1. Biostatistics A foundation for analysis in the health science
Yongli YANG Ph.D, Associate Professor
Department of Biostatistics & Epidemiology, college of public health TEL:

2. Statistics in life
GDP in China increased 7.7% in 2013 from the report of State Statistical Bureau.
Life expectancy is year in 6th population census
Weather forecast in Zhengzhou

3. week Theory course content8
introduction 9
Description of quantitative variable 10
Description of qualitative variable . Statistical table and graph
Exercise: statistical description 11
Normal distribution
Sampling error and sampling distribution 12
The principle of hypothesis test
t test 13
One-way analysis of variance
Nonparametric test 14
Exercise: t test and ANOVA
Chi-square test 15
Simple linear correlation analysis
Simple linear regression analysis 16
Exercise: Chi-square test Correlation and regression analysis

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

5. Basic step of statistical work
Be familiar with
The definition: statistics and biostatistics
Understand
The definition: population, sample, probability, quantitative variable, qualitative variable
Master

6. 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.

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

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

9. Smoking group
Non-smoking group

10. 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?

11. 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?

12. 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

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

14. 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

15. 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.

16. 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.

17. 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.

18. 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……

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

20. 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

21. National economic statistics
The tools of statistics are employed in many fields—demography, national economic, psychology, medicine……
Demographics
National economic statistics
Psychological statistics
Biostatistics ……

22. 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.

23. Population and sample
We want to learn the average income of Beijing doctors in 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.

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

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

26. 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.

27. 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.

28. 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.

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

31. 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 1 9 2 3 4 5 6 7 8 10 17 16 15 13 14 12 11
Sample

32. 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

33. Variable Binary Multiple categorical Ordinal Quantitative variable
Qualitative
Multiple categorical
Ordinal
Binary

34. 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

35. 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

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

37. Variable ID age gender Educational level occupation height weight 27
male
graduate
teacher
165
71.5 22
undergraduate
doctor
160 74 25
female
junior high school
worker
158 68 23
senor high school
students
161 69
159 62
elementary
farmer
157 20
cadre 66 24
70.5 29
154 57

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

39. quantitative variable qualitative variable
example：WBC（1/m3）count of five persons： lower normal normal normal higher
Binary variable : normal persons;
abnormal persons
Ordinal variable: lower person
normal persons
higher person

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

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

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

44. 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

45. 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 .

46. Probability Relative frequency 1.00 0.00 0.25 0.50 0.75 25 50 75 10025 50 75
100
125

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

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

49. Ⅲ Basic step of statistical work
4 Analysis
of data
3 Sorting of
data
2 Collection
of data
1 Design

50. 1 Design Professional design Study aim Study subject measures
Statistical design
Sampling method
Allocation method
Calculation of sample size
Data processing
What are you going to do？

51. 2 Collection of data
Source of data
Routinely kept records
Surveys
Experiments
External sources
Principle：in time, accurate, complete

52. Checking: outlier, missing value,
3 Sorting of data
Checking: outlier, missing value,
Coding: Blood type A(1), B(2), AB(3), O(4);
gender male(1), female(2)
Grouping:
DBP
SBP
Computing: weight
height
hypotension
normal
hypertension
Body mass index

53. 4 Analysis of data Statistical analysis is divided into two parts: descriptive statistics and inferential statistics

54. To teach the student to organize and summarize data
Statistical
description
inference
indicator
Table and chart
Parameter
estimation
Hypothesis
testing
analysis
To teach the student how to reach decisions about a large body of data by examining only a small part of the data

55. Review questions and exercises
Define:
Quantitative variable
Qualitative variable
Population
Sample
probability

56. Review questions and exercises
Explain the type of the following variables:
Admitting diagnosis in a mental health clinic
Weights of babies born in hospital during a year
Gender of babies born in hospital during a year
Under-arm temperature of patients with fever

57. Thank you