Biostatistics introduction.pptx
- 1. Introduction to Biostatistics 1 Hirbo Shore (MPH,Assist. Professor) SPH-CHMS|HU December 2020
- 2. At the end of the course students will able to; – The role of biostatistics in health sciences and the use of biostatistical methods in field of health sciences – Describe methods of data collection, recording, coding and handling data – Compute numerical summary measures and present data – Identify and make use of data from existing health record Course objectives: 2
- 3. – Apply different techniques of sampling and determine sample size – Explain the context and meaning of statistical estimation and statistical significance – Measure associations between outcome and exposure Course objectives, … 3
- 4. 1. Introduction 2. Measures of central tendency and dispersion 3. Probability and probability distribution 4. Sampling and sampling distribution 5. Basic Concepts of Inference 6. Introduction to correlation and Regressions Course contents 4
- 5. 1. M. Pagano & K. Gauvereau: Principles of Biostatistics 2. Daniel W. : Biostatistics:AFoundation for analysis in Health Sciences 3. Colton T. : Statistics in Medicine 4. Bland M. :An Introduction to Medical Statistics 5. David S. Moor, G.P.McCable: Introduction to the practice of Statistics 6. Kleinbaum, K.Muller:Applied Regression 7. Analysis and other Multivariate Methods 8. L. D. Fisher & G. Van Belle: Biostatistics References 5
- 6. 8. Kirkwood B. : Essentials of Medical Statistics 9. A. R. Feinstein: Principles of Medical Statistics 10. R. G. Knapp & M. C. Miler: Clinical epidemiology and biostatistics 11. D. J. Sheskin: Hand book of Parametric and Nonparametric Statistical Procedure 12. Armitage P. & Berry G. : Statistical Methods in Medical Research 13. P. S.R.S. Rao: Sampling methodologies with application 14. R.N.Forthofer & E. S. Lee: Introduction to Biostatistics References, … 6
- 8. • Why we need Biostatistics in Public Health? • What is public health? – “Public Health is the science and art of preventing disease, prolonging life and promoting health through the organized efforts of society.” (WHO) Chapter 1 Introduction 8
- 9. • Assessment: Identify problems related to the public’s health, and measure their extent • Policy Setting: Prioritize problems, find possible solutions, set regulations to achieve change, and predict effect on the population • Assurance: Provide services as determined by policy, and monitor compliance • Evaluation: how well are they performed? The Functions of Public Health 9
- 10. • A field of study concerned with the collection, organization and summarization of data, and the drawing of inferences about a body of data when only part of the data are observed. • The science of collecting, describing, and interpreting data • Help scientists to describe our perception of the world What is statistics 10
- 11. What is biostatistics 11 • Deals with the collection, organization, summarization, and analysis of data in the fields of biological, health, and medical sciences including other life sciences • An application of statistical method applied to life and health sciences • Statistical tools/methods applied to biological phenomenon characterized by the resultant of interaction between the genetic architecture and the environmental factors under which lives exist
- 12. What is biostatistics,… 12 • Emerged as one of the most important disciplines in recent decades due to; – interactive advancements in the fields of statistics, computer science, and life sciences • To address new challenges due to new sources of data and growing demand for biostatisticians with sound background to face the needs
- 13. What is biostatistics,… 13 • Biostatistics deals with designing studies, analyzing data, and developing new statistical techniques to address the problems in the fields of life sciences • It includes statistical analysis with special focus to the needs in the broad field of life sciences including public health, biomedical science, medicine, biological science, community medicine
- 14. What is biostatistics,… 14 • At the current stage of information boom in every sector, we need to attain an optimum decision utilizing the available data. • The information used for making decision through a statistical process is called data. • The decision about the underlying problem is to be made on the basis of a relatively small set of data that can be generalized for the whole population of interest
- 15. • Experimenter faces two challenges: 1. to find the values that summarize the basic facts about the unknown characteristics of the population as sought in the study 2. to make sure that the values obtained have adequate statistical support for generalizing the findings for the domain or more specifically the population from where the sample is drawn 15
- 16. • The fundamental objective is to learn the basics about two major aspects of statistics: I. descriptive statistics II. inferential statistics. 16
- 17. Major aspect of statistics 17 • The descriptive statistics deals with organization, summarization, and description of data using simple statistical techniques • the inferential statistics link the descriptive statistics measures from sample with the larger body of data, called population from which the smaller data sets are drawn
- 18. Population & Sample 18 • The information used to decision about the underlying problem is to be made on the basis of a relatively small set of data that can be generalized for the whole population of interest • The term population is used with specific meaning and a defined domain
- 19. Population 19 • E.g. 1 In a study of disease among under five children; • The population is comprised of every child of under 5 years of age In the study substance use among university students The study population is students studying in universities
- 20. Population (N) 20 • Target population:Acollection of items that have something in common for which we wish to draw conclusions at a particular time • E.g. – Under five children in Harar – Haramaya University students – Adults 25 years or older in Dire Dawa
- 21. Population, … 21 • Study Population: The specific population from which data are collected or accessible population • E.g. – under five children in Harar in Hakim Woreda – Haramaya University students in CHMS – Adults 25 years or older in Kebele 8
- 22. Sample (n) 22 • Asubset of a study population, about which information is actually obtained • In reality, it is difficult to conduct the study on the whole population due to; – cost – Time – skilled manpower needed to collect quality data
- 23. Sample,… 23 • E.g. – under five children in randomly selected from Hakim Woreda – Haramaya University students randomly selected from Schools and department in CHMS – Adults 25 years or older in randomly selected from Kebele 8 in Dire Dawa
- 24. Population and sample Target population Study population Sample 24
- 25. Parameters and statistics 25 • Parameter: Adescriptive measure computed from the data of a population – E.g • polation mean (µ) • Population variance(σ2) • Population standard deviation (σ), etc Statistic:Adescriptive measure computed from the data of a sample. E.g. • Sample mean(𝑥) • Sample variance (S2) • Sample standard Deviation (S), ete.
