Epidemiology and Bio-statistics using Stata


Participants will learn the principles of epidemiology and biostatistics and gain skills in using epidemiological and biostatistical tools to describe, monitor and investigate the determinants of population health.

The statistical background required to conduct research, describe, summarize, develop hypothesis, assess associations, analyze data, interpret and communicate results will be studied comprehensively. The course targets health care professionals who wish to consolidate their knowledge and skills and increase their understanding of the importance of epidemiology and statistics in public health today.


At the end of the course, participants will be able to:

• Use epidemiological and biostatistical tools to describe, monitor and investigate the determinants of population health.

• Gain key statistical background necessary for conducting valid research

• Describe and summarize data

• Develop hypothesis and analyze data. • Interpret and communicate results

Module 1:
1. Data management & graphics in Stata

Introduction to Stata
• Starting Stata
• Setting layout
• Directory management commands
• Data types in Stata
• Using Stata as a calculator
• Stata command and options
• Stata do-files
• Creating data sets directly in Stata
• Rename of variables
• Managing variables and/or variable properties
• Importing data from other software
• Exporting data to other software
• Loading data into the memory
• The in and if qualifiers
• The by prefix
• Create subsets (keep and drop)
• Create random variables (from distributions)
• Random sampling
• Sort variables
• Change order of variables
• Count number of observations
• Generate sequential numbers
• Working with dates
• Viewing data sets
• Interrupting computations
• Help
Module 2:
Creating and changing variables

• Create new variables
• Extended generate command
• Duplicate an existing variable
• Replace contents of a variable
• Convert numeric to string
• Convert string numbers to numeric
• Convert numeric values to missing and vice versa
• Recode string variables
• Decode numerically coded variables
• Transforming a continuous variable to categorical
• Reduce number of categories of a categorical variable
• Managing duplicates

Transforming variables and data sets
• Split variables
• Extract parts of variables
• Standardize variables
• Create dummy variables
• Create separate variables
• Transpose variables
• Stack variables
• Unstack variables
• Appending data sets
• Combining data sets by a common variable
• Convert datasets from wide to long
• Convert datasets from long to wide
• Some application to data cleaning

Introduction to Stata graphics
• The graphics dialog windows
• Graph elements (x and y labels, titles, legends)
• Graph appearance (marker symbol, color, size, line
• width, pattern, e.t.c)
• Multiple graphs (by option)
• Graphics syntax
• Adding text and annotations to graphs
• Saving and printing graphs
• Combining active graphs into one figure
• Graphics window (interactive plotting)
• Common graphs and charts
Module 3:
Biostatistics: Introduction to statistical concepts

• Review of research process
• Research designs
• Sampling techniques
• Types of data
• Descriptive statistics
• Graphs for descriptive statistics
Hypothesis testing
• Definitions
• Statistical inference
• Generalizability
• Confidence intervals in clinical research
• P-values in clinical research
• Hypothesis testing
• Interpreting hypothesis test results

Tests of differences in population means
• One sample t tests
• Two sample independent t tests
• Two sample paired t test
• One way analysis of variance
• Two way analysis of variance
Module 4:
Analysis of contingency tables

• Introduction
• Two by two tables: Proportion test
• Two by two tables: Fisher’s exact test
• McNemar matched pairs for binary response
• Other measures of association

Non-parametric methods
• Sign test
• Wilcoxon signed-rank test
• Median test
• Wilcoxon signed-sum (Mann-Whitney) test
• Kolmogorov-Sminorv goodness-of-fit test
• Kruskal-Wallis one way analysis of variance
• Friedman two-way analysis of variance
• Spearman rank correlation
• Nonparametric regression analysis

Linear regression and correlation
• Overview
• Pearson correlation analysis
• Simple linear regression
• Multiple linear regression
• Interpret results from linear regression
• Regression diagnostics
Module 5:

Measures of disease frequency
• Importance of measures of disease frequency
• Measures of risk and association
• Risk verses prevention
• Prevalence
• Incidence, cumulative incidence & incidence density
• Relationship between prevalence and incidence
• Stratification of disease frequency
Module 6:
Measures of effect for categorical data

• Risk difference
• Risk ratio
• Attribute fraction
• Attribute risk
• Relative risk
• Odds ratio

Measures of effect for stratified categorical data
• Mantel-Haenzsel test
• Odds ratio for stratified data
• Odds ratio for matched pairs studies
• Testing for trends

Vital statistics
• Introduction
• Death rates and ratios
• Measures of fertility
• Measures of morbidity

Clinical research designs
• Study population
• Exposure and outcome
• Study designs
• Causation
Module 7:
Case report and series

Cross-sectional studies
Cohort studies
• Cohort study design
• Ascertainment
• Advantages
• Disadvantages
• Poisson regression for cohort studies

Case-control studies
• Case-control study design
• Advantages
• Disadvantages
• Unconditional logistic regression
• Conditional logistic regression

• Definition
• Non-differential misclassification
• Differential misclassification
• Assessing misclassification
Module 8:

• Confounding overview
• Evaluation of confounding factors
• Confounding by indication

Remedies for confounding
• Restriction
• Stratification
• Matching
• Regression
• Randomization
• Interpretation after adjusting for confounding
• Unadjusted verse adjusted association: confounding

Effect modification
• Overview
• Synergy between exposure variables
• Effect modification verses confounding
• Evaluation of effect modification
• Effect modification in clinical research articles
• Effect modification on the relative and absolute scales
Module 9:
Introduction to survival analysis

• Overview
• Organizing survival data for computer use
• Censoring (right and left)
• Truncation (right and left)
• Plotting survival data (the Kaplan-Meier curve)
• Log-rank tests
• Hazard rates
• Cox proportional hazard models
Module 10:
Research ethics and statistics

• Introduction
• Protection of human research subjects
• Informed consent
• Equipoise
• Research integrity
• Authorship policies
• Data and safety monitoring boards

In person, or online, with live instructor

Course Information

Estimated Time: 10 DAYS

Categories: ,

Course Instructor

Owi_ghola Owi_ghola Author
This course does not have any sections.

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