Tuj

1. Importance of Biostatistics
in Biomedical Research
Dr. R.M. Pandey
Prof & Head
Department of Biostatistics
A.I.I.M.S., New Delhi
rmpandey@yahoo.com

2. Clinical Research
Definition
“Clinical research is a component of medical and
health research intended to produce knowledge
valuable for understanding disease, preventing and
treating illness, and promoting health”
US National Clinical Research SummitProject, 1998
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3. Issues &Questions in BiomedicalResearch
ISSUE(S) QUESTION(S)
Normality Isa person sick or well?
Abnormality What abnormalities are associated with having adisease?
Diagnosis How accurate are diagnostic tests or strategies used to finda
disease?
Frequency How often does a diseaseoccur?
Risk What factors are associated with an increased likelihood of disease?
Prognosis What are the consequences of having adisease?
Prevention Does intervention on people without diseasework?
Does early detection and treatment improve the course ofdisease?.
Cause What condition results in adisease?
What are the pathogenetic mechanisms ofdisease?

4. Evidence Based Alliance
Clinical
Expertise
Research
Evidence
Patient
Preferences
Clinically relevant, patient
centred, research about:
• Diagnosis
• Prognosis
• Interventions
The ability to use our
clinical skills and past
experience to rapidly
identify each patient's
• unique health state and
diagnosis,
• their individual risks,
• the benefits of potential
interventions, and their
• personal valuesand
expectations.
The unique
preferences,
concerns and
expectations of each
patient
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EBM
EBM. Sackett etal 1996

5. Evidence Based Alliance?
Expertise
Research
Evidence
Patient
Preferences
EBM
Our clinical experience
alone may not improve
our patient’s lives
Clinical
Are ourpatients
always able to judge
what is best for
them?
Alot of research is
not aimed at
improving the
lives of patients!
Who asksthem?
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6. 6
The Research Question
• All studies should start with a research question
that addresses what the investigator would like to
know
• Goal is to find an important research question
that can be developed into a feasible and valid
study plan

7. Research Questions
Primary/ Secondary
1. What is the prevalence of a condition?.
2. What is the average (Mean) of a characteristics?
3. What is the strength of correlation between two quantitative parameters?
4. What is the agreement between methods?
5. What are the diagnostic characteristics of a candidate test
(categorical/quantitative) with reference to a “Gold Standard”?.
6. What is incidence of an outcome?.
7. What are the predictors of an outcome?
8. What are the risk factors associated with an outcome?
9. Evaluation of a candidate intervention against a control (standard of car7e)?.

8. Population vs. Sample
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9. 9
Population Sample
Parameter Statistic
Sampling variation

10. Drawing
Conclusions
Designing &
Implementing
TRUTH IN THE
UNIVERSE
Research
Question
TRUTH IN THE
STUDY
Study
Plan
FINDINGS IN
THE STUDY
Actual
Study
Infer Infer
Design Implement
Process of Research Project
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11. 11

12. Overview of Study Designs in Clinical
Research
12
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13. Variables
Outcome
Exposures
Confounder(s)
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14. Total variability = True variability + Error
Sources of Error in Measurements
• Observer
• Subject
• Instrument
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15. Random
Systematic
Systolic
Blood
Pressure
Time 24 hr
Types of Variability
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16. Two questions asked at the end
• Validity the study results?
• Reliability of the study results?

17. Illustration of the Difference Between
Precision and Accuracy
Hulley & Cummings, Designing Clinical Research, 1
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988.

18. Good Precision
PoorAccuracy
Poor Precision
Good Accuracy
Good Precision
Good Accuracy
Poor Precision
PoorAccuracy
Illustration of the Difference Between
Precision and Accuracy
Hulley & Cummings, Designing Clinical Research, 1981
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8.

