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The statistical analysis was done by the Kaplan-Meier method, Wilcoxon's generalised test for studying the differences in survival curves and Cox's regression analysis for independent prognostic factors.
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Overall survival curves and disease-free survival curves were plotted according to the Kaplan Meier method measured from the day of surgery, and the generalised Wilcoxon test and the log-rank test were applied for comparison.
The differences are also confirmed by the generalised Hausman test.
Statistical significance between groups was determined by log-rank or generalised Wilcoxon test.
We used using a generalised Hausman test to compare the estimates from observational and genetic analyses described below.
For comparison of proportions, Chi-square or Fisher's generalised exact test with Monte Carlo simulation was used.
But, as expected, the generalised Hausman test shows more differences between types of households (especially for outpatient care where effects are statistically significant).
However, a generalised Hausman test rejects the hypothesis of equal coefficients between nuclear and extended households for the method to raise money.
The survival curves were estimated using the Kaplan-Meier method, and differences in survival distributions were evaluated by the generalised Wilcoxon test.
On the other hand, the generalised Hausman test indicates that methods to raise money for health care differ between types of households.
A similar framework, combining a variance component and generalised burden test as independent score statistics, using Fisher's or Tippett's procedures, has been implemented in the Mixed effects Score Test (MiST) [ 29].
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