biostats

1. Faculty of Medicine
Introduction to Community Medicine Course
(31505201)
Introduction to
Statistics and Demography
By
Hatim Jaber
MD MPH JBCM PhD
27+29 - 11- 2016
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2. World AIDS Day 2016:
end AIDS by 2030
• People living with HIV 36.7
million
• People on antiretroviral
therapy 18.2 million
• Mother-to-child
transmission 7 out of 10
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3. 3

4. 4

5. Presentation outline
Time
Introduction and Definitions of Statistics and
biostatistics
12:00 to 12:10
Role of Statistics in Clinical Medicine 12:10 to 12:20
Basic concepts 12:20 to 12:30
Methods of presentation of data 12:30 to 12:40
12:40 to 12:50
5

6. Introduction to
Biostatistics
6

7. Definition of Statistics
• Different authors have defined statistics differently. The best
definition of statistics is given by Croxton and Cowden according to
whom statistics may be defined as the science, which
deals with collection, presentation, analysis
and interpretation of numerical data.
• 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 )
• Branch of mathematics that deals with the collection, organization,
and analysis of numerical data and with such problems as
experiment design and decision making. —(Microsoft
Encarta Premium 2009)
7

8. Definition of Biostatistics= Medical
statistics
• Biostatistics may be defined as application of
statistical methods to medical, biological
and public health related problems.
• It is the scientific treatment given to the medical
data derived from group of individuals or patients
Collection of data.
Presentation of the collected data.
Analysis and interpretation of the results.
Making decisions on the basis of such analysis 8

9. Role of Statistics in Clinical Medicine
The main theory of statistics lies in the term variability.
There is No two individuals are same. For example, blood
pressure of person may vary from time to time as well as from
person to person.
We can also have instrumental variability as well as
observers variability.
Methods of statistical inference provide largely objective means
for drawing conclusions from the data about the issue under
study. Medical science is full of uncertainties and statistics deals
with uncertainties. Statistical methods try to quantify the
uncertainties present in medical science.
It helps the researcher to arrive at a scientific judgment about
a hypothesis. It has been argued that decision making is an
integral part of a physician’s work.
Frequently, decision making is probability based. 9

10. Role of Statistics in
Public Health and Community Medicine
Statistics finds an extensive use in Public Health and Community Medicine.
Statistical methods are foundations for public health administrators to
understand what is happening to the population under their care at community
level as well as
individual level. If reliable information regarding the disease is available, the
public health administrator is in a position to:
●● Assess community needs
●● Understand socio-economic determinants of health
●● Plan experiment in health research
●● Analyze their results
●● Study diagnosis and prognosis of the disease for taking
effective action
●● Scientifically test the efficacy of new medicines and
methods of treatment.
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11. Why we need to study Medical Statistics?
Three reasons:
(1) Basic requirement of medical research.
(2) Update your medical knowledge.
(3) Data management and treatment.
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12. Role of statisticians
 To guide the design of an experiment or survey prior to
data collection
 To analyze data using proper statistical procedures and
techniques
 To present and interpret the results to researchers and
other decision makers
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13. I. Basic concepts
• Homogeneity: All individuals have similar values or
belong to same category.
Example: all individuals are Chinese, women, middle age (30~40
years old), work in a computer factory ---- homogeneity in nationality,
gender, age and occupation.
• Variation: the differences in feature, voice…
• Throw a coin: The mark face may be up or down ---- variation!
• Treat the patients suffering from pneumonia with same antibiotics:
A part of them recovered and others didn’t ---- variation!
• If there is no variation, there is no need for statistics.
• Many examples of variation in medical field: height, weight, pulse,
blood pressure, … …
13

