Training Course on Biostatistics for Health Research

Module 1 – Introduction to Biostatistics and Health Research
Subtopics & Theory:
Role of biostatistics in healthcare and research.
Types of data: qualitative vs. quantitative, discrete vs. continuous.
Measurement scales and levels of measurement.
Basic concepts: population, sample, parameters, statistics.
Practical Exercises:
Group discussion: identifying data types in real health studies.
Workshop: defining populations and samples for research questions.

Module 2 – Descriptive Statistics and Data Visualization
Subtopics & Theory:
Measures of central tendency: mean, median, mode.
Measures of dispersion: variance, standard deviation, interquartile range.
Frequency distributions and graphical representations: histograms, boxplots, bar charts.
Data cleaning, coding, and preparation for analysis.
Practical Exercises:
Hands-on: summarize datasets using descriptive statistics.
Workshop: create visualizations using statistical software.

Module 3 – Probability and Sampling Methods
Subtopics & Theory:
Basic probability concepts and probability distributions.
Common distributions in health research: binomial, Poisson, normal.
Sampling techniques: random, stratified, cluster, and systematic sampling.
Sampling error and sample size determination.
Practical Exercises:
Simulation: generating probability distributions.
Case study: designing a sampling strategy for a health survey.

Module 4 – Study Design and Data Collection
Subtopics & Theory:
Types of study designs: observational vs. experimental, cross-sectional, cohort, case-control,
randomized trials.
Bias, confounding, and effect modification.
Data collection methods, tools, and quality assurance.
Ethical considerations in biostatistical research.
Practical Exercises:
Workshop: designing a study and selecting appropriate data collection methods.
Group activity: identifying potential biases and mitigation strategies.

Module 5 – Inferential Statistics: Estimation and Hypothesis Testing
Subtopics & Theory:
Sampling distributions and central limit theorem.
Point and interval estimation.
Hypothesis formulation and testing (null vs. alternative).
Parametric vs. non-parametric tests.
Practical Exercises:
Hands-on: perform t-tests, chi-square tests, and ANOVA.
Case study: interpreting p-values and confidence intervals in health research.

Module 6 – Correlation and Regression Analysis
Subtopics & Theory:
Correlation: Pearson and Spearman coefficients.
Simple linear regression and multiple regression models.
Model assumptions, diagnostics, and interpretation.
Logistic regression for binary outcomes.
Practical Exercises:
Workshop: fit regression models using statistical software.
Group activity: interpret coefficients, odds ratios, and model outputs.

Module 7 – Survival Analysis and Time-to-Event Data
Subtopics & Theory:
Concepts of survival and censoring.
Kaplan-Meier survival curves.
Log-rank test for group comparisons.
Cox proportional hazards regression.
Practical Exercises:
Simulation: generate survival data and plot survival curves.
Case study: analyze time-to-event outcomes in clinical research.

Module 8 – Advanced Statistical Modeling
Subtopics & Theory:
Repeated measures and longitudinal data analysis.
Generalized linear models (GLMs).
Multilevel and hierarchical modeling.
Model selection and validation techniques.
Practical Exercises:
Workshop: implement GLMs and hierarchical models on sample datasets.
Group activity: evaluate model fit and interpret results.

Module 9 – Statistical Software Applications
Subtopics & Theory:
Overview of R, SPSS, Stata, or SAS for biostatistical analysis.
Data management and coding practices in software.
Performing descriptive and inferential analyses.
Generating tables, graphs, and reports for publication.
Practical Exercises:
Hands-on: analyze a real health dataset using software.
Workshop: produce publication-ready tables and visualizations.

Module 10 – Interpretation, Reporting, and Critical
Appraisal
Subtopics & Theory:
Interpreting statistical outputs in the context of health research.
Reporting results in publications, presentations, and reports.
Critical appraisal of biostatistical methods in literature.
Integrating statistical findings into evidence-based decision making.
Practical Exercises:
Group exercise: critically appraise a published health research article.
Capstone project: analyze a dataset, interpret results, and present findings