- 26. Data 26 • Raw material of statistics • numbers that resulting from the taking a measurement and those that result from the process of counting.
- 27. Variable 27 • Characteristic that it takes on different values in different persons, places, or things • Some examples of variables include diastolic blood pressure, heart rate, the heights of adult males, the weights of preschool children, and the ages of patients seen in a dental clinic
- 28. Types of variable 28 • Quantitative Variables – that can be measured in the usual sense – Measurements made on quantitative variables convey information regarding amount – Example; obtain measurements on the heights of adult males, the weights of preschool children, and the ages of patients seen in a dental clinic
- 29. Quantitative variable 29 Divided into two; 1. Discrete • Characterized by presence of gaps between to subsequent number • E.g. number of patients admitted, number empty bed 2. Continuous • Characterized by absence of gaps between two consecutive number • E.g. weight, height, bloods pressure, BMI, etc.
- 30. Qualitative Variables 30 • Characteristics are not capable of being measured • Many characteristics can only be categorized only • Measurements made on qualitative variables convey information regarding attribute. • Example: when an ill person is given a medical diagnosis, a person is designated as belonging to an ethnic group, or a person, place, or object is said to possess or not to possess some characteristic of interest
- 31. Qualitative variable,… 31 • Divided into two 1. Nominal variable – Name or symbol assigned to characteristic being measured – Categories in variable are mutually exclusive – E.g. sex, cause of disease, caused of death, marital status, place of residence
- 32. 2. Ordinal variable – Similar to nominal attribute assigned to characteristics – Categories are not only mutually exclusive, but also have some order or rank – E.g. cancer stage, class performance, height as taller/average/shorter, condition of patients; unimproved/slightly improved, improved, much improved, etc. 32
- 33. Scale of Measurement 33 • Clearly not all measurements are the same. • Measuring an individuals weight is qualitatively different from measuring their response to some treatment on a three category of scale, “improved”, “stable”, “not improved”. • Measuring scales are different according to the degree of precision involved. • There are four types of scales of measurement.
- 34. Scale of measurement,… 34 1. Nominal scale: – uses names, labels, or symbols to assign each measurement to one of a limited number of categories that cannot be ordered. – Used only to qualitatively classify or categorize not to quantify • Examples: sex, race, marital status, caused diseases, Blood type, causes death, religions
- 35. Scale of measurement,… 35 2. Ordinal scale: – characterized by the ability to measure a variable in terms of both identity and magnitude – Categorize a variable and its relative magnitude in relation to other variables – assigns each measurement to one of a limited number of categories that are ranked in terms of a graded order. • Examples: Patient status, Cancer stages
- 36. Scale of measurement,… 36 3. Interval scale: – Builds on ordinal measurement by providing information about both order and distance between values of variables – assigns each measurement to one of an unlimited number of categories that are equally spaced. It has no true zero point. – The numbers on an interval scale are scaled at equal distances.
- 37. Scale of measurement,… 37 Interval scale,… – Numbers scaled at equal distances – No absolute zero point; zero point is arbitrary. • Example: – Temperature measured on Celsius or Fahrenheit – Time of each day in the meaning of a 12-hour clock – IQ test (intelligence scale) – Measuring an income as a range
- 38. Scale of measurement,… 38 4. Ratio scale: – measurement begins at a true zero point and the scale has equal space. – Allow for the use of sophisticated statistical techniques. – Examples: Height, weight, Age, budget, number of students, area
- 39. The end ! THANK YOU FOR THANK YOU FOR !! THANK YOU FOR YOURATTENTION!! 39