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# Medical Statistics Pt 2

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### Medical Statistics Pt 2

1. 1. Using Statistical tests<br />Richard Salisbury richardasalisbury@gmail.com<br />
2. 2. What I’m going to cover<br />Key concepts<br />What test when?<br />Examples<br />
3. 3. Key concept 1: The null hypothesis<br />I predict that any difference seen between two groups is due to chance alone.<br />Use 95% cut off in medicine<br />P > 0.05 = accept null hypothesis<br />P < 0.05 = reject null hypothesis as difference is NOT due to chance. There is a statistically significant difference between groups.<br />
4. 4. Key concept 2: Data types<br />Continuous eg. height<br />Discrete - integers<br />Ordinal - ranked<br />Categorical eg. Hair colour<br />Dichotomous/Binary eg. Yes/no<br />
5. 5. Key concept 3: Normal/Gaussian distribution<br />Cumulative frequency <br />Mean =median=mode <br />Value<br />Central Limit Theorem<br />Shapiro Wilk test<br />
6. 6. Common statistical tests<br />
7. 7. Chi-squared test<br />Which test to use?<br />Yes<br />Is data categorical?<br />No<br />Mann-Whitney U test<br />Is data normally distributed?<br />No<br />2 groups or less?<br />Yes<br />No<br />Yes<br />Is n > 30<br />ANOVA<br />No<br />Yes<br />T-test<br />Z-test<br />
8. 8. Chi-squared test<br />Which test to use?<br />Yes<br />Is data categorical?<br />No<br />Mann-Whitney U test<br />Is data normally distributed?<br />No<br />2 groups or less?<br />Yes<br />No<br />Yes<br />Is n > 30<br />ANOVA<br />No<br />Yes<br />T-test<br />Z-test<br />
9. 9. Normally distributed data - T-test<br />Comparison of means taking into account spread<br />Allows comparison 2 groups OR a comparison of one group and an expected mean<br />1 tailed Vs 2 tailed – what question are you asking?<br />Independent groups Vs Dependent/Paired groups<br />
10. 10. Example 1<br />I have audited BMI of 20 patients undergoing gastric banding, I want to compare this with the national average.<br />Data - BMI is a continuous variable and therefore will be normally distributed about the mean.<br />Groups - 2 groups<br />Number - n<30<br />T-test using mean and variance of my group compared to mean and variance of national average.<br />2 tail t-test as I am interested in knowing whether the BMI is different therefore either smaller or larger<br />1 tail t-test could be used if I wanted to ask is the BMI larger in patients undergoing gastric banding compared to national average<br />
11. 11. Example 2<br />Does CBT change the mood (measured by visual analogue scale) of 50 depressed individuals? – Comparison of before and after scores<br />Data – Normally distributed<br />Groups – 2; before Vs after CBT<br />Number – n>30 BUT groups are not independent – repeated measures<br />2-tail paired T-test<br />1-tail paired t-test would be for a question that asks if CBT increases mood.<br />
12. 12. Alternatives to t-test<br />Z-test for independent variables where n > 30<br />ANOVA for more than 2 groups – multiple comparisons (the more comparisons you do, the more likely you are to get a false positive)<br />ANOVA tests for difference between all groups<br />A post test egBonferroni then tests for differences between individual groups<br />Eg. RCT Placebo Vs Drug A Vs Drug B<br />
13. 13. Chi-squared test<br />Which test to use?<br />Yes<br />Is data categorical?<br />No<br />Mann-Whitney U test<br />Is data normally distributed?<br />No<br />2 groups or less?<br />Yes<br />No<br />Yes<br />Is n > 30<br />ANOVA<br />No<br />Yes<br />T-test<br />Z-test<br />
14. 14. Mann-whitney U test<br />Non-parametric test (Parameter-free test)<br />Not normally distributed<br />Small sample size (n<10)<br />Discrete (integers)/Ordinal (ranked) data<br />Upper or lower limits<br /><ul><li>2 Independent groups
15. 15. Uses ranking to analyse data (not important)</li></li></ul><li>Categorical Data<br />Data which can be put into categories<br />Best displayed by a frequency table<br />
16. 16. Chi squared and Fisher’s exact test<br />Used to compare categorical data against expected data (probabilities eg. Mendellian crosses) OR against other independent categorical data.<br />Fisher’s exact test is more accurate, especially if n is small, but is harder to calculate.<br />
17. 17. Regression Analysis<br />Compares how an independent variable changes the value of a dependent variable, independent of any other independent variables.<br />This is as complicated as it sounds. Seek help early!<br />
18. 18. Examples to finish<br />
19. 19. Example 1(Kostov DV, KobakovGL.Segmental liver resection for colorectal metastases. J Gastrointestin Liver Dis. 2009 Dec;18(4):447-53)<br />56 colorectal liver metastasis patients had two types of operations for their liver metastasis: 38 patients had major liver resection with 16 of them having surgical wound infection later. 18 patients had segmentectomy and only 7 of them experienced wound infection later. <br /><ul><li>Objective: is the occurrence of wound infection different in these two types of operations? </li></li></ul><li>(Kostov DV, Kobakov GL.Segmental liver resection for colorectal metastases. J Gastrointestin Liver Dis. 2009 Dec;18(4):447-53)<br />Analysis: comparison<br />Variable: wound infection<br />categorical <br />Comparison across segmentectomy and major liver resection<br />Chi Sqaure Test <br />yes/no<br />2 independent groups<br />
20. 20. Example 2 (Siregar P, Setiati S., Urine osmolality in the elderly. Acta Med Indones. 2010 Jan;42(1):24-6.)<br />A study recorded the urine osmolality of 13 and 15 respectively female and male elderlies.<br />Objective: is the urine osmolality different in males and females? <br />
21. 21. <ul><li>Analytical statistics: comparison
22. 22. Variable: urine osmolality
23. 23. Comparison across females and males </li></ul>2 independent groups <br /><ul><li>If data not normally distributed </li></ul>Mann Whitney U test<br /><ul><li>If data normally distributed 2 Sample T test</li></ul>continuous<br />
24. 24. Questions <br />