An introduction class on biostatistics and its use in medicine & public health

1. INTRODUCTION TO
BIOSTATISTICS

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

5. “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.

6. 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

7. 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 variation 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

8. 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

9. Medical Errors that cause Uncertainties
 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

10. 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

11. Biostatistics is the science
that helps in managing
medical uncertainties

12. 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.

13. 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”.

14. 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-specificity 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

15. 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.

16. 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

17. 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

18. Example: Evaluation of Penicillin (treatment A) vs Penicillin &
Chloramphenicol (treatment B) for treating bacterial
pneumonia in children< 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 ?

19. WHAT DOES STAISTICS COVER ?
Planning
Design
Execution (Data collection)
Data Processing
Data analysis
Presentation
Interpretation
Publication

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

21. TYPES OF DATA
QUALITATIVE DATA
DISCRETE QUANTITATIVE
CONTINOUS QUANTITATIVE

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

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

24. 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

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

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

27. hospital length of stay Number Percent
1 – 3 days 5891 43.3
4 – 7 days 3489 25.6
2 weeks 2449 18.0
3 weeks 813 6.0
1 month 417 3.1
More than 1 month 545 4.0
Total 14604 100.0
Mean = 7.85 SE = 0.10
Table 1 Distribution of blunt injured patients
according to hospital length of stay

28. Scale of measurement
Qualitative variable:
A categorical variable
Nominal (classificatory) scale
- gender, marital status, race
Ordinal (ranking) scale
- severity scale, good/better/best

29. 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)

30. 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.

31. 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: Temperature scale .

32. 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

33. THANK YOU