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HaDPop

HaDPop. Measuring Disease and Exposure in Populations (MD) & Introduction to Medical Statistics (MS). Overview. Prevalence (MD) Incidence (MD) Confidence Intervals (MS) Standard Error Error Factor Null Hypothesis (MS) P-values (MS) Ratios (MD) Confounders (MD) SMR (MD). Prevalence.

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HaDPop

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  1. HaDPop Measuring Disease and Exposure in Populations (MD) & Introduction to Medical Statistics (MS)

  2. Overview • Prevalence (MD) • Incidence (MD) • Confidence Intervals (MS) • Standard Error • Error Factor • Null Hypothesis (MS) • P-values (MS) • Ratios (MD) • Confounders (MD) • SMR (MD)

  3. Prevalence No. of existing cases / No. of persons in the population • A measure of how much of a disease there is (both new and old cases) • Period and point prevalence • It gives a proportion of the population • Useful for studying long term conditions and service provision

  4. Prevalence Example • In a hypothetical office (total 1000 people), 12 were off work with the flu today. • Point Prevalence (today): 12/1000 = 1.2% • Over the past year, 150 took off work due to flu. • Period Prevalence (past year): 150/1000 = 15%

  5. Incidence No. of new cases / In a defined population in a specified time interval (person-years) • A measure of the frequency of new cases (it is a rate) • Useful for tracking infectious diseases and exploring the cause of disease (aetiology) • Person years

  6. Incidence Example • Over the last 5 years, 4000 people have been diagnosed with lung cancer (total population: 200,000) • 4000/(200,000 x 5) = 0.004 or 4 per 1000 per year

  7. Types of Incidence Numerator Disease free - 100 Denominator New Cases = 10 Non Cases = 90 • Incidence Rate – different length of follow up • Cumulative Incidence (or risk) – same length of follow up • Odds of Disease – ratio between having the disease or not Time (t)

  8. Medical Statistics • Statistics are used to estimateinformation about the general population (its not practical to measure everyone!) • This estimate is the known as the observed value and this varies from the true value due to sampling variation • The accuracy of an estimate is calculated using confidence intervals

  9. Confidence Intervals (CI) • The confidence interval is a range of values around the observed value within which the true value lies • The most common range used is the 95%CI, which means 95 times out of 100 the real value will be within that range • The way the are calculated is different depending on the statistics you are using • Proportions (i.e. prevalence) use standard error (SE) • Ratios/rates (i.e. incidence) use error factors (EF)

  10. Standard Error (Prevalence) • Note Accuracy Depends on Sample Size Note 1.96 is a constant used for working out 95% CI’s It changes if you want different CI’s

  11. Standard Error Example • Prevalence of diabetes, sample of 1000 subjects (n = 1000), 243 found to have diabetes (k = 243) • Prevalence = k/n, so = 243/1000 = 24.3% • Standard error (SE) , so = = 0.013

  12. Standard Error Example Cont. • SE = 0.013 • Original prevalence estimate (): 24.3% population had diabetes • = (0.243 – 1.96(0.013), 0.243 + 1.96(0.013) • = (0.218, 0.268) = (21.8%, 26.8%)

  13. Difference between two prevalences

  14. Error Factor (Incidence) • Note Accuracy Depends on No. cases

  15. Error Factor Example • 24 new cases of diabetes per 1000 population per year (i.e. d = 24) • Error factor = exp(1.96 x ) • = exp(1.96 x ) = 1.5 • = (0.0024/1.5, 0.0024 x 1.5) • = (0.0016, 0.0036) • = (16, 36) cases per 1000 p-y

  16. Key Points for CI’s • Proportions (prevalence) • 95%CI = “Estimate ± (‘constant’ x SE)” • Rates/ratios (incidence and SMR’s) • 95%CI= (Estimate/EF , Estimate x EF) • How to calculate the Standard error or Error factor will be given in the exam

  17. Null Hypothesis (H0) • This is used to make a comparison between different groups to see if there is a statistical difference between them • E.g. differences between different drugs • The null hypothesis is when there is no statistical difference between the two groups • Differences – null hypothesis is 0 • Ratios – null hypothesis is 1 • SMR – null hypothesis is 100

  18. Null Hypothesis • If the 95% CI includes the null hypothesis then the data agrees with the null hypothesis can’t be rejected and there is no statistical difference between the two groups • You can never accept the null hypothesis!

  19. P-values • p-values state how likely the results in the study would have occurred by chance if the null hypothesis was true • P-values <0.05 (5%) are good! They mean that the results are statistically significant and that the null hypothesis can be rejected • If the 95%CI overlap with the null hypothesis then p>0.05 and the results are not statistically significant

  20. Relative measures of exposure(Relative risk) see slide 7 • Note – an exposure can be to a treatment, therefore it can be used to find out which treatments are best

  21. Incidence Rate Ratio Example • In one group of 1000 pizza eaters that were followed for 1.5 years (‘exposed’) it was found that 33 new cases (d1) of obesity developed • In another group of 1000 non-pizza eaters that were followed for 2 years (unexposed) it was found that 27 new cases (d2) of obesity developed

  22. Incidence Rate Ratio Example Cont. • In the exposed group: 33/(1000 x 1.5) = 0.022 (or 22 per 1000 pop. per year) • So in the unexposed group: 27/(1000 x 2) = 0.014 (or 14 per 1000 pop. per year) • Error factor = exp(1.96 x ) • = exp(1.96 x ) = 1.66

  23. Incidence Rate Ratio Example Cont. • Estimate (IRR) = 1.57 • EF = 1.66 • 95% CI = (est / EF, est x EF) • = (1.57/1.66, 1.57x1.66) = (0.95, 2.61) • IRR = 1.57 (0.95, 2.61)

  24. Incidence Rate Ratio Example Cont.Interpretation of results • IRR of 1.57 indicates that the observed value indicates a damaging effect of eating pizza on becoming obese on (i.e. >1) • We are 95% confident that the true IRR lies between 0.95 and 2.61. • The 95% confidence interval includes the null hypothesis (IRR=1) and so the result is not statistically significant at the p<0.05 level. • Null hypothesis cannot be rejected. • The results do not indicate an association between eating pizza and obesity.

  25. Absolute Measures of Risk(attributable risk) • Risk difference = risk exposed - risk non-exposed • Attributable Risk = IR exposed - IR non-exposed = events saved per 1,000 • Attributable Risk (%) = Attributable Risk / IR exposed

  26. Examples of attributable risk? • People with lung cancer can be smokers and non smokers • Thus the attributable risk of smoking is the difference between the incidence of smokers and non smokers • Ie the attributable risk is the risk above background risk (the non smokers with lung cancer have suffered from the background risk)

  27. Confounders • A Confounder is a factor that is associated with the exposure under study and independently affects disease risk

  28. Standardised mortality ratio (SMR) • Compares the observed number of deaths in the population under study with the expected number of deaths based on the standard population • It accounts for common confounders such as age and gender • Uses error factors for 95% CI

  29. Thanks for listening • Any questions please Facebook me!!!

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