6 Combining univariate and multivariable models

To display both models in a single table run the rm_uvsum and rm_mvsum functions with tableOnly=TRUE and combine.

uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE)

glm_fit <- glm(orr~change_ctdna_group+pdl1,
               family='binomial',
               data = pembrolizumab)
mvsumTable <- rm_mvsum(glm_fit, showN = TRUE,tableOnly = TRUE)

rm_uv_mv(uvsumTable,mvsumTable)

	Unadjusted OR(95%CI)	p	Adjusted OR(95%CI)	p (adj)
age	0.96 (0.91, 1.00)	0.089
sex		0.11
Female	Reference
Male	0.41 (0.13, 1.22)
pdl1	0.97 (0.95, 0.98)	<0.001	0.98 (0.96, 1.00)	0.024
change ctdna group		0.002		0.004
Decrease from baseline	Reference		Reference
Increase from baseline	28.74 (5.20, 540.18)		24.71 (2.87, 212.70)

Note: This can also be done with adjusted p-values, but when combined the raw p-values are dropped.

uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE,p.adjust='holm')

glm_fit <- glm(orr~change_ctdna_group+pdl1,
               family='binomial',
               data = pembrolizumab)
mvsumTable <- rm_mvsum(glm_fit,tableOnly = TRUE,p.adjust='holm')

rm_uv_mv(uvsumTable,mvsumTable)

	Unadjusted OR(95%CI)	p	Adjusted OR(95%CI)	p (adj)
age	0.96 (0.91, 1.00)	0.18
sex		0.18
Female	Reference
Male	0.41 (0.13, 1.22)
pdl1	0.97 (0.95, 0.98)	<0.001	0.98 (0.96, 1.00)	0.024
change ctdna group		0.005		0.007
Decrease from baseline	Reference		Reference
Increase from baseline	28.74 (5.20, 540.18)		24.71 (2.87, 212.70)