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  1. M

    P value for trend

    I would be very grateful if someone could help me with the code for this - I'm reporting the incidence of stroke in patients with diabetes versus no diabetes in 5 year bands, and would like to calculate the P value for trend over time if possible. I've tried the ptrend command but I'm confused...
  2. M

    How do I calculate p value for incidence rate

    Hello, I have calculated the crude incidence rates per 1000 population per year of strokes in patients with and without kidney disease e.g.18.37 (16.94-19.9) vs 6.37 (5.78-7.0). How do I calculate the p value for difference? Thanks!
  3. M

    Case-case comparisons

    Dear all, I have a population of 2000 patients - all of whom have had a stroke. I have calculated the ORs of the stroke subtypes (there are 7 subtypes) according the presence or absence of kidney disease. However, some of the results don't really make sense and kidney disease appears to be...
  4. M

    Age-adjusted P value

    Hi, I have a table of baseline characteristics of a cohort in which I have compared the differences between those with kidney disease and those without, using a combination of Chi-square test, t-test, or Kruskall-Wallis test as appropriate. How do I age-adjust the P values that I get from these...
  5. M

    Comparing correlation coefficients

    Hello, I have a large sample of patients who have had a TIA, minor stroke or major stroke. I have been looking at correlations in each of these three groups. Is there a statistical test in SPSS (or elsewhere) that would allow me to compare the correlation coefficients between each of three...
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    Is there a formal test for linearity?

    Using SPSS, in preparation for correlation analysis, apart from scatterplots, is there a formal statistical test for linearity for two continuous variables? Someone suggested using Analzye -> Compare means -> Means -> under options, test for linearity? Is that the best test for this purpose...
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    When to log-transform?

    Should Iog-transform variables for correlation if they aren't normally distributed even if I'm using a non-parametric test for correlation? And if so, why? Thanks for your help!