Spss statistical software package

A total of 150 cases of IDC of breast diagnosed on FNAC, who underwent modified radical mastectomy, comprised the study group. FNAC slides of the primary tumor and formalin-fixed, paraffin-embedded blocks of regional lymph nodes were retrieved from case files of the Department of Pathology of a large, referral teaching hospital.
Cytological evaluation of all primary tumors was done using Robinson's system [Table 1].[7 (link)] The scores of each of the six features were added to arrive at a total score. Grade 1 was assigned to total scores ranging from 6 to 11, grade 2 to a total score ranging from 12 to 14, and grade 3 to a total score ranging from 15 to 18. Assessment of the nuclear size of tumor cells was done by comparing with adjacent red blood cells on the smear.
Hematoxylin-and-eosin-stained slides of axillary lymph nodes were analyzed for the presence of metastasis. Statistical analysis was done by SPSS statistical software package, version 14.0. The overall cytological grade and each feature of cytological grade were correlated with axillary lymph node status using χ² test; P < 0.05 was considered significant.

The SPSS statistical software package (version 20.0) was used for statistical analysis. Measurement data were processed using a one-way ANOVA for the groups and least significant difference for the pairwise comparisons. P<0.05 was considered statistically significant.

To determine the differences between CM users and non-users, chi-square tests were used for categorical variables (gender, pre-injury education, living arrangements, and funding source), and independent sample t-tests were used for continuous variables (age, severity of injury, activity limitations, and community integration). Predictor variables were then examined for collinearity. Relational concerns were addressed using Pearson correlation coefficients for continuous variables and Spearman’s rho coefficients for categorical variables. Most correlations were modest, with the highest correlation between the ALI and CIQ(r = 0.43).
Sequential (hierarchical) logistic regression was used to create a model predicting CM use. The order in which the groups of variables – predisposing factors first, need factors second, and enablement factors last – were entered into the model was derived from Andersen and Davidson’s (2001) BMHSU. They suggest that need, as opposed to available resources, will explain most of the variance in health service use. Wald tests were used to determine the statistical significance of each regression coefficient in the model. Data were entered into the SPSS statistical software package, version 11.0 which solves for the group coded 1 (i.e., CM users).