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Biostatistics and epidemiology for public health professionals

It is important to note that this course assumes some general undertanding of data management routines in Stata or R. For those who have never used Stata or R, please talk to us before applying.

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. Career pathway with these areas include: public health physicians, epidemiologists, biostatisticians, surveillance officers, monitoring and evaluation coordinators, data managers, research officers, among others.

Learning objectives:

  • Perform basic and advanced data management routines on survey data sets.
  • Perform explanatory data analysis tasks with easy.
  • Create univariate and bivariate graphs / charts.
  • Carry out classical tests of hypothesis and draw clear inferences and conclusion to them.
  • Perform non-parametric tests and draw conclusions.
  • Come up with linear, poisson and logistic regression models, tests their validity, make conclusions and use such models to make prediction about the general population.
Dates Monthly: Starts every First Monday
Duration: 2 weeks (10 days)
Time 0830Hrs - 1630Hrs EAT (Mon - Fri)
Delivery Classroom (online also available)
Charges KES 125,000 ($1,250)
The course can be customized to fit your specific needs in terms of course content, date, time and venue (including online sessions). Please talk to us for the customization.
  1. Descriptive statistics and graphics
    • Explanatory data analysis in Stata
      • One way frequency tables
      • Crosstabutions
      • Tables of descriptive statistics: tabstat command
      • Tables of descriptive statistics: table command
      • Exporting Stata tables to Excel with tabout command
      • Multiple responses with mrtab command
    • Introduction to graphing
      • 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
      • Pie chart
      • Simple bar graph
      • Grouped/clustered bar graph
      • Stacked bar graph
      • Confidence interval bands
      • Line graph
      • Combined graph
      • Area bar graph
      • Scatter plot
      • Box plot
  2. Hypothesis testing
    • Hypothesis testing background
      • 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 z tests
      • One sample t tests
      • Two sample independent z tests
      • Two sample independent t tests
      • Two sample paired t test
      • One way analysis of variance
      • Two way analysis of variance
      • Analysis of covariance
    • Analysis of contingency tables
      • Proportion test
      • Chi-square goodness of fit test
      • Chi-square test of independence
      • Chi-square test of homogeneity
      • Proportion test
      • Fisher’s exact test
      • McNemar matched pairs for binary response
    • 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's rank order correlation
      • Kendall's correlation
  3. Regression analysis and correlation
    • Pearson correlation coefficient
    • Linear regression for continuous response
      • Overview of linear regression analysis
      • Simple linear regression
      • Multiple linear regression
      • Prediction in linear regression
      • Testing for assumptions of linear regression
      • Regression diagonostics for linear regression
  4. Basics of epidemiology
    • Overview to 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
    • 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
  5. Epidemiological studies
    • Clinical research designs
      • Study population
      • Exposure and outcome
      • Study designs
      • Causation
    • 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
  6. Other analysis
    • Misclassification
      • Definition
      • Simple linear regression
      • Non-differential misclassification
      • Differential misclassification
      • Assessing misclassification
    • 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
    • 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

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