This CPD-certified workshop was for early career mental health researchers and data scientists to learn epidemiological methods using R programming. It was part of a series focusing on innovative data science approaches in mental health research, hosted by DATAMIND and MQ, in collaboration with HDR. Led by experienced mental health epidemiologists and R data specialists, the workshop covered study design, causal inference, and basic to intermediate analysis methods in a mental health research context.
🎤 Introduction by Naomi Launders
Introduction to the day, aims, and objectives, outlining the structure of the workshop
Understanding observational epidemiology and its implications
Overview of the main steps in a study: design, conduct, analysis, and interpretation
🔍 Study Design Part 1 by Ellen Thompson
Exploring different types of studies and data, defining the research question, and cohort definition
🔍 Study Design Part 2 by Ellen Thompson
Understanding sample size, power considerations, and addressing missing data in study design
📊 Analysis Part 1 by Naomi Launders
Exploring prevalence, incidence, ratios, and proportions
Introduction to hypothesis testing, confidence intervals, and p-values
Overview of univariable regression techniques: linear, logistic, Poisson
Introduction to univariable survival analyses including Cox and other models
🔍 Causal Inference by Annie Jeffery
Understanding the association and causation in epidemiological research
Addressing sources of bias/error, confounding, and Directed Acyclic Graphs (DAGs)
📊 Analysis Part 2 by Annie Jeffery
Techniques for adjusting for confounders through multivariable analysis
Exploring effect modification and mediation analysis techniques
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