design, conduct and interpretation of epidemiologic studies, including descriptive studies, observational analytic studies (case-control and cohort), and randomized clinical trials. In addition, the course will address the calculation and interpretation of measures of disease frequency and association; the assessment of association versus causation in the interpretation of study results; and issues related to the evaluation of chance, bias, confounding, and effect modification. Lectures will be complemented by seminars devoted to case studies, exercises, or critiques of relevant examples of epidemiologic studies.

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.

Missing data in observational and randomized studies - N.J. Horton (Smith College)

Missing data arise in most real-world situations, and can cause bias or lead to inefficient analyses. The development of statistical methods to address missingness has been actively pursued in recent years, and sophisticated software to appropriately account for it is available within Stata. In this course, participants will learn ways to minimize missingness, the nomenclature for missing data methods, appropriate ways to describe patterns of missing data as well as how to account for incomplete observations using multiple imputation and sensitivity analysis. The course will emphasize practical skills with biomedical examples of observational studies and randomized trials.

Block 3

Modern Epidemiology - J. Kasperzyk (Harvard University)

This course will explore in greater depth the fundamental epidemiologic concepts introduced in Principles of Epidemiology (Week 1). Topics will include disease surveillance, nutritional epidemiology, and a special emphasis on genetic and molecular epidemiology. Epidemiologic examples across major chronic diseases/conditions (e.g. heart disease, metabolic syndrome, cancer) will be discussed. Students will revisit the issues of confounding, selection bias, effect modification, and generalizability in the context of these topics. Lectures will be augmented by workshops to illustrate practical examples in the epidemiologic literature. The material covered in Principles of Epidemiology will be assumed of the students entering this course.

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.

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)

This course is designed to provide the student with an understanding of the foundations of biostatistics and of the various statistical techniques that have been developed to answer research questions in the health sciences. Students will be introduced to methods for the comparison of outcome between two groups (t-test and non parametric tests), as well as the extension to the comparison of outcome across several groups (ANOVA); methods for the study of association between two continuous variables (correlation and linear regression); the analysis of contingency tables; the study of survival (time-to-event) data. The afternoon sessions are devoted to discussion and learning to use Stata® to implement materials covered in the morning lectures.

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®).

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

This course will present case studies aimed to understand how EBPH methods can be applied in the public health decision-making process. Students will role play leadership positions during the presentation of case studies and discuss as a group the consequences of the decisions they make. Topics will include: search for scientific evidence in public health, translation of research findings in public health practice, methods for evaluating public health programs and policies. During the course emphasis will be given to the use of questionnaires as tools designed to change policies and programs. Students will learn how to assess the validity and reliability of such questionnaires using a variety of statistical methods. Knowledge of basic concepts and methods in biostatistics is required for this course.

Block 5

Causal Inference - Andrea Rotnitzky (Harvard University and Universidad Di Tella)

Students will learn to critically evaluate the pitfalls of observational studies and of imperfect experimental studies, in particular all possible biases that can arise. Students will learn analytic tools, based on causal diagrams and new statistical models, that can help squeeze as much evidence as these imperfect studies carry about the causal effects of interventions, treatments and/or exposures of interest.

Randomized Clinical Trials - D. Harrington (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.

Statistical methods for population-based cancer survival analysis - P. Dickman (Karolinska Institutet) & P. Lambert (University of Leicester and Karolinska Institutet)

The course will cover central concepts, such as how to estimate and model relative survival, as well as recent methodological developments including cure models, flexible parametric models, estimation in the presence of competing risks, and methods for analysing data with missing covariates. Comparison of alternative methodological approaches (e.g., to estimating relative survival and to modelling relative survival) will will be a focus of the course and participants will get the opportunity to apply and contrast a range of methods to real data. The course will consist primarily of lectures and hands-on computing sessions with a focus on individual instruction and discussion. Click here for further details of the course content.

Stata 1

Introduction to Stata® - S. Venturini (Bocconi University)

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-management, data-reporting, graphics and basic use of do-files. By the end of this one-day course, the student should be capable of using Stata independently.

Meta-analysis using Stata® - R. D'Amico (University of Modena and Reggio Emilia)

The aim of this course is to provide an overview of methods to perform meta-analysis using Stata. We will cover Stata commands for a variety of tasks: data preparation and input, fixed and random-effect models, forest plots (publication quality graph), heterogeneity across studies, publications bias, sensitivity analysis, and meta-regression models.

Tables for epidemiologists using Stata® - N. Orsini (Karolinska Institutet)

This course is designed to introduce students to basic Stata commands useful in epidemiological research: descriptive statistics to estimate the incidence of a binary response and to characterize the demographic information supplied by study participants; statistical tests to identify univariate predictors associated with the binary response; graph the incidence of a binary response as a function of a predictor; and table of standardized means and proportions.

Stata 2

Introduction to Stata® - D. Rizzuto (Karolinska Institutet)

Flexible dose-response analysis with Stata® - N. Orsini (Karolinska Institutet)

This course is designed to introduce students to flexible modeling of a quantitative covariate using different approaches. Emphasis is placed on communicating results in a powerful manner using graphs. Motivating examples from top medical journals will be used during lectures and exercises. By the end of this one-day course, the students will be capable to perform and present a dose-response analysis.

Multiple imputation of missing data with Stata® - R. Bellocco (Karolinska Institutet and University of Milano-Bicocca)

To provide a practical overview of methods to estimate missing data. The course will introduce the basics of multiple imputation, in particular imputation by chained equations. By the end of this one day course, participants should be capable to analyse data by multiple imputation in Stata. Students should have a background in linear regression methods prior to taking this course.

Stata 3

Introduction to Stata® for survival analysis - S. Eloranta & T. Andersson

This course is designed to introduce students to basic survival analysis using Stata. We will describe how Stata can be used to analyse time-to-event data, including an introduction to the powerful built-in stset command. In addition, this short course will cover the topics such as the Kaplan-Meier estimator of survival, log-rank tests, hazard functions as well as a short introduction to the Cox regression model. Prior experience with at least one other statistical software will be helpful, but is not mandatory.

postmaster@biostatepi.org