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Biostatistics

Biostatistics. A foundation for analysis in the health science. Yongli YANG Ph.D, Associate Professor Department of Biostatistics & Epidemiology, college of public health TEL: 67781249 E-mail: ylyang377@126.com. STATISTICS IN LIFE.

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Biostatistics

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  1. Biostatistics A foundation for analysis in the health science Yongli YANG Ph.D, Associate Professor Department of Biostatistics & Epidemiology, college of public healthTEL: 67781249 E-mail: ylyang377@126.com

  2. STATISTICS IN LIFE • GDP in China increased 7.7% in 2013 from the report of State Statistical Bureau. • Life expectancy is 74.83 year in 6th population census • Weather forecast in Zhengzhou

  3. Chapter I introduction to biostatistics Introduction Some basic concepts Basic step of statistical work Review questions and exercises

  4. introduction • We are frequently reminded of the fact that we are living in the information age. Appropriately, then, this subject is about information—how it is obtained, how it is analyzed, and how it is interpreted. The information about which we are concerned are called data, and the data are available to us in the form of numbers.

  5. Question 1 • We aim to explore whether smoking is harmful to your health. • How to explore? • Lung cancer, Heart disease, Other diseases?

  6. Lung cancer a/(a+b) smoking no lung cancer b compare conclusion Lung cancer c non- smoking c/(c+d) no lung cancer d a

  7. Smoking group Non-smoking group

  8. Question 2 It is obvious that generally men are taller than women, while some other women are taller than men. • Therefore, if you wanted to ‘prove’ that men were taller, you should measure many people of each sex. • How many people should you measure?

  9. ？ Question 3 • A doctor used a new drug to cure 5 AIDS patients. 4 of them are cured. • Conclusion: The cured rate of this drug was 80%. Is his conclusion right? Why or why not?

  10. A knowledge of statistics is like a knowledge of foreign languages or of algebra; it may prove of use at any time under any circumstances. A.L. Bowley

  11. Some basic concepts • Data • Statistics and biostatistics • Population and sample • Variable • Parameter and Statistic • Probability

  12. Data • Definition: The raw material of statistics is data. For our purses we define data as numbers. • Sources of data: Routinely kept records Surveys Experiments External sources

  13. Data Routinely kept records. Hospitals keep day-to-day records, which contain immense amounts of information on patients. When the need for data arises, we should look for them first among routinely kept records.

  14. Data Surveys If the data needed to answer a question are not available from routinely kept records, then logical source may be a survey. For example, the administration of the health department want to learn the numbers of hypertension in Zhengzhou, we may conduct a survey.

  15. Data Experiments Frequently the data needed to answer a question are available only as the result of an experiment. For example, a nurse wish to know which of several strategies is best for maximizing patient compliance.

  16. Data External sources The data needed to answer a question may already exist in the form of published reports. For example, statistical yearbook, population census……

  17. STATISTICS A science dealing with the collection, analysis, interpretation and presentation of masses of numerical data ----Webster’s international dictionary

  18. statistics The science and art of dealing with variation in data through collection, classification and analysis in such a way as to obtain reliable results. —— John M. Last —— A Dictionary of Epidemiology

  19. statistics • The tools of statistics are employed in many fields—demography, national economic, psychology, medicine……

  20. Biostatistics • When the data analyzed are derived from the biological sciences and medicine, we use the term “biostatistics” to distinguish this particular application of statistical tools and concepts.

  21. Population and sample We want to learn the average income of Beijing doctors in 2010. Suppose there are 20,000 doctors in Beijing in 2010. • To investigate all the doctors one by one (But it is consuming-time ) • 500 are drawn from which randomly. Then generalize the population average income from the incomes of 500 doctors.

  22. Population and sample Questions • What is study aim? • What is study population? • What is our observational unit? • What is sample? • What is sample size?

  23. Population and sample Answers • To learn the average income of Beijing doctors in 2010 • 20,000 doctors’ income • Individual • 500 doctors’ income • 500

  24. Population and sample population Definition：Population is the largest collection of entities for which we have an interest at a particular time. For example, we are interested in the weights of all the children enrolled in a certain country elementary school system, our population consists of all these weights.

  25. Population and sample population Population may be finite or infinite. If a population of values consists of a fixed number of these values, the population is said to be finite. If, on the other hand, a population consists of an endless succession of values, the population in an infinite one.

  26. Population and sample Sample Definition: A sample is a random part of population. Suppose our population consists of the weights of all the elementary school children enrolled in a certain country school system. If we collect for analysis the weights of only a fraction of these children, we have only a part of our population of weights, that is, we have a sample.

  27. Population and sample How to get a random part of population? • Simple random sampling • Systematic sampling • Stratified sampling • Cluster sampling

  28. 3 4 5 6 2 1 7 10 11 8 12 9 13 15 17 14 16 Sample If a sample of size n is drawn from a population of size N in such a way that every possible sample of size n has the same chance of being selected, the sample is called a simple random sample

  29. Variable If we observe a characteristic, we find that it takes on different values in different persons, places, or things, we label the characteristic a variable. Examples: heart rate, the heights of adult males, diastolic blood pressure, gender，blood type，treatment effect

  30. Quantitative variable Binary variable variable Multiple categorical variable Qualitative variable Ordinal variable • Variable

  31. Variable quantitative variable: • also known as metric, or numerical • is one that can be measured in the usual sense • convey information regarding amount example：the weights of preschool children, diastolic blood pressure

  32. Variable qualitative variable • also known as categorical or nominal • is one that can not be measured in the usual sense，only can be categorized • convey information regarding attribute

  33. blood types race A, B， AB, O white, black, yellow, brown • Variable • Binary variable: gender, live or death, yes or no. • Multiple categorical variable • Ordinal variable: there is an order in the categories Your opinion on something: unsatisfactory, normal, very satisfactory

  34. Variable

  35. weight (kg) fat or overweight normal thin normal abnormal Numerical variable Ranked variable binary variable • Variable Data transformation

  36. quantitative variable qualitative variable example：WBC（1/m3）count of five persons： 3000 6000 5000 8000 12000 lower normal normal normal higher Binary variable : normal 3 persons; abnormal 2 persons Ordinal variable: lower 1 person normal 3 persons higher 1 person

  37. Parameter and Statistic • Parameter • describe the characteristic of population. • usually presented by Greek letter，such as μ. • Usually unknown

  38. Parameter and Statistic statistic • describe the characteristic of a sample • usually presented by Latin letter，such as s and p.

  39. Certain Impossible Probability • the possibility of occurrence of a random event. • designated as P 0≤P≤1 P=0 impossible event P=1 certain event P≤0.05 small probability event

  40. Probability random event: The event may occur or may not occur in one experiment. Before one experiment, nobody is sure whether the event occurs or not. Throw the dice

  41. Probability Frequency of an event------the number of times the event occurs in a sequence of repetition of the random phenomenon. Probability of an event----if in a long sequence of repetition， the relative frequency of an event approached a fixed number, that number is the probability of the event .

  42. Relative frequency 1.00 0.75 0.50 0.25 0.00 125 0 25 50 75 100 Probability

  43. Probability The relationship between relative frequency and probability →Probability is the limit of frequency P=f=m/n n ∝

  44. Examples of small probability event： • Probability of traffic accident • Serious adverse events happened after injecting hepatitis b vaccine • Winning the lottery

  45. Ⅲ BASIC STEP OF STATISTICAL WORK 4 Analysis of data 3 Sorting of data 2 Collection of data 1 Design

  46. 1 Design

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