Stratified Analysis

Stratified regression repeats the analysis inside each subgroup and places the results side by side. It is useful when the same association may look different across groups.

library(gtregression)
library(dplyr)

data("data_birthwt", package = "gtregression")

birthwt_data <- data_birthwt |>
  mutate(
    race = factor(race, levels = c(1, 2, 3),
                  labels = c("White", "Black", "Other")),
    smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
    ht = factor(ht, levels = c(0, 1), labels = c("No", "Yes")),
    ui = factor(ui, levels = c(0, 1), labels = c("No", "Yes")),
    low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW")),
    ptl_cat = factor(ifelse(ptl > 0, "Yes", "No"), levels = c("No", "Yes"))
  )

Univariable by Stratum

strata_uni <- stratified_uni_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("age", "lwt", "smoke", "ht", "ui", "ptl_cat"),
  stratifier = "race",
  approach = "logit",
  theme = clinical
)

strata_uni$table

	race = White			race = Black			race = Other
Characteristic	N	OR (95% CI)	p-value	N	OR (95% CI)	p-value	N	OR (95% CI)	p-value
age	96	0.95 (0.86–1.04)	0.226	26	1.05 (0.90–1.23)	0.526	67	0.94 (0.84–1.05)	0.297
lwt	96	0.98 (0.97–1.00)	0.123	26	0.99 (0.97–1.01)	0.517	67	0.97 (0.95–1.00)	0.056
smoke	96			26			67
No		—			—			—
Yes		5.76 (1.78–18.60)	0.003		3.30 (0.63–17.16)	0.156		1.25 (0.35–4.46)	0.731
ht	96			26			67
No		—			—			—
Yes		2.22 (0.35–14.20)	0.399		3.11 (0.24–39.54)	0.382		5.59 (0.55–56.99)	0.146
ui	96			26			67
No		—			—			—
Yes		2.26 (0.66–7.75)	0.196		3.11 (0.24–39.54)	0.382		2.88 (0.80–10.33)	0.105
ptl_cat	96			26			67
No		—			—			—
Yes		5.96 (1.80–19.72)	0.003		1.44 (0.17–12.23)	0.736		4.47 (1.18–16.90)	0.027
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.

Adjusted by Stratum

Use adjust_for when each exposure should be adjusted for the same variables within each stratum.

strata_multi <- stratified_multi_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat"),
  stratifier = "race",
  adjust_for = c("age", "lwt"),
  approach = "logit",
  theme = striped
)

strata_multi$table

	race = White		race = Black		race = Other
Characteristic	Adjusted OR (95% CI)	p-value	Adjusted OR (95% CI)	p-value	Adjusted OR (95% CI)	p-value
smoke
No	—		—		—
Yes	4.97 (1.47–16.80)	0.010	2.96 (0.48–18.32)	0.243	1.23 (0.32–4.80)	0.762
ht
No	—		—		—
Yes	3.72 (0.45–30.67)	0.222	5.71 (0.27–121.78)	0.264	7.93 (0.66–95.10)	0.102
ui
No	—		—		—
Yes	1.59 (0.43–5.96)	0.488	4.49 (0.28–72.28)	0.289	2.68 (0.72–10.05)	0.143
ptl_cat
No	—		—		—
Yes	6.26 (1.77–22.16)	0.004	0.96 (0.09–9.85)	0.973	5.55 (1.31–23.56)	0.020
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
Adjusted for age and lwt
Complete observations included by race stratum: White: N = 96; Black: N = 26; Other: N = 67

What To Inspect

$table: rendered side-by-side table.
$table_display: wide data used to build the table.
$models: fitted models by stratum.
$reg_check: diagnostics for linear models.

- Stratified Analysis