Medical statistics can be daunting. Understanding them is essential to understand any research paper. Here are some basic in medical statistics by Dr Vivek Baliga, Consultant Internal Medicine, Bangalore. Read more by Dr Vivek Baliga at http://drvivekbaliga.net

1. Medical Statistics –Medical Statistics –
The BasicsThe Basics
Dr Vivek Baliga BDr Vivek Baliga B
Consultant Internal Medicine,Consultant Internal Medicine,
Baliga Diagnostics Pvt. LtdBaliga Diagnostics Pvt. Ltd

2. What is Statistics?
• Science of collecting, organising and
interpreting numerical facts
• Science of learning from data :
– Design the data collection
– Prepare the data for analysis
– Analyse the data
– Communicate the results of the data

3. Topics to cover
• Types of data
• Types of studies
• Displaying data

4. Types of data
• Quantitative
(How much?)
– Measured : BP,
Height
– Counted : Attacks
of asthma a week
• Categorical
(What type?)
– Nominal : Sex
(m/f), hair colour
– Ordinal : Grade of
breast Ca
– Binary :
Male/Female,
Dead/alive

5. Measures of Effect
• Describe the measure that is used to
compare treatment effects in 2 or
more comparison groups

6. Measure of Effect
• Quantitative Variables
– Mean
– Median
• Categorical Variables
– Risks
– Odds Ratio

7. • Mean
1+2+3+6+7+12+18 = 49
Mean = 49/7 =7
• Median (Odd number N)
1+2+3+6+7+12+18
Median =6
• Median (Even number N)
2+3+6+7+12+18
Median = 6+7/2 = 6.5

8. Normal Distribution Curve

9. Standard Deviation
2+8+10+13+22 = 55
Mean = 55/5 =11
Variance = (2-11)2
+(8-11)2
+(10-11)2
+(13-11)2
+(22-11)2
N-1
= 216/4 = 54
Standard Deviation = √54 = 7.2

10. Standard deviation
• Estimate of variability of
observations
• Larger sample provides a better and
more precise estimate of the
standard deviation.

11. Measures of Effect
• Absolute risk : A/A+C
• Relative Risk :
A/A+C÷B/B+D
• Absolute risk
reduction : A/A+C-
B/B+D
• Number needed to
treat : 1/ARR
D+ D-
Ex+ A B
Ex- C D
A+C B+D

12. Types of studies
• Randomised control trials
• Cohort studies
• Case control studies
• Cross sectional studies
• Case reports

13. Randomised Control
Trials
• Gold standard in medical research
• Best to study cause vs effect
• Various components
– Randomisation
– Blinding
– Controlled

14. Randomised Control
Trials
Select a population
Select a Sample
Make necessary exclusions
Randomise
Experimental group Control group

15. Randomised Control
Trials
• Randomisation
– Each patient has an equal chance of each
treatment option
– Fair unbiased comparison of treatment

16. Randomised Control
Trials
• Blinding
– Single blind : patient cannot predict
which treatment they get
– Double blind : neither patient nor
investigator knows
– Triple blind : Neither pt, investigator or
person administering treatment (eg
pharmacist) knows

17. Randomised Control
Trials
• Controlled trial
– Placebo controlled : Simvastatin vs
placebo
– Active control : Simvastatin vs
Pravastatin
– Active – placebo –control : Simvastatin
vs pravastatin vs placebo

18. Randomised Control
Trials
• Advantages
– Prospective design
– Rigorous evaluation
of a single variable
– Eradicates bias
– Uses null
hypothesis
• Disadvantages
– Expensive
– Time consuming

19. Cohort studies
• Cohort is a group of people who share a
common characteristic or experience
within a defined time period
• Eg : People born in 1980= birth cohort
• Cohort studies are done to obtain
additional evidence that there is an
association between a suspected cause
and disease.

20. Cohort studies
• Prospective
– Follow up in years
– Can collect confounding factors
– Expensive, time consuming
– E.g.: Framingham heart study
• Retrospective
– Incomplete information
– Confounding factors may not be collected
– Quick, cheap
– E.g.: angiosarcoma in relation to poly-vinyl chloride

21. Cohort studies- Elements
• Selection of subjects
– General population
– Special groups eg: Dolls study of
smoking and lung cancer in British
doctors in 1951
– Exposure groups : eg radiologists and X-
rays

22. Cohort studies- Elements
• Obtaining data
– Interviews/questionnaires – dolls study
– Review of records
– Medical examination and special tests
– Environmental surveys – exposure etc

23. Cohort studies- Elements
• Selection of comparison groups
– Internal – within the cohort
– External – eg radiologists vs
ophthalmologists
– General population

24. Cohort studies- Elements
• Follow up
– Periodic examination - best method
– Questionnaires
– Review of records periodically

25. Cohort studies- Elements
• Analysis
– Incidence rates
– Estimation of risk
• Relative risk
• Attributable risk

26. Cohort studies- Elements
• Incidence rates
– Exposed 70/7000 = 10
per 1000
– Non Exposed 3/3000 =
1 per 1000
• Relative risk =10/1 = 10
• Attributable risk =
[(10-1)/10]x100 = 90%
Cigarette
smoking
Ca + Ca - Total
Yes 70 (a) 6930
(b)
7000
(a+b)
No 3(c) 2997
(d)
3000
(c+d)

27. Cohort studies- Risks
• Relative risk
– Incidence among exposed
Incidence among non exposed
– RR = 1 means no association
– RR > 1 implies ‘positive’ association
– Smokers are 10 times at risk of lung Ca that
non smokers.

28. Cohort studies- Risks
• Attributable risks
– Incidence among exposed-non exposed x100
Incidence among exposed
– Tells us to what extent the disease under study can be
attributed to the exposure.

29. Cohort studies
• Strengths
– Valuable if
exposure is rare
– Examine multiple
effects of an
exposure
– Can measure
incidence of a
disease
• Limitations
– Cannot evaluate
rare diseases
– Expensive and
time consuming if
prospective
– Several losses to
follow up can
effect validity

30. Case Control Study
• Retrospective study
• Both exposure and outcome have
occurred before the start of the
study
• Uses a ‘control’ or comparison group

31. Case Control Study
• Selection of cases and controls
• Matching
• Measurement of exposure
• Analysis and interpretation

32. Case Control Study-
Analysis
• Exposure rates
• Relative risk
• Odds ratio

33. Case Control Study
• Exposure rates
– Cases a/(a+c) =94.2%
– Controls b/(b+d) = 67%
• Relative risk = a/a+c ÷b/b+d
• Odds ratio = ad/bc = 8.1
– Smokers of < 5/day have a
risk of developing lung cancer
8.1 times that of non-
smokers.
Cases
(with
lung Ca)
Controls
(without
Lung Ca)
Smokers
(<5/day)
33 (a) 55 (b)
Non
Smokers
2(c) 27(d)
Total 35 (a+c) 82 (b+d)

34. Bias in Case Control
Study
• Confounding factors – alcoholism and
oesophageal cancer; smoking is a
confounding factor.
• Recall bias
• Selection bias
• Interviewers bias

35. Cross sectional studies
• ‘Prevalence study’
• Based on a single examination of a
cross section of population at one
point in time.

36. Meta-analysis
• Statistical analysis of the results
from independent studies, which
generally aims to produce a single
estimate of treatment effect.

37. Displaying Data
• Bar Charts
• Histogram
• Line diagrams
• Pie charts
• Scatter plots
• Forest plots

38. THANK YOU!