INTRODUCTION TO BIOSTATISTICS

1
INTRODUCTION TO BIOSTATISTICS

• DR.S.Shaffi Ahamed
• Asst. Professor
• Dept. of Family and Comm. Medicine
• KKUH

2
This session covers

• Background and need to know Biostatistics
• Origin and development of Biostatistics
• Definition of Statistics and Biostatistics
• Types of data
• Graphical representation of a data
• Frequency distribution of a data

3

• Statistics is the science which deals with
  collection, classification and tabulation of
  numerical facts as the basis for explanation,
  description and comparison of phenomenon.
• ------ Lovitt

4
BIOSTATISICS

• (1) Statistics arising out of biological
  sciences, particularly from the fields of
  Medicine and public health.
• (2) The methods used in dealing with statistics
  in the fields of medicine, biology and public
  health for planning, conducting and analyzing
  data which arise in investigations of these
  branches.

5
Origin and development of statistics in Medical
Research

• In 1929 a huge paper on application of statistics
  was published in Physiology Journal by Dunn.
• In 1937, 15 articles on statistical methods by
  Austin Bradford Hill, were published in book
  form.
• In 1948, a RCT of Streptomycin for pulmonary tb.,
  was published in which Bradford Hill has a key
  influence.
• Then the growth of Statistics in Medicine from
  1952 was a 8-fold increase by 1982.

6
C.R. Rao
Ronald Fisher
Karl Pearson
Douglas Altman
Gauss -
7

• Basis

8
Sources of Medical Uncertainties

1. Intrinsic due to biological, environmental and
   sampling factors
2. Natural variation among methods, observers,
   instruments etc.
3. Errors in measurement or assessment or errors in
   knowledge
4. Incomplete knowledge

9
Intrinsic variation as a source of medical
uncertainties

• Biological due to age, gender, heredity, parity,
  height, weight, etc. Also due to variation in
  anatomical, physiological and biochemical
  parameters
• Environmental due to nutrition, smoking,
  pollution, facilities of water and sanitation,
  road traffic, legislation, stress and strains
  etc.,
• Sampling fluctuations because the entire world
  cannot be studied and at least future cases can
  never be included
• Chance variation due to unknown or complex to
  comprehend factors

10
Natural variation despite best care as a source
of uncertainties

• In assessment of any medical parameter
• Due to partial compliance by the patients
• Due to incomplete information in conditions such
  as the patient in coma

11
Medical Errors that cause Uncertainties

• Carelessness of the providers such as physicians,
  surgeons, nursing staff, radiographers and
  pharmacists.
• Errors in methods such as in using incorrect
  quantity or quality of chemicals and reagents,
  misinterpretation of ECG, using inappropriate
  diagnostic tools, misrecording of information
  etc.
• Instrument error due to use of non-standardized
  or faulty instrument and improper use of a right
  instrument.
• Not collecting full information
• Inconsistent response by the patients or other
  subjects under evaluation

12
Incomplete knowledge as a source of Uncertainties

• Diagnostic, therapeutic and prognostic
  uncertainties due to lack of knowledge
• Predictive uncertainties such as in survival
  duration of a patient of cancer
• Other uncertainties such as how to measure
  positive health

13

• Biostatistics is the science that helps in
  managing medical uncertainties

14
Reasons to know about biostatistics

• Medicine is becoming increasingly quantitative.
• The planning, conduct and interpretation of much
  of medical research are becoming increasingly
  reliant on the statistical methodology.
• Statistics pervades the medical literature.

15
CLINICAL MEDICINE

• Documentation of medical history of diseases.
• Planning and conduct of clinical studies.
• Evaluating the merits of different procedures.
• In providing methods for definition of normal
  and abnormal.

16
Role of Biostatistics in patient care

• In increasing awareness regarding diagnostic,
  therapeutic and prognostic uncertainties and
  providing rules of probability to delineate those
  uncertainties
• In providing methods to integrate chances with
  value judgments that could be most beneficial to
  patient
• In providing methods such as sensitivity-specifici
  ty and predictivities that help choose valid
  tests for patient assessment
• In providing tools such as scoring system and
  expert system that can help reduce epistemic
  uncertainties

17
PREVENTIVE MEDICINE

• To provide the magnitude of any health problem
  in the community.
• To find out the basic factors underlying the
  ill-health.
• To evaluate the health programs which was
  introduced in the community (success/failure).
• To introduce and promote health legislation.

