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Dr Vivek Baliga - The Basics Of Medical Statistics

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Basic Medical Statistics
Basic Medical Statistics
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Dr Vivek Baliga - The Basics Of Medical Statistics

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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

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

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Dr Vivek Baliga - The Basics Of Medical Statistics

  1. 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. 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. 3. Topics to cover • Types of data • Types of studies • Displaying data
  4. 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. 5. Measures of Effect • Describe the measure that is used to compare treatment effects in 2 or more comparison groups
  6. 6. Measure of Effect • Quantitative Variables – Mean – Median • Categorical Variables – Risks – Odds Ratio
  7. 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. 8. Normal Distribution Curve
  9. 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. 10. Standard deviation • Estimate of variability of observations • Larger sample provides a better and more precise estimate of the standard deviation.
  11. 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. 12. Types of studies • Randomised control trials • Cohort studies • Case control studies • Cross sectional studies • Case reports
  13. 13. Randomised Control Trials • Gold standard in medical research • Best to study cause vs effect • Various components – Randomisation – Blinding – Controlled
  14. 14. Randomised Control Trials Select a population Select a Sample Make necessary exclusions Randomise Experimental group Control group
  15. 15. Randomised Control Trials • Randomisation – Each patient has an equal chance of each treatment option – Fair unbiased comparison of treatment
  16. 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. 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. 18. Randomised Control Trials • Advantages – Prospective design – Rigorous evaluation of a single variable – Eradicates bias – Uses null hypothesis • Disadvantages – Expensive – Time consuming
  19. 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. 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. 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. 22. Cohort studies- Elements • Obtaining data – Interviews/questionnaires – dolls study – Review of records – Medical examination and special tests – Environmental surveys – exposure etc
  23. 23. Cohort studies- Elements • Selection of comparison groups – Internal – within the cohort – External – eg radiologists vs ophthalmologists – General population
  24. 24. Cohort studies- Elements • Follow up – Periodic examination - best method – Questionnaires – Review of records periodically
  25. 25. Cohort studies- Elements • Analysis – Incidence rates – Estimation of risk • Relative risk • Attributable risk
  26. 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. 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. 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. 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. 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. 31. Case Control Study • Selection of cases and controls • Matching • Measurement of exposure • Analysis and interpretation
  32. 32. Case Control Study- Analysis • Exposure rates • Relative risk • Odds ratio
  33. 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. 34. Bias in Case Control Study • Confounding factors – alcoholism and oesophageal cancer; smoking is a confounding factor. • Recall bias • Selection bias • Interviewers bias
  35. 35. Cross sectional studies • ‘Prevalence study’ • Based on a single examination of a cross section of population at one point in time.
  36. 36. Meta-analysis • Statistical analysis of the results from independent studies, which generally aims to produce a single estimate of treatment effect.
  37. 37. Displaying Data • Bar Charts • Histogram • Line diagrams • Pie charts • Scatter plots • Forest plots
  38. 38. THANK YOU!

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