19. 19
Validity and Reliability of both
: Measurements, and Studyresult
• Validity :
– Asking - Are we measuring/estimating what we think we
are meaning?
• Reliability :
– Asking - How reproducible is the value/study result ?
STATISTICALMETHODS, IF USED PROPERLY,
PROVIDES VALID AND RELIABLE RESULTS

20. Statistical
Methods
Research
Methods in Health
Sciences
Computing Skills
BIOSTATISTICS

21. 21
Statistical Methods
I. Design Stage
Sample Size
Sampling/Randomization
Data Management Plan
Data Analysis Plan
II. Analysis Stage
Descriptive Analysis
Inferential analysis
III.Interpretation &Publication

22. Statistical Methods (Analysis)
I. Descriptive Methods :
Tables
Diagrams
Summary : Univariable,
Bivariable strength
Multivariable
II. Inferential Methods :
 Estimation
 Point Estimation
Mean /proportion
etc.
 Interval Estimation
i.e. Confidence interval of
point estimate
 Hypotheses Testing
 Comparison between
the treatments
 Association
 Etc.
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23. Five Important Terms
Outcome
Exposures
Bias
Confounder(s)
Chance factor
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24. Variables
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Functional
Relationsh
ip
Measurement/Analysis
Categorical Quantitative Time to an event
Outcome
√ √ √
Exposure
√ √ x
Other factors
• Confounder/s
• Effect modifier/s
• Interaction
√
√
√
√
√
√
x
x
x

25. 25
Descriptive Analysis
• One variable (uni-variable)
– Qualitative
– Quantitative
– Time to an event
• Two variables (Bivariable)
– Qualitative vs Qualitative
– Qualitative vs Quantitative
– Quantitative vs Quantitative
• More than two variables (Multivariable)

26. 26
Measures of Associations
• Measures of association are mathematical
comparisons
• Mathematical comparisons can be done in
– absolute terms, or
– relative terms

27. 27
Absolute and RelativeMeasures
Risk Difference (Absolute Measures)
Relative Risk (Risk Ratio)
Odds Ratio
Rate Ratio
Each one gives a different perspective
Each one appeals to different constituencies

28. Observed Exposure-Outcome
Association :Possibilities
The observed statistical association between a certainoutcome
and the hypothesized exposure could be the result of
systematic errors in collection of data (sampling, disease and
exposure ascertainment) or its interpretation
- role of bias
Or it could be due to the effect of additional variables that
might be responsible for the observedassociation
- role of confounding variable(s)
Or it could be just a matter of chance
Or it could be a realassociation

29. 29
Confounding
• Confounding is a distortion in a measure
of effect that may arise because we fail to
control for other variables that are
previously known risk factors for the health
outcome being studied.

30. 30
Confounding
• Confounding can lead to the observation of
apparent differences between the study
groups when they do not truly exist, or
conversely, the observation of of no
difference when they do exist.

31. 31
Confounding variable
• It is an independent risk factor(cause) of
outcome)
• It is unevenly distributed among exposed
and unexposed
• It is not on the causal pathway between
exposure and outcome

32. 32
THE DIFFERENCE BETWEEN
BIAS AND CONFOUNDING
 Bias creates an association that is not true,
 Confounding describes an association that
is true, but potentially misleading.

33. 33
EXAMPLES OF RANDOM ERROR, BIAS,
MISCLASSIFICATION AND CONFOUNDING IN THE
SAME STUDY:
Cohort study: babies of women who bottle
feed and women who breast feed are
compared,
it is found that the incidence of
gastroenteritis, as recorded in medical
records, is lower in the babies who are
breast-fed.

34. 34
EXAMPLE OF RANDOM ERROR
By chance, there are more episodes of
gastroenteritis in the bottle-fed group in the study
sample, producing a type 1error. (When in truth
breast feeding is not protective against
gastroenteritis).
Or, also by chance, no difference in risk was found,
producing a type 2error (When in truth breast
feeding is protective against gastroenteritis).

35. 35
EXAMPLE OF RANDOM
MISCLASSIFICATION
Lack of good information on feeding history
results in some breast-feeding mothers being
randomly classified as bottle-feeding, and vice-
versa.
If this happens, the study finding
underestimates the true RR,whichever feeding
modality is associated with higher disease
incidence, producing a type 2 error.

