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# Why to know statistics

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### Why to know statistics

1. 1. Why to know statistics
2. 2. <ul><li>To understand data </li></ul>
3. 3. Example <ul><li>One of your colleague is an oncology surgeon </li></ul><ul><li>60% of his cases died </li></ul><ul><li>Does this mean that he is a looser!! </li></ul>
4. 4. <ul><li>We should ask what are the results of his colleagues in similar patients </li></ul><ul><li>How many patients he operated upon e.g 2/3!!!! </li></ul>
5. 5. <ul><li>To summarize data </li></ul>
6. 6. Example <ul><li>Diastolic Blood pressure </li></ul><ul><li>80,70,65,90,74,80,60,90,60,75,80,90,100,100,100,95 </li></ul><ul><li>Age </li></ul><ul><li>24,30,26,40,28,21,26,31,32,36,27,45,62,58,52,50,60 </li></ul>
7. 7. Vital for research <ul><li>Without the use of statistics it would be very difficult to make decisions based on the data collected from a research project </li></ul>
8. 8. Statistical steps in research <ul><li>Collect data </li></ul><ul><li>Organise data </li></ul><ul><li>Analyse data </li></ul><ul><li>Interpret the data </li></ul><ul><li>Present the data </li></ul>
9. 9. How to read the results <ul><li>An understanding of basic statistics will provide you with the fundamental skills necessary to read and evaluate results section in published papers </li></ul>
10. 10. Are groups comparable!!! <ul><li>the baseline characteristics of the groups being studied should be comparable </li></ul><ul><li>If not, they should be adjusted for differences </li></ul>
11. 11. statistical tests <ul><li>Are they frequently used tests!! </li></ul><ul><li>If not, why!! </li></ul>
12. 12. <ul><li>Are the data analysed according to the original protocol? </li></ul>
13. 13. <ul><li>Was follow- up complete? </li></ul><ul><li>Patients lost to follow-up ……… loss of subjects bias </li></ul><ul><li>> 10% - 15 % ………………… ..invalid results </li></ul>
14. 14. P value <ul><li>A P value of <0.05 means that this result would have arisen by chance on less than one occasion in 20 </li></ul>
15. 15. Standardization of measures of outcome: <ul><li>Odds and odds ratio </li></ul><ul><li>The odds is the number of patients who fulfil the criteria for a given endpoint divided by the number of patients who do not. </li></ul>
16. 16. For example <ul><li>the odds of diarrhoea during treatment with an antibiotic in a group of 10 patients may be 4 to 6 (4 with diarrhoea divided by 6 without, 0.66); </li></ul><ul><li>in a control group the odds may be 1 to 9 (0.11). The odds ratio of treatment to control group would be 6 (0.66÷0.11). </li></ul>
17. 17. Risk and relative risk <ul><li>The risk is the number of patients who fulfil the criteria for a given end point divided by the total number of patients. </li></ul>
18. 18. For example, <ul><li>the risk of diarrhoea during treatment with an antibiotic in a group of 10 patients may be 4 to 10; in the control group the risks may be 1 to 10. The relative risk of treatment to control group would be 4 (0.4÷0.1). </li></ul>
19. 19. C.I <ul><li>The confidence interval around a result in a clinical trial indicates the limits within which the &quot;real&quot; difference between the treatments is likely to lie, </li></ul><ul><li>hence the strength of the inference that can be drawn from the result </li></ul>
20. 20. <ul><li>Example: </li></ul><ul><li>95 % CI for RRR 25 % : </li></ul><ul><li>sample size 100 …………… .= -- 38 % to 59 % </li></ul><ul><li>Sample size 1000 ………… .= 9 % to 41 % </li></ul><ul><li>The larger the sample size , the narrower and more precise the CI , and the greater our confidence that the true RRR is closer to what we have observed . </li></ul>
21. 21. <ul><li>OR = 0.34, 95% CI 0.23 - 0.52 </li></ul><ul><li>Odds Ratio < 1  decreased risk </li></ul><ul><li>Confidence Interval does not cross 1  statistically significant </li></ul>
22. 22. <ul><li>A statistically significant result may not be clinically significant. </li></ul>
23. 23. <ul><li>The results of intervention trials should be expressed in terms of the likely benefit an individual could expect (for example, the absolute risk reduction) </li></ul>
24. 24. How large was the treatment effect ? <ul><li>Treatment effect …………… .. Adverse outcome </li></ul><ul><li>e.g.; </li></ul><ul><li>Risk of outcome without therapy ( baseline risk ) X ( = 20 % or 0.20 ) </li></ul><ul><li>Risk of outcome with therapy Y ( = 15% 0r 0.15) </li></ul>
25. 25. <ul><li>Absolute risk reduction= X -- Y </li></ul><ul><li>0.20-0.15= 0.05 </li></ul><ul><li>Relative risk ( RR )= Y / X = 0.15 /0.20 = 0.75 </li></ul><ul><li>Relative risk reduction ( RRR ) = </li></ul><ul><li>{ X -- Y/ X } x 100 % = 0.05 / 0.2 x 100%= 25 % i.e : </li></ul><ul><li>therapy reduced the risk of the outcome by 25 % relative to that occurring among the controls </li></ul>
26. 26. <ul><li>the greater the RRR, the more effective the therapy. </li></ul>
27. 27. Example: M.I <ul><li>patients receiving medical treatment have a chance of 404/1324=0.305 or 30.5% of being dead at 10 years. </li></ul><ul><li>Let us call this risk x . Patients randomised to coronary artery bypass grafting have a chance of 350/1325=0.264 or 26.4% of being dead at 10 years. Let us call this risk y . </li></ul>
28. 28. RR <ul><li>The relative risk of death — </li></ul><ul><li>that is, the risk in surgically treated patients compared with medically treated controls — is </li></ul><ul><li>y/x or 0.264/0.305=0.87 (87%). </li></ul>
29. 29. RRR <ul><li>The relative risk reduction — that is, the amount by which the risk of death is reduced by the surgery — is 100%-87% (1- y / x )=13%. </li></ul>
30. 30. ARR <ul><li>The absolute risk reduction (or risk difference) — that is, the absolute amount by which surgical treatment reduces the risk of death at 10 years — is 30.5%-26.4%=4.1% (0.041). </li></ul>
31. 31. NNT <ul><li>The number needed to treat — how many patients need coronary artery bypass grafting in order to prevent, on average, one death after 10 years — is the reciprocal of the absolute risk reduction: 1/ARR=1/0.041=24. </li></ul>
32. 32. Conclusion <ul><li>to be able to effectively conduct research </li></ul><ul><li>to be able to read and evaluate journal articles </li></ul><ul><li>to further develop critical thinking and analytic skills </li></ul><ul><li>to know when you need to hire outside statistical help </li></ul>

Jun. 9, 2016

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