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

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 Summit Project, 1998

3. Issues & Questions in Biomedical Research
Normality Is a person sick or well?
Abnormality What abnormalities are associated with having a disease?
Diagnosis How accurate are diagnostic tests or strategies used to find a
disease?
Frequency How often does a disease occur?
Risk What factors are associated with an increased likelihood of disease?
Prognosis What are the consequences of having a disease?
Prevention Does intervention on people without disease work?
Does early detection and treatment improve the course of disease?.
Cause What condition results in a disease?
What are the pathogenetic mechanisms of disease?

4. Evidence Based Alliance
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 values and expectations.
Clinical
Expertise
Research
Evidence
Patient Preferences
The unique preferences, concerns and expectations of each patient
EBM
Clinically relevant, patient centred, research about:
Diagnosis
Prognosis
Interventions
EBM. Sackett et al 1996

5. Evidence Based Alliance?
Our clinical experience alone may not improve our patient’s lives
Clinical
Expertise
Research
Evidence
Patient Preferences
Are our patients always able to judge what is best for them?
EBM
A lot of research is not aimed at improving the lives of patients!
Who asks them?

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
What is the prevalence of a condition?.
What is the average (Mean) of a characteristics?
What is the strength of correlation between two quantitative parameters?
What is the agreement between methods?
What are the diagnostic characteristics of a candidate test (categorical/quantitative) with reference to a “Gold Standard”?.
What is incidence of an outcome?.
What are the predictors of an outcome?
What are the risk factors associated with an outcome?
Evaluation of a candidate intervention against a control (standard of care)?. 7 7 7

8. Population vs. Sample

9. Population Sample
Parameter Statistic
Sampling variation

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

11. Population v. sample - relationship11

12. Overview of Study Designs in Clinical Research12 12

13. Variables
Outcome
Exposures
Confounder(s)

14. Total variability = True variability + Error
Sources of Error in Measurements
Observer
Subject
Instrument 14

15. Types of Variability Systolic Blood Pressure Systematic Time 24 hr
Random
Systematic
Time
24 hr

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, 1988.

18. Illustration of the Difference Between Precision and Accuracy
Good Precision
Poor Accuracy
Poor Precision
Good Accuracy
Good Precision
Good Accuracy
Poor Precision
Poor Accuracy 18
Hulley & Cummings, Designing Clinical Research, 1988.

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

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

21. Statistical Methods I. Design Stage II. Analysis 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.

23. Five Important Terms Outcome Exposures Bias Confounder(s)
Chance factor

24. Measurement/Analysis
Variables
Functional Relationship
Measurement/Analysis
Categorical
Quantitative
Time to an event
Outcome √
Exposure x
Other factors
Confounder/s
Effect modifier/s
Interaction

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. Measures of Associations
Chapter 8: Association & Impact
11/7/2017
Measures of Associations
Measures of association are mathematical comparisons
Mathematical comparisons can be done in
absolute terms, or
relative terms
We compare the weight of a man of 100 kg to the weight of a woman of 50 kg.
-Absolute comparisons are derived by subtraction and using (original units of measure kg)
-{Relative comparisons are derived by division (the division cancels out units, making a unit-free comparison}
Epi Kept Simple 26 26

27. Absolute and Relative Measures
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 certain outcome 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 observed association
- role of confounding variable(s)
Or it could be just a matter of chance
Or it could be a real association

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

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

39. Association Between Demographic and Clinical Factors and Smoking Status
Non-smokers
Former smokers
Recent quitters
Persistent smokers
Age, year + SD
Duration of angina, month + SD
Diabetes, %
21%
18% 8%
10%
Hypertension, %
54%
48%
38%
39%
Extent of coronary artery disease, %
One vessel
Two vessels
Three vessels
50%
36%
14%
51%
13%
57%
34%
55% 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:

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
( )
0.56
( )
0.74
( )
Relative risk of death Multivariable
1.34
( )
1.21
( )
1.76
( )
Hasdai, D., et al. “Effect of smoking status on the long-term outcome after successful percutaneous coronary revascularization.” N. Engl. J. Med. 1997; 336:

41. Myocardial Infarction
Intervening Variable
An intervening variable is on the causal pathway to the outcome
Alcohol Consumption
Blood Pressure
BMI
Myocardial Infarction
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.

42. -Prev, mean Prev of RF’s & Measures of Assoc.
What to compute?
Study Designs
Cross-Sectional
Case-Control
Cohort
Clinical Trial
Objective(s)
Primary Secondary
Burden Hypothesis generation
-Prev, mean 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. 42

43. Statistical Analysis is a computing problem: Avoid Such a thinking
Prevention is Cost-Effective: Also true for Biostatistics
Preventive measures:
Must have knowledge of Principles of Research Methods & Biostatistics.
Develop computing skills

44. Thank you
Thank You