Biostatistics is the study of statistical estimation and inference in the context of biology and the health sciences. The domain encompasses a wide range of topics, such as statistical genetics, design and analysis of randomized control trials, modelling time-to-event data. Research in biostatistics is often motivated by particular biomedical questions or applications which raise methodological investigations into a general mathematical or statistical framework of the problem. Thus, biostatistics is a discipline which requires both a solid foundation in inference and asymptotic theory mixed with versatility and interdisciplinary collaborative skills. A sample of areas of interest of the group members includes the following topics:
Specific techniques developed or extended in the group include semi- and non-parametric response modelling, data quality in genomic studies, Bayesian techniques for diagnostic testing, g-estimation and inference of dynamic treatment regimes, and high-dimensional propensity scores.
Examples of applications of statistics and probability in epidemiologic research. Sources of epidemiologic data (surveys, experimental and non-experimental studies). Elementary data analysis for single and comparative epidemiologic parameters.
Foundations of causal inference in biostatistics. Statistical methods based on potential outcomes; propensity scores, marginal structural models, instrumental variables, structural nested models. Introduction to semiparametric theory.
Statistical methods for multinomial outcomes, overdispersion, and continuous and categorical correlated data; approaches to inference (estimating equations, likelihood-based methods, semi-parametric methods); analysis of longitudinal data; theoretical content and applications.
Common data-analytic problems. Practical approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients.
This course will provide a basic introduction to methods for analysis of correlated, or dependent, data. These data arise when observations are not gathered independently; examples are longitudinal data, household data, cluster samples, etc. Basic descriptive methods and introduction to regression methods for both continuous and discrete outcomes.
Brève introduction aux concepts génétiques; épidémiologie génétique, concepts et introduction; études d’agrégation familiale; analyse de liaison; études d’association de population; analyse de liaison et études d’association pour traits quantitatifs.
7 au 23 novembre 2018
Multivariable regression models for proportions, rates, and their differences/ratios; Conditional logistic regression; Proportional hazards and other parametric/semi-parametric models; unmatched, nested, and self-matched case-control studies; links to Cox's method; Rate ratio estimation when "time-dependent" membership in contrasted categories.
Advanced applied biostatistics course dealing with flexible modeling of non-linear effects of continuous covariates in multivariable analyses, and survival data, including e.g. time-varying covariates and time-dependent or cumulative effects. Focus on the concepts, limitations and advantages of specific methods, and interpretation of their results. In addition to 3 hours of weekly lectures, shared with epidemiology students, an additional hour/week focuses on statistical inference and complex simulation methods. Students get hands-on experience in designing and implementing simulations for survival analyses, through individual term projects.