1. Course Objectives
Define the concepts of Biostatistics, and common terminologies used
Describe the different types of
Scales of measurements
Populations, and methods of obtaining samples from these populations
Demonstrate the
ability to process, analyze and present data using appropriate simple statistical ,and make correct interpretation and conclusions
use of Statistical software's (EPIINFO, and STATA SSP)
Define and concept of hypothesis testing, and describe the hypothesis testing using one-sample t-test, two-sample t-test, and paired t-test, Z-test and Chi-square
Describe ANOVA and F-test and their application
Define and describe the concept of non-parametric tests including sign, and sign-rankl and Mann-Whitney Test

2. Course outline-1
Introduction to the concepts and applications of biostatistics, and common terminologies, and different sources of data
The different types of scales of measurements
(Nominal, ordinal, interval, ratios)
Data Summary measures
Measures of central tendency, of dispersion etc..,
Data presentation methods (tabular and graphical)
Probability distributions including
the normal, t-distribution, chi-square and F-distributions
Sampling and sampling procedures; Population; target and study populations, sample,
selection of sample (SRS, systematic, cluster, stratified, pps and multi-stage sampling);
sample size determination (cross-sectional cases-control and cohort studies)

3. Estimation (point and interval estimation)
and introduction to hypothesis testing (study and statistical hypothesis), p-values using t-test, Z-test and chi-square
Measures of association (correlation coefficients, ORs and RR, and their confidence intervals
Introduction to the concept of tests and when to use them
signed test, signed rank test, and Mann-Whitney test
Standardization of Rates and proportions
Introduction of the use of statistical softwares for simples data analysis techniques

4. MUK-SPH Applied Biostatistics 7103
Grading 1 Final university exam (70%) 2 Take home/in class tests (30%) (Pass mark 60%)???? 3 Take home exercises & within class Reviews (class participation is required)
MPH Sept Fredrick E Makumbi

5. MUK-SPH Applied Biostatistics 7103
Biostatistics Introduction, and Descriptive statistics Makerere University- Department of Veterinary Public Health
MPH Sept Fredrick E Makumbi

6. Objectives
Concepts, terminologies, uses and importance in public health and animal sector
Scales of measurement and related terms

7. Concepts, terminologies, uses and importance
Definition of Biostatistics
Study of statistics as applied to biological areas
E.g Laboratory experiments, medical , health and animal sciences research
Reasons for focusing on biostatistics
Targeted teaching of health and animal Professionals
Gives an understanding of application of statistical
methods

8. Biostatistics A way of summarizing information (data)
A way of thinking about data :
Collection
Analysis
Presentation to an audience
A body of methods that enables us to draw conclusions from data
Includes both descriptive statistics and statistical inferences
A tool for estimating and verifying underlying truths and laws of nature
Biostatistics is statistics applied to the biological sciences, medicine and health

9. Population and Sample
Most research studies are done on samples of a specified population
The population is the set of objects, measurements or individuals that the researcher is interested in studying
The sample is a subset of the population – the group that is actually studied

10. Population and Sample
Most research studies are done on samples of a specified population
The population is the set of objects, measurements or individuals that the researcher is interested in studying
The sample is a subset of the population – the group that is actually studied
Why study only a sample
Population may be too big ex. All people in Africa who have had an episode of malaria
Might be too costly
Especially prospective studies which follow patients forward in time
Population may be hypothetical ex. Patients’ response to a new treatment or future subjects with a certain disease

11. Concepts and terminologies
Variable
A quantity that may vary from object to object
Or simply “what is being observed or measured ”
e.g. height, which varies from person to person.or age or temperature, etc
Value
Contents of a variable (quantitative or qualitative)
height (in cm)=136, 159, 182, 153, 140

12. Concepts and terminologies
Sample (or data set)
A collection of values of one or more variables as part of a larger population (a part of the population from which we actually collect information)
Element (record/observation)
A member of the sample
Sample space or population
A set of all possible values of a variable (the entire group from which we want information).

