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Core Courses

EPIDEMIOLOGY BIOSTATISTICS
  • Introduction to epidemiology
    • History, concepts, principles and methods relevance to clinical & healt research, evidence-based medicine, patient care and decision making
  • Designs of epidemiological research
    • Cross-sectional studies, surveys, ecological studies
    • Case control studies, cohort studies
    • Clinical trials, systematic reviews and meta-analyses
  • Research questions that can be answered in epidemiological studies
    • Etiology and causes of disease
    • Evaluation of diagnostic methods, efficacy and effectiveness
    • Investigation of harms and adverse effects
    • Health needs and policy evaluation
  • Making sense of epidemiological data
    • Estimation and confidence intervals
    • Control of confounding; identification of interaction and dose-response relationship
    • Stratified analysis, standardization and multiple regression
  • Evidence-based medicine
    • Critical appraisal skills with respect to different study designs
    • Literature search, meta-analysis and systematic review
  • Major epidemiological concepts
    • Validity, biases and confounding
    • Quality and grading of evidence
    • Causal inference, generalizability and applicability
    • Measures of disease frequency & occurrence
    • Measures of association
  • Field work techniques
    • Protocol development and sample size planning
    • Design and evaluation of measurement and data collection
    • Use of routinely collected data in health and disease
    • Quality control methods
    • Statistical, tabular and graphical presentation of data and report writing skills
  • Other topics
    • Infectious diseases epidemiology
    • Environmental health and nutritional epidemiology
    • Ethical considerations in epidemiological research
    • Epidemics, outbreaks and outbreak investigation
  • Introduction to biostatistics
    • Conceptual understanding, application and interpretation on a broad range of commonly used statistical methods in medicine and public health research
  • Modern statistical analysis in depth with diagnostic procedures and model building techniques
  • Analysis of variance and regression
    • One and two-way ANOVA
    • Simple and multiple regressions
  • Categorical data analysis
    • Logistic regression
  • Survival data analysis
    • Kaplan-Meier estimation, the log-rank test and Cox proportional hazards regression
  • Advanced logistic regression
    • Multinomial logistic model
    • Proportional odds model for ordered categorical data
    • Conditional logistic model for matched case-control designs
  • Longitudinal data analysis
    • Repeated measures ANOVA and MANOVA
    • Generalized linear mixed effect models
    • Generalized estimating equations
  • Analysis of questionnaire data
    • Translation and validation of questionnaires
    • Exploratory and confirmatory factor analysis
    • Structural equation models
  • Other topics
    • Multilevel Modeling
    • Poisson & Negative Binomial Regression
    • Regression Trees

 

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