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SIMPLE TWO GROUP TESTS Prof Peter T Donnan Prof Peter T Donnan.

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Presentation on theme: "SIMPLE TWO GROUP TESTS Prof Peter T Donnan Prof Peter T Donnan."— Presentation transcript:

1 SIMPLE TWO GROUP TESTS Prof Peter T Donnan Prof Peter T Donnan

2 TESTS TO BE COVERED 1. Chi-squared test (2x2) 2. t-test 3. Logrank test

3 CHI SQUARED TEST Used to compare the proportions of observations in different categories. Example: Are gender and achieving LDL target related? Null hypothesis: There is no association of gender with achieving LDL target (or no difference between men and women) Achieved LDL target Total NoYes Gender Male140563703 Female149531680 Total28910941383

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9 SIGNIFICANCE OF THE TEST STATISTICS  The value  2 = 0.834 is not significant (p = 0.361)  The null hypothesis can not be rejected.  Gender and achieving LDL target are not related  % Males meeting target: 80.1%  % Females meeting target: 78.1%

10 CONDITIONS 1.Data are categorical. 2.If you tried to do Crosstabs with continuous variables you will get one column or row for each unique value!

11 T-TEST – COMPARE TWO MEANS 1.Parametric test since comparing means 2.Paired samples t-test – the mean difference between two linked groups 3.Independent samples t-test – the mean difference between two independent groups.

12 PAIRED SAMPLE T-TEST One variable measured in: 2 different groups who are matched or same group at 2 different times (e.g before / after) Example: Is there a difference in LDL level before and after treatment in the total sample? Is this paired or unpaired?

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16 INDEPENDENT SAMPLES T-TEST Example: Is there a difference between the population mean ages of the males and females? Assumptions: - In the population of interest the variable is normally distributed. - The variances of the 2 groups are the same

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21 LOGRANK TEST

22 Example: Survival curves for women with glioma by diagnosis. Bland J M, Altman D G BMJ 2004;328:1073

23 The most popular method of comparing the survival between groups, which takes the whole follow up period into account. LOGRANK TEST

24  Log rank test involves calculating Chi-squared (  2 ) statistic for difference in median survival between two groups. Example: Do colorectal cancer patients with hypertension have worse survival than patients without hypertension?

25 Logrank Test: Null Hypothesis The Null hypothesis for the logrank test: Hazard Rate group A = Hazard Rate for group B = HR = O A / E A = 1 O B / E B

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30 THANK YOU FOR LISTENING Explore 2-group tests further with datasets: LDL Data.sav colorectal.sav


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