36. 36
EXAMPLE OF BIAS
The medical records of bottle-fed babies
only are less complete (perhaps bottle fed
babies go to the doctor less) than those of
breast fed babies, and thus record fewer
episodes of gastro-enteritis in them only.
This is called bias because the observation
itself is in error.

37. 37
EXAMPLE OF CONFOUNDING
The mothers of breast-fed babies are of higher social
class, and the babies thus have better hygiene, less
crowding and perhaps other factors that protect against
gastroenteritis.
Less crowding and better hygiene are truly protective
against gastroenteritis, but we mistakenly attribute
their effects to breast feeding. This is called
confounding because the observation is correct, but its
explanation is wrong.

38. 38
Bivariate Association Between
Smoking Status and Risk of Death
Bivariate Non-
smokers
Former
smokers
Recent
quitters
Persistent
smokers
Relative risk of
death
1.0 (ref.) 1.08
(0.92-1.26)
0.56
(0.40-0.77)
0.74
(0.59-0.94)
Hasdai, D., et al. “Effect of smoking status on the long-term outcome after
successful percutaneous coronary revascularization.” N. Engl. J. Med. 1997;
336:755-61.
Various other studies have also found similar results. It is known as Smoker’s
paradox

39. 39
Association Between Demographicand
Clinical Factors and Smoking Status
Non-
smokers
Former
smokers
Recent
quitters
Persistent
smokers
Age, year + SD 67 + 11 65 + 10 56 + 10 55 + 11
Duration of angina, month + SD 41 + 66 51 + 72 21 + 46 29 + 55
Diabetes, % 21% 18% 8% 10%
Hypertension, % 54% 48% 38% 39%
Extent of coronary artery disease, %
One vessel 50% 51% 57% 55%
Two vessels 36% 36% 34% 36%
Three vessels 14% 13% 10% 9%
Hasdai, D., et al. “Effect of smoking status on the long-term outcome after
successful
percutaneous coronary revascularization.” N. Engl. J. Med. 1997; 336:755-61.

40. 40
Comparison of Bivariate and
Multivariable Association Between
Smoking Status and Risk of Death
Non-
smokers
Former smokers Recent quitters Persistent
smokers
Relative risk of death
Bivariate 1.0 (ref.) 1.08
(.92-1.26)
0.56
(.40-.77)
0.74
(.59-.94)
Relative risk of death
Multivariable 1.0 (ref.) 1.34
(1.14-1.57)
1.21
(.87-1.70)
1.76
(1.37-2.26)
Hasdai, D., et al. “Effect of smoking status on the long-term outcome after
successful
percutaneous coronary revascularization.” N. Engl. J. Med. 1997; 336:755-61.

41. Intervening Variable
• An intervening variable is on the causal pathway to the
outcome
Camargo, C.A, Stampfer,M.J. ,et al. “Moderate alcohol consumption and risk of angina pectoris in myocardial infarction
in U.S. male physians” Ann. Intern. Med. 1997;126:372-5.
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42. Study Designs
Cross-Sectional
Case-Control
Cohort
Clinical Trial
Objective(s)
Secondary
Primary
Burden
-Prev, mean
Hypothesis generation
Prev of RF’s & Measures of Assoc.
Association Hypothesis generation
Prev of RF’s in Case-Control & Measures of Assoc.
Cause-Effect Hypothesis generation
Incidence of outcome(s), Measures of Assoc.
Cause-Effect Hypothesis generation
Incidence of outcome(s), Measures of Assoc.
What to compute?
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43. • Statistical Analysis is a computing problem: Avoid Such a thinking
• Prevention is Cost-Effective: Also true for Biostatistics
• Preventive measures:
1. Must have knowledge of Principles of Research Methods & Biostatistics.
2. Develop computing skills
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44. 44
you
Thank You