Medical Statistics Pt 2
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Fastbleep Academic Masterclass N

Fastbleep Academic Masterclass N

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Medical Statistics Pt 2 Presentation Transcript

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