dr vivek baliga - the basics of medical statistics
TRANSCRIPT
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Medical Statistics – Medical Statistics – The Basics The Basics Dr Vivek Baliga BDr Vivek Baliga B
Consultant Internal Medicine, Consultant Internal Medicine, Baliga Diagnostics Pvt. LtdBaliga Diagnostics Pvt. Ltd
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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
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Topics to cover• Types of data• Types of studies• Displaying data
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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
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Measures of Effect• Describe the measure that is used
to compare treatment effects in 2 or more comparison groups
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Measure of Effect• Quantitative Variables
– Mean– Median
• Categorical Variables– Risks– Odds Ratio
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• 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
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Normal Distribution Curve
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Standard Deviation
2+8+10+13+22 = 55Mean = 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
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Standard deviation• Estimate of variability of
observations• Larger sample provides a better
and more precise estimate of the standard deviation.
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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
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Types of studies• Randomised control trials• Cohort studies• Case control studies• Cross sectional studies• Case reports
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Randomised Control Trials
• Gold standard in medical research• Best to study cause vs effect• Various components
– Randomisation– Blinding– Controlled
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Randomised Control TrialsSelect a population
Select a Sample
Make necessary exclusions
Randomise
Experimental group Control group
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Randomised Control Trials
• Randomisation– Each patient has an equal chance of
each treatment option– Fair unbiased comparison of
treatment
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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
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Randomised Control Trials
• Controlled trial– Placebo controlled : Simvastatin vs
placebo– Active control : Simvastatin vs
Pravastatin– Active – placebo –control :
Simvastatin vs pravastatin vs placebo
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Randomised Control Trials
• Advantages– Prospective design– Rigorous
evaluation of a single variable
– Eradicates bias– Uses null
hypothesis
• Disadvantages– Expensive– Time consuming
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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.
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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
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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
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Cohort studies- Elements
• Obtaining data– Interviews/questionnaires – dolls study– Review of records– Medical examination and special tests– Environmental surveys – exposure etc
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Cohort studies- Elements
• Selection of comparison groups– Internal – within the cohort– External – eg radiologists vs
ophthalmologists– General population
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Cohort studies- Elements
• Follow up– Periodic examination - best method– Questionnaires– Review of records periodically
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Cohort studies- Elements
• Analysis– Incidence rates– Estimation of risk
• Relative risk• Attributable risk
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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)
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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.
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Cohort studies- Risks• Attributable risks
– Incidence among exposed-non exposed x100Incidence among exposed
– Tells us to what extent the disease under study can be attributed to the exposure.
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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
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Case Control Study• Retrospective study• Both exposure and outcome have
occurred before the start of the study
• Uses a ‘control’ or comparison group
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Case Control Study• Selection of cases and controls• Matching• Measurement of exposure• Analysis and interpretation
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Case Control Study- Analysis
• Exposure rates • Relative risk• Odds ratio
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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)
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Bias in Case Control Study
• Confounding factors – alcoholism and oesophageal cancer; smoking is a confounding factor.
• Recall bias• Selection bias• Interviewers bias
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Cross sectional studies• ‘Prevalence study’• Based on a single examination of a
cross section of population at one point in time.
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Meta-analysis• Statistical analysis of the results
from independent studies, which generally aims to produce a single estimate of treatment effect.
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Displaying Data• Bar Charts• Histogram• Line diagrams• Pie charts• Scatter plots• Forest plots
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