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

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)

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

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. 2007 Fredrick E Makumbi

MUK-SPH Applied Biostatistics 7103 Biostatistics Introduction, and Descriptive statistics Makerere University- Department of Veterinary Public Health MPH Sept. 2007 Fredrick E Makumbi

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

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

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

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

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

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

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).

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

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…)

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)

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.

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

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

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

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

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