18
Role of Biostatics in Health Planning and
Evaluation

• In carrying out a valid and reliable health
  situation analysis, including in proper
  summarization and interpretation of data.
• In proper evaluation of the achievements and
  failures of a health programme

19
Role of Biostatistics in Medical Research

• In developing a research design that can minimize
  the impact of uncertainties
• In assessing reliability and validity of tools
  and instruments to collect the infromation
• In proper analysis of data

20
Example Evaluation of Penicillin (treatment A)
vs Penicillin Chloramphenicol (treatment B) for
treating bacterial pneumonia in childrenlt 2 yrs.

• What is the sample size needed to demonstrate the
  significance of one group against other ?
• Is treatment A is better than treatment B or
  vice versa ?
• If so, how much better ?
• What is the normal variation in clinical
  measurement ? (mild, moderate severe) ?
• How reliable and valid is the measurement ?
  (clinical radiological) ?
• What is the magnitude and effect of laboratory
  and technical
• error ?
• How does one interpret abnormal values ?

21
WHAT DOES STAISTICS COVER ?

• Planning
• Design
• Execution (Data
  collection)
• Data Processing
• Data analysis
• Presentation
• Interpretation
• Publication

22
BASIC CONCEPTS
Data Set of values of one or more variables
recorded on one or more observational units
Sources of data 1. Routinely kept
records 2. Surveys (census) 3.
Experiments 4. External source
Categories of data 1. Primary data
observation, questionnaire, record form,
interviews, survey, 2. Secondary data census,
medical record,registry
23
TYPES OF DATA

• QUALITATIVE DATA
• DISCRETE QUANTITATIVE
• CONTINOUS QUANTITATIVE

24
QUALITATIVE

• Nominal
• Example Sex ( M, F)
• Exam result (P, F)
• Blood Group (A,B, O or AB)
• Color of Eyes (blue, green,
• brown,
  black)

25

• ORDINAL
• Example
• Response to treatment
• (poor, fair, good)
• Severity of disease
• (mild, moderate, severe)
• Income status (low, middle,
• high)

26

• QUANTITATIVE (DISCRETE)
• Example The no. of family members
• The no. of heart beats
• The no. of admissions in a day
• QUANTITATIVE (CONTINOUS)
• Example Height, Weight, Age, BP, Serum
• Cholesterol and BMI

27
Discrete data -- Gaps between possible values
Number of Children
Continuous data -- Theoretically, no gaps between
possible values
Hb
28

• CONTINUOUS DATA
• QUALITATIVE DATA
• wt. (in Kg.) under wt, normal over wt.
• Ht. (in cm.) short, medium tall

29
(No Transcript)
30
Scale of measurement
Qualitative variable A categorical
variable Nominal (classificatory) scale  -
gender, marital status, race Ordinal (ranking)
scale  - severity scale, good/better/best
31
Scale of measurement
Quantitative variable A numerical variable
discrete continuous Interval scale Data is
placed in meaningful intervals and order. The
unit of measurement are arbitrary. -
Temperature (37º C -- 36º C 38º C-- 37º C are
equal) and No implication of ratio (30º C
is not twice as hot as 15º C)
32

• Ratio scale
• Data is presented in frequency distribution in
  logical order. A meaningful ratio exists.
• - Age, weight, height, pulse rate
• - pulse rate of 120 is twice as fast as 60
• - person with weight of 80kg is twice as heavy
  as the one with weight of 40 kg.

33
Scales of Measure

• Nominal qualitative classification of equal
  value gender, race, color, city
• Ordinal - qualitative classification which can
  be rank ordered socioeconomic status of
  families
• Interval - Numerical or quantitative data can
  be rank ordered and sizes compared temperature
  
• Ratio - Quantitative interval data along with
  ratio time, age.

34
CLINIMETRICS

• A science called clinimetrics in which qualities
  are converted to meaningful quantities by using
  the scoring system.
• Examples (1) Apgar score based on appearance,
  pulse, grimace, activity and respiration is used
  for neonatal prognosis.
• (2) Smoking Index no. of cigarettes, duration,
  filter or not, whether pipe, cigar etc.,
• (3) APACHE( Acute Physiology and Chronic Health
  Evaluation) score to quantify the severity of
  condition of a patient

35
(No Transcript)
36
(No Transcript)
37
(No Transcript)
38
INVESTIGATION
39
Frequency Distributions

• data distribution pattern of variability.
• the center of a distribution
• the ranges
• the shapes
• simple frequency distributions
• grouped frequency distributions
• midpoint