13. Types of variables
Qualitative (Categorical) Variables
Has values that are intrinsically non-numerical (categorical)
e.g. color of skin, gender, tribe, country of residence
Quantitative variables
Has values that are intrinsically numerical
e.g. birth weight (in kg)=2.3, 3.8, 1.8, 4.2;
height, age

14. Scales of measurement Type of variables collected
Nominal/Classificatory Scale
Observations fall into categories
Named categories with no implied order
(e.g. cars by model, profession, marital status, etc…)
Ordinal/Ranking Scale
Ordered categories, but differences between categories can not be considered to be equal
Ordered data, lowest to highest (e.g. positions, scores such as Agree-disagree, Military ranks etc…)

15. Scales of measurement Type of variables collected Interval
Equal distances between values, but zero point is arbitrary
Can change without impacting on the meaning as long as the distances are equal
E.g. IQ
Ratio
. Equal intervals between values and a meaningful zero point
(height, weight, BP)

16. Concepts and terminologies
Statistical inference
“the attempt to draw conclusions concerning all members (population) from observations of only some of them (sample)” (Rune 1959)
A parameter
A numerical descriptor of a population e.g. population mean (can be age, height, weight, etc.).
A statistic
A numerical descriptor/characteristic of a sample e.g. sample mean.

17. Scales of measurement Type of data collected Discrete data
Takes on only certain numeric values such as count of events,
number of children (usually whole numbers)
Continuous data
Takes on values including decimal places, within a range

18. SUMMARIZING DATA Measures of central tendency/location Mean Median
Mode
Measures of dispersion/Variability
Range
Percentile
Standard deviation
Interquartile range
Graphical display of data
Graphs
Box plot
Stem and leaf

19. SUMMARY OF BIVARIATE ANALYSIS TECHNIQUES
INDEPENDENT VARIABLE
(LIKELY CHARACTERISTIC )(X)
DEPENDENT VARIABLE
LIKELY CHARACTERISTIC(Y)
METHOD FOR ANALYSIS
Nominal(binary)
Nominal(Binary)
Chi-square
OR/RR
Fishers Exact Test
Nominal (multi-categorical)
Numerical-independent samples
Independent samples t-test
Numerical-dependent samples
Paired t-test
Nominal(multi-categorical)
Numerical
One way Anova
Numerical(continous-Fixed)
Numerical(continous-Random)
Linear Regression
correlation

20. Summary of techniques for Questions involving two or more Independent variables(Multi-variate analysis)
Outcome/Dependent Variable(Y)
Independent(2 or more)(X) (x1, x2, x3, etc)
Method
Nominal(Binary)
Nominal
Logistic Regression/Binary Logistic regression/Log Linear
Nominal(Dichotomous outcomes or Binary
Nominal + Numerical
Logistic regression
Numerical
ANOVA/MANOVA
Numerical (fixed)
Multiple Linear regression
Correlation
Numerical but time factored(Censored )
Numerical + Numerical
Cox Regression
Nominal with suspected Confounding
Analysis of Covariance(ANACOVA)
Mantel-Haenszel(Stratified Analysis)
Logistic Regression

21. Summary of Non-Parametric Tests and their Corresponding parametric tests
Nonparametric Equivalent
Single Sample t-test, paired t-test
Sign test
Paired t-test
Wilcoxon Signed Rank Sum Test
Two sample t-test(Independent samples t-test/unpaired t-test)
Mann-Whitney U test(Wilcoxon Sign Rank Test)
Product-Moment correlation(r) or Pearson Correlation
Spearman’s rank correlation or Kendall’s rank correlation
One-Way Analysis of Variance (ANOVA)
Completely Randomized designs
Kruskal-Wallis test
Two way Anova(Randomized Complete Block Design or Repeated Measurements Design)
Friedmans Two way Anova
Chi-squared test
Fisher’s Exact Test