Dr Vivek Baliga - The Basics Of Medical Statistics

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

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

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

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

Measures of Effect• Describe the measure that is used

to compare treatment effects in 2 or more comparison groups

Measure of Effect• Quantitative Variables

– Mean– Median

• Categorical Variables– Risks– Odds Ratio

• 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

Normal Distribution Curve

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

Standard deviation• Estimate of variability of

observations• Larger sample provides a better

and more precise estimate of the standard deviation.

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

baligs

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

Randomised Control Trials

• Gold standard in medical research• Best to study cause vs effect• Various components

– Randomisation– Blinding– Controlled

Randomised Control TrialsSelect a population

Select a Sample

Make necessary exclusions

Randomise

Experimental group Control group


• Randomisation– Each patient has an equal chance of

each treatment option– Fair unbiased comparison of

treatment


• 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


• Controlled trial– Placebo controlled : Simvastatin vs

placebo– Active control : Simvastatin vs

Pravastatin– Active – placebo –control :

Simvastatin vs pravastatin vs placebo


• Advantages– Prospective design– Rigorous

evaluation of a single variable

– Eradicates bias– Uses null

hypothesis

• Disadvantages– Expensive– Time consuming

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.

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

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


• Obtaining data– Interviews/questionnaires – dolls study– Review of records– Medical examination and special tests– Environmental surveys – exposure etc


• Selection of comparison groups– Internal – within the cohort– External – eg radiologists vs

ophthalmologists– General population


• Follow up– Periodic examination - best method– Questionnaires– Review of records periodically


• Analysis– Incidence rates– Estimation of risk

• Relative risk• Attributable risk


• 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)

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.

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.

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

Case Control Study• Retrospective study• Both exposure and outcome have

occurred before the start of the study

• Uses a ‘control’ or comparison group

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

Case Control Study- Analysis

• Exposure rates • Relative risk• Odds ratio

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)

Bias in Case Control Study

• Confounding factors – alcoholism and oesophageal cancer; smoking is a confounding factor.

• Recall bias• Selection bias• Interviewers bias

Cross sectional studies• ‘Prevalence study’• Based on a single examination of a

cross section of population at one point in time.

Meta-analysis• Statistical analysis of the results

from independent studies, which generally aims to produce a single estimate of treatment effect.

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

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Dr Vivek Baliga - The Basics Of Medical Statistics