14. 2. Population and Sample
• Population: The whole collection of individuals that
one intends to study.
• Sample: A representative part of the population.
• Randomization: An important way to make the
sample representative.
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15. limited population and limitless population
• All the cases with hepatitis B collected in a hospital
in Amman . (limited)
• All the deaths found from the permanent residents
in a city. (limited)
• All the rats for testing the toxicity of a medicine.
(limitless)
• All the patients for testing the effect of a medicine.
(limitless) hypertensive, diabetic, …
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16. Random
By chance!
• 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.
Example: weather, traffic accident, …
There must be some regulation in a large number
of experiments.
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17. 3. Probability
• Measure the possibility of occurrence of a random
event.
• A : random event
• P(A) : Probability of the random event A
P(A)=1, if an event always occurs.
P(A)=0, if an event never occurs.
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18. Estimation of Probability----Frequency
• Number of observations: n (large enough)
Number of occurrences of random event A: m
f(A)  m/n
(Frequency or Relative frequency)
Example: Throw a coin event:
n=100, m (Times of the mark face occurred)=46
m/n=46%, this is the frequency; P(A)=1/2=50%,
this is the Probability.
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19. 4. Parameter and Statistic
• Parameter : A measure of population or
A measure of the distribution of population.
Parameter is usually presented by Greek letter.
such as μ,π,σ.
-- Parameters are unknown usually
To know the parameter of a population, we need a sample
• Statistic: A measure of sample or A measure of the distribution of sample.
Statistic is usually presented by Latin letter
such as s , p, t.
19

20. 5. Sampling Error
error :The difference between observed value and
true value.
Three kinds of error:
(1) Systematic error (fixed)
(2) Measurement error (random) (Observational error)
(3) Sampling error (random)
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21. Sampling error
• The statistics of different samples from same
population: different each other!
• The statistics: different from the parameter!
The sampling error exists in any sampling research.
It can not be avoided but may be estimated.
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22. II. Types of data
1. Numerical Data ( Quantitative Data )
• The variable describe the characteristic of individuals
quantitatively
-- Numerical Data
• The data of numerical variable
-- Quantitative Data
22

23. 2. Categorical Data ( Enumeration Data )
• The variable describe the category of individuals according to a
characteristic of individuals
-- Categorical Data
• The number of individuals in each category
-- Enumeration Data
23

24. Special case of categorical data :
Ordinal Data ( rank data )
• There exists order among all possible categories. ( level of
measurement)
-- Ordinal Data
• The data of ordinal variable, which represent the order of
individuals only
-- Rank data
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25. Examples
Which type of data they belong to?
• RBC (4.58 106/mcL)
• Diastolic/systolic blood pressure
(8/12 kPa) or ( 80/100 mmHg)
• Percentage of individuals with blood type A (20%)
(A, B, AB, O)
• Protein in urine (++) (－, ±, +, ++, +++)
• Incidence rate of breast cancer ( 35/100,000)
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26. III. The Basic Steps of Statistical Work
1. Design of study
• Professional design:
Research aim
Subjects,
Measures, etc.
26

27. • Statistical design:
Sampling or allocation method,
Sample size,
Randomization,
Data processing, etc.
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28. 2. Collection of data
• Source of data
Government report system such as: cholera,
plague (black death) …
Registration system such as: birth/death
certificate …
Routine records such as: patient case report …
Ad hoc survey such as: influenza A (H1N1) …
28

29. • Data collection – Accuracy, complete,
in time
Protocol: Place, subjects, timing; training; pilot;
questionnaire; instruments; sampling method and
sample size; budget…
Procedure: observation, interview, filling
form, letter, telephone, web.
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30. 3. Data Sorting
• Checking
Hand, computer software
• Amend
• Missing data?
• Grouping
According to categorical variables (sex, occupation, disease…)
According to numerical variables (age, income, blood pressure …)
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31. 31
4. Data Analysis
• Descriptive statistics (show the sample)
mean, incidence rate …
-- Table and plot
• Inferential statistics (towards the population)
-- Estimation
-- Hypothesis testing (comparison)

32. About Teaching and Learning
• Aim:
Training statistical thinking
Skill of dealing with medical data.
• Emphasize:
Essential concepts and statistical thinking
-- lectures and practice session
Skill of computer and statistical software
-- practice session ( Excel and SPSS )
32

33. Sources of
data
Records Surveys Experiments
Comprehensive Sample
33

34. Types of data
Constant
Variables
34

35. Quantitative
continuous
Types of variables
Quantitative variables Qualitative variables
Quantitative
descrete
Qualitative
nominal
Qualitative
ordinal
35

36. Numerical presentation
Graphical presentation
Mathematical presentation
Methods of presentation of data
36

37. 1- Numerical presentation
Tabular presentation (simple – complex)
Name of variable
(Units of variable)
Frequency %
-
- Categories
-
Total
Simple frequency distribution Table (S.F.D.T.)
Title
37

38. Table (I): Distribution of 50 patients at the surgical
department of AAAAA hospital in May 2008 according
to their ABO blood groups
Blood group Frequency %
A
B
AB
O
12
18
5
15
24
36
10
30
Total 50 100
38

