13.4 Logistic regression table

After investigating the relationships between our explanatory variables, we will use logistic regression to include the outcome variable.

explanatory <- c( "differ.factor", "age", "sex.factor",
"extent.factor", "obstruct.factor",
"nodes")
dependent <- "mort_5yr"
table2 <- colon_s %>%
finalfit(dependent, explanatory,
dependent_label_prefix = "")
table2
TABLE 13.2: Exporting a regression results table.
Mortality 5 year Alive Died OR (univariable) OR (multivariable)
Differentiation Well 52 (56.5) 40 (43.5)
Moderate 382 (58.7) 269 (41.3) 0.92 (0.59-1.43, p=0.694) 0.62 (0.38-1.01, p=0.054)
Poor 63 (42.3) 86 (57.7) 1.77 (1.05-3.01, p=0.032) 1.00 (0.56-1.78, p=0.988)
Age (years) Mean (SD) 59.8 (11.4) 59.9 (12.5) 1.00 (0.99-1.01, p=0.986) 1.01 (1.00-1.02, p=0.098)
Sex Female 243 (55.6) 194 (44.4)
Male 268 (56.1) 210 (43.9) 0.98 (0.76-1.27, p=0.889) 0.97 (0.73-1.30, p=0.858)
Extent of spread Submucosa 16 (80.0) 4 (20.0)
Muscle 78 (75.7) 25 (24.3) 1.28 (0.42-4.79, p=0.681) 1.25 (0.36-5.87, p=0.742)
Serosa 401 (53.5) 349 (46.5) 3.48 (1.26-12.24, p=0.027) 3.03 (0.96-13.36, p=0.087)
Adjacent structures 16 (38.1) 26 (61.9) 6.50 (1.98-25.93, p=0.004) 6.80 (1.75-34.55, p=0.010)
Obstruction No 408 (56.7) 312 (43.3)
Yes 89 (51.1) 85 (48.9) 1.25 (0.90-1.74, p=0.189) 1.26 (0.88-1.82, p=0.206)
Lymph nodes involved Mean (SD) 2.7 (2.4) 4.9 (4.4) 1.24 (1.18-1.30, p<0.001) 1.24 (1.18-1.31, p<0.001)