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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 nonparametric 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.
 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
 Explanatory data analysis in Stata
 Hypothesis testing
 Hypothesis testing background
 Definitions
 Statistical inference
 Generalizability
 Confidence intervals in clinical research
 Pvalues 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
 Chisquare goodness of fit test
 Chisquare test of independence
 Chisquare test of homogeneity
 Proportion test
 Fisherâ€™s exact test
 McNemar matched pairs for binary response
 Nonparametric methods
 Sign test
 Wilcoxon signedrank test
 Median test
 Wilcoxon signedsum (MannWhitney) test
 KolmogorovSminorv goodnessoffit test
 KruskalWallis one way analysis of variance
 Friedman twoway analysis of variance
 Spearman's rank order correlation
 Kendall's correlation
 Hypothesis testing background
 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
 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
 MantelHaenzsel 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
 Epidemiological studies
 Clinical research designs
 Study population
 Exposure and outcome
 Study designs
 Causation
 Case report and series
 Crosssectional studies
 Cohort studies
 Cohort study design
 Ascertainment
 Advantages
 Disadvantages
 Poisson regression for cohort studies
 Casecontrol studies
 Casecontrol study design
 Advantages
 Disadvantages
 Unconditional logistic regression
 Conditional logistic regression
 Clinical research designs
 Other analysis
 Misclassification
 Definition
 Simple linear regression
 Nondifferential 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 KaplanMeier curve)
 Logrank tests
 Hazard rates
 Cox proportional hazard models
 Misclassification