Epidemiology And Bio-Statistics Using Stata

Introduction

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.

DURATION

10 Days

 course outline

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

Module 3:

2. Biostatistics

Introduction to statistical concepts

•      Review of research process
•                Research designs
•             Sampling techniques
• Types of data
•          Descriptive statistics
• Graphs for descriptive statistics

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

Module 5:

3. Epidemiology

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

Module 7:

Case report and serie

Cross-sectional studies

Cohort studies

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

Module 8:

Confounding

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

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