Medical Statistics Pt 2
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Transcript of Medical Statistics Pt 2
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What I’m going to cover
Key concepts What test when? Examples
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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.
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Key concept 2: Data types
Continuous eg. height Discrete - integers Ordinal - ranked Categorical eg. Hair colour Dichotomous/Binary eg. Yes/no
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Key concept 3: Normal/Gaussian distribution
Value
Cumulative frequency
Mean =median=mode
Central Limit TheoremShapiro Wilk test
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Common statistical testsContinuous and Gaussian distributed
Continuous or discrete and NOT Gaussian distributed
Binary/Categorical
Comparison of Independent 2 groups
Box plotT-testZ-test
Box plotCross-tabulationMann-Whitney U-test
2x2 frequency tableChi-squared testFisher’s exact test
Comparison of more than 2 groups
Analysis of variance (ANOVA)
Kruskal Wallis Cross-tabulationChi-squared test
Comparison of 2 related outcomes
Paired t-test Wilcoxon matched pairs
McNemar’s test
Relationship between a dependent variable and one or more independent variables
Scatter plotRegression analysisPearson’s correlation coefficient
Spearman correlation or Kendall’s correlation coefficient
Phi coefficientLogistic Regression
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Which test to use?
Is data normally
distributed?
Is data categorical?
2 groups or less?
Chi-squared test
Mann-Whitney U
test
Is n > 30 ANOVA
Yes
No
Yes
No
NoYe
s
Yes
No
Z-test T-test
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Which test to use?
Is data normally
distributed?
Is data categorical?
2 groups or less?
Chi-squared test
Mann-Whitney U
test
Is n > 30 ANOVA
Yes
No
Yes
No
NoYe
s
Yes
No
Z-test T-test
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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
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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
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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.
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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 eg Bonferroni then tests for
differences between individual groups Eg. RCT Placebo Vs Drug A Vs Drug B
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Which test to use?
Is data normally
distributed?
Is data categorical?
2 groups or less?
Chi-squared test
Mann-Whitney U
test
Is n > 30 ANOVA
Yes
No
Yes
No
NoYe
s
Yes
No
Z-test T-test
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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• Uses ranking to analyse data (not
important)
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Categorical Data
Data which can be put into categories Best displayed by a frequency table
Exposure to dust
No exposure to dust
Total
Asthma sympts
59 62 121
No asthma sympts
4 11 15
63 73 136
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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.
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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!
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Examples to finish
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Example 1(Kostov DV, Kobakov GL.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?
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(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
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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?
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• Analytical statistics: comparison• Variable: urine osmolality• 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
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Questions