40
Tabulate the hemoglobin values of 30 adult male
patients listed below
Patient No Hb (g/dl) Patient No Hb (g/dl) Patient No Hb (g/dl)
1 12.0 11 11.2 21 14.9
2 11.9 12 13.6 22 12.2
3 11.5 13 10.8 23 12.2
4 14.2 14 12.3 24 11.4
5 12.3 15 12.3 25 10.7
6 13.0 16 15.7 26 12.5
7 10.5 17 12.6 27 11.8
8 12.8 18 9.1 28 15.1
9 13.2 19 12.9 29 13.4
10 11.2 20 14.6 30 13.1
41
Steps for making a table

• Step1 Find Minimum (9.1) Maximum (15.7)
• Step2 Calculate difference 15.7 9.1 6.6
  
• Step3 Decide the number and width of
• the classes (7 c.l) 9.0 -9.9,
  10.0-10.9,----
• Step4 Prepare dummy table
• Hb (g/dl), Tally mark, No. patients

42
 
DUMMY TABLE
Tall Marks TABLE
   
43
Table Frequency distribution of 30 adult male
patients by Hb
44
Table Frequency distribution of adult patients
by Hb and gender
45
Elements of a Table
Ideal table should have Number
Title Column headings
Foot-notes Number Table number
for identification in a report Title,place
- Describe the body of the table,
variables, Time period (What, how
classified, where and when) Column -
Variable name, No. , Percentages (),
etc., Heading Foot-note(s) - to describe some
column/row headings, special cells,
source, etc.,
46
Table II. Distribution of 120 (Madras)
Corporation divisions according to annual death
rate based on registered deaths in 1975 and 1976
Figures in parentheses indicate percentages
47
DIAGRAMS/GRAPHS

• Discrete data
• --- Bar charts (one or two groups)
• Continuous data
• --- Histogram
• --- Frequency polygon (curve)
• --- Stem-and leaf plot
• --- Box-and-whisker plot

48
Example data
68 63 42 27 30 36 28 32 79 27 22 28 24 25 44 65
43 25 74 51 36 42 28 31 28 25 45 12 57 51 12 3
2 49 38 42 27 31 50 38 21 16 24 64 47 23 22 43
27 49 28 23 19 11 52 46 31 30 43 49 12
49
Histogram
Figure 1 Histogram of ages of 60 subjects
50
Polygon
51
Example data
68 63 42 27 30 36 28 32 79 27 22 28 24 25 44 65
43 25 74 51 36 42 28 31 28 25 45 12 57 51 12 3
2 49 38 42 27 31 50 38 21 16 24 64 47 23 22 43
27 49 28 23 19 11 52 46 31 30 43 49 12
52
Stem and leaf plot
Stem-and-leaf of Age N 60 Leaf Unit
1.0 6 1 122269 19 2
1223344555777788888 (11) 3 00111226688 13
4 2223334567999 5 5 01127 4 6
3458 2 7 49
53
Box plot
54
Descriptive statistics report Boxplot

• - minimum score
• maximum score
• lower quartile
• upper quartile
• median
• - mean

• the skew of the distribution positive
  skew mean gt median high-score whisker is
  longer negative skew mean lt median
  low-score whisker is longer

55
Pie Chart

• Circular diagram total -100
• Divided into segments each representing a
  category
• Decide adjacent category
• The amount for each category is proportional to
  slice of the pie

The prevalence of different degree of
Hypertension in the population
56
Bar Graphs
Heights of the bar indicates frequency Frequency
in the Y axis and categories of variable in the X
axis The bars should be of equal width and no
touching the other bars
The distribution of risk factor among cases with
Cardio vascular Diseases
57
HIV cases enrolment in USA by gender
Bar chart
58
HIV cases Enrollment in USA by gender
Stocked bar chart
59
Graphic Presentation of Data
the frequency polygon (quantitative data)
the histogram (quantitative data)
the bar graph (qualitative data)
60
(No Transcript)
61
General rules for designing graphs

• A graph should have a self-explanatory legend
• A graph should help reader to understand data
• Axis labeled, units of measurement indicated
• Scales important. Start with zero (otherwise //
  break)
• Avoid graphs with three-dimensional impression,
  it may be misleading (reader visualize less easily

62

• Any Questions

63
Origin and development of statistics in Medical
Research

• In 1929 a huge paper on application of statistics
  was published in Physiology Journal by Dunn.
• In 1937, 15 articles on statistical methods by
  Austin Bradford Hill, were published in book
  form.
• In 1948, a RCT of Streptomycin for pulmonary tb.,
  was published in which Bradford Hill has a key
  influence.
• Then the growth of Statistics in Medicine from
  1952 was a 8-fold increase by 1982.