39. Table (II): Distribution of 50 patients at the surgical
department of AAAAA hospital in May 2008 according to
their age
Age
(years)
Frequency %
20-<30
30-
40-
50+
12
18
5
15
24
36
10
30
Total 50 100
39

40. Complex frequency distribution Table
Table (III): Distribution of 20 lung cancer patients at the chest department
of AAAAA hospital and 40 controls in May 2008 according to smoking
Smoking
Lung cancer
Total
Cases Control
No. % No. % No. %
Smoker 15 75% 8 20% 23 38.33
Non
smoker 5 25% 32 80% 37 61.67
Total 20 100 40 100 60 100
40

41. Complex frequency distribution Table
Table (IV): Distribution of 60 patients at the chest department of
AAAAA hospital in May 2008 according to smoking & lung cancer
Smoking
Lung cancer
Total
positive negative
No. % No. % No. %
Smoker 15 65.2 8 34.8 23 100
Non
smoker 5 13.5 32 86.5 37 100
Total 20 33.3 40 66.7 60 100
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42. 42

43. Line Graph
0
10
20
30
40
50
60
1960 1970 1980 1990 2000
Year
MMR/1000 Year MMR
1960 50
1970 45
1980 26
1990 15
2000 12
Figure (1): Maternal mortality rate of (country),
1960-2000
43

44. Frequency polygon
Age
(years)
Sex Mid-point of interval
Males Females
20 - 3 (12%) 2 (10%) (20+30) / 2 = 25
30 - 9 (36%) 6 (30%) (30+40) / 2 = 35
40- 7 (8%) 5 (25%) (40+50) / 2 = 45
50 - 4 (16%) 3 (15%) (50+60) / 2 = 55
60 - 70 2 (8%) 4 (20%) (60+70) / 2 = 65
Total 25(100%) 20(100%)
44

45. Frequency polygon
Age
Sex
M-P
M F
20- (12%) (10%) 25
30- (36%) (30%) 35
40- (8%) (25%) 45
50- (16%) (15%) 55
60-70 (8%) (20%) 65
0
5
10
15
20
25
30
35
40
25 35 45 55 65
Age
%
Males Females
Figure (2): Distribution of 45 patients at (place) , in (time)
by age and sex 45

46. 0
1
2
3
4
5
6
7
8
9
20- 30- 40- 50- 60-69
Age in years
Frequency
Female
Male
Frequency curve
46

47. Histogram
Distribution of a group of cholera patients by age
Age (years) Frequency %
25-
30-
40-
45-
60-65
3
5
7
4
2
14.3
23.8
33.3
19.0
9.5
Total 21 100
0
5
10
15
20
25
30
35
0
2
5
3
0
4
0
4
5
6
0
6
5
Age (years)
%
Figure (2): Distribution of 100 cholera patients at (place) , in
(time) by age 47

48. Bar chart
0
5
10
15
20
25
30
35
40
45
%
Single Married Divorced Widowed
Marital status
Marital Status
48

49. Bar chart
0
10
20
30
40
50
%
Single Married Divorced Widowed
Marital status
Male
Female
Marital Status
49

50. Pie chart
Deletion
3%
Inversion
18%
Translocation
79%
50

51. Doughnut chart
Hospital A
Hospital B
DM
IHD
Renal
51

52. 3-Mathematical presentation
Summery statistics
Measures of location
1- Measures of central tendency
2- Measures of non central locations
(Quartiles, Percentiles )
Measures of dispersion
52

53. 1- Measures of central tendency (averages)
Midrange
Smallest observation + Largest observation
2
Mode
the value which occurs with the greatest
frequency i.e. the most common value
Summery statistics
53

54. 1- Measures of central tendency (cont.)
Median
the observation which lies in the middle of the
ordered observation.
Arithmetic mean (mean)
Sum of all observations
Number of observations
Summery statistics
54

55. Measures of dispersion
Range
Variance
Standard déviation
Semi-interquartile range
Coefficient of variation
“Standard error”
55

56. Standard déviation SD
7 7
7 7 7
7
7 8
7 7 7
6 3 2
7 8 13
9
Mean = 7
SD=0
Mean = 7
SD=0.63
Mean = 7
SD=4.04
56

57. Standard error of mean SE
SE (Mean) =
S
n
A measure of variability among means of samples
selected from certain population
57