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| 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) |