1. By Hatim Jaber MD MPH JBCM PhD 27+29 - 11- 2016
Faculty of Medicine Introduction to Community Medicine Course ( ) Introduction to Statistics and Demography By
Hatim Jaber
MD MPH JBCM PhD

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

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

6. Introduction to Biostatistics

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)

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

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.

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.

11. Why we need to study Medical Statistics
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.

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

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, … …

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.

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, …

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.

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.

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.

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.

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)

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.

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

23. -- Enumeration Data 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

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

25. Examples RBC (4.58 106/mcL) Diastolic/systolic blood pressure
Which type of data they belong to?
RBC ( /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)

26. III. The Basic Steps of Statistical Work
1. Design of study
Professional design:
Research aim
Subjects,
Measures, etc.

27. Statistical design: Sampling or allocation method, Sample size,
Randomization,
Data processing, etc.

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) …

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.

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 …)

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 )

34. Types of data
Constant
Variables

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

36. Methods of presentation of data
Numerical presentation
Graphical presentation
Mathematical presentation

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

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

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

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. %
Smoker 15
75% 8
20% 23
38.33
Non smoker 5
25% 32
80% 37
61.67 20
100 40 60

41. Smoking Lung cancer Total positive negative No. % Smoker 15 65.2 8
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. %
Smoker 15
65.2 8
34.8 23
100
Non smoker 5
13.5 32
86.5 37 20
33.3 40
66.7 60

43. Figure (1): Maternal mortality rate of (country), 1960-2000
Line Graph
Year
MMR
1960 50
1970 45
1980 26
1990 15
2000 12
Figure (1): Maternal mortality rate of (country),

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
2 (8%)
4 (20%)
(60+70) / 2 = 65
Total
25(100%)
20(100%)

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
(20%) 65
Figure (2): Distribution of 45 patients at (place) , in (time) by age and sex

46. Frequency curve

47. Histogram
Figure (2): Distribution of 100 cholera patients at (place) , in (time) by age

48. Bar chart
Marital Status

49. Bar chart
Marital Status

50. Pie chart

51. Doughnut chart

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

53. 1- Measures of central tendency (averages)
Summery statistics
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

54. 1- Measures of central tendency (cont.)
Summery statistics
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

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

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

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