Epidemiology and Bio-statistics using Stata

Course date: 05/10/2020 to 16/10/2020 Duration: 10 Days Course fee: USD 1,600, KES 130,000

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 OBJECTIVES

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:

2. 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:

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

 

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

 

Misclassification

                   Definition

                   Non-differential misclassification

                   Differential misclassification

                   Assessing misclassification

 

Module 8:

Confounding

                   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

 

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TRAINING CUSTOMIZATION

This training can also be customized for your institution upon request. You can also have it delivered your preferred location.

For further inquiries, please contact us through Mobile: +254 732 776 700 or Email: training@fineresultsresearch.org

 

REQUIREMENTS

Participants should be reasonably proficient in English.  During the trainings, participants should come with their own laptops.

 

TRAINING FEE

The course fee covers the course tuition, training materials, two break refreshments, lunch, and study visits.

 

ACCOMMODATION

Accommodation is arranged upon request. For reservations contact us through Mobile: +254 732 776 700 or Email: training@fineresultsresearch.org

 

PAYMENT

Payment should be transferred to FineResults Research Limited bank before commencement of training. Send proof of payment through the email: training@fineresultsresearch.org

 

CANCELLATION POLICY

                   All requests for cancellations must be received in writing.

                   Changes will become effective on the date of written confirmation being received.

 

 

Course Date: 05/10/2020 to 16/10/2020 Duration: 10 Days Course fee: USD 1,600 , KES 130,000
Call us on +254 732 776 700/ +254 759 285 295
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