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"))
  )

attr(birthwt_data$age, "label") <- "Maternal age"
attr(birthwt_data$lwt, "label") <- "Maternal weight"
attr(birthwt_data$smoke, "label") <- "Smoking during pregnancy"
attr(birthwt_data$ht, "label") <- "Hypertension"
attr(birthwt_data$ui, "label") <- "Uterine irritability"
attr(birthwt_data$ptl_cat, "label") <- "Previous preterm labour"

Describe by Stratum

Start with a descriptive table by the stratifying variable. This is the companion table for the stratified regression: it helps users see the size and clinical profile of each subgroup before fitting stratum-specific models.

strata_desc <- descriptive_table(
  data = birthwt_data,
  exposures = c("age", "lwt", "smoke", "ht", "ui", "ptl_cat"),
  by = race,
  percent = column,
  show_overall = last,
  theme = clinical
)

strata_desc$table

Characteristic	White, N=96	Black, N=26	Other, N=67	Overall, N=189
Maternal age	23.5 (20.0-29.0)	20.5 (17.2-24.0)	22.0 (19.0-25.0)	23.0 (19.0-26.0)
Maternal weight	129.5 (112.0-143.2)	129.0 (120.0-179.0)	119.0 (105.0-130.0)	121.0 (110.0-140.0)
Smoking during pregnancy
No	44 (45.8%)	16 (61.5%)	55 (82.1%)	115 (60.8%)
Yes	52 (54.2%)	10 (38.5%)	12 (17.9%)	74 (39.2%)
Hypertension
No	91 (94.8%)	23 (88.5%)	63 (94.0%)	177 (93.7%)
Yes	5 (5.2%)	3 (11.5%)	4 (6.0%)	12 (6.3%)
Uterine irritability
No	83 (86.5%)	23 (88.5%)	55 (82.1%)	161 (85.2%)
Yes	13 (13.5%)	3 (11.5%)	12 (17.9%)	28 (14.8%)
Previous preterm labour
No	82 (85.4%)	22 (84.6%)	55 (82.1%)	159 (84.1%)
Yes	14 (14.6%)	4 (15.4%)	12 (17.9%)	30 (15.9%)
Categorical variables shown as n (%); percentages are by column.
Continuous variables shown as Median (IQR).

Univariable by Stratum

stratified_uni_reg() fits one model per exposure inside each stratum. The result is a single wide table, with one spanner per 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

	White			Black			Other
Characteristic	N	OR (95% CI)	p-value	N	OR (95% CI)	p-value	N	OR (95% CI)	p-value
Maternal 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
Maternal weight	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
Smoking during pregnancy	96			26			67
No		Ref.			Ref.			Ref.
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
Hypertension	96			26			67
No		Ref.			Ref.			Ref.
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
Uterine irritability	96			26			67
No		Ref.			Ref.			Ref.
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
Previous preterm labour	96			26			67
No		Ref.			Ref.			Ref.
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
Stratified by: race.
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
Ref. = reference category.

Full Multivariable Model by Stratum

With adjust_for = NULL, stratified_multi_reg() fits one multivariable model inside each stratum using all supplied exposures.

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

strata_full$table

	White		Black		Other
Characteristic	Adjusted OR (95% CI)	p-value	Adjusted OR (95% CI)	p-value	Adjusted OR (95% CI)	p-value
Maternal age	0.97 (0.86–1.08)	0.548	0.87 (0.64–1.19)	0.391	0.93 (0.81–1.07)	0.305
Maternal weight	0.99 (0.97–1.01)	0.333	0.97 (0.94–1.01)	0.136	0.97 (0.94–1.00)	0.074
Smoking during pregnancy
No	Ref.		Ref.		Ref.
Yes	3.35 (0.94–12.02)	0.063	16.50 (0.91–298.21)	0.058	0.81 (0.17–3.85)	0.794
Hypertension
No	Ref.		Ref.		Ref.
Yes	3.43 (0.39–30.08)	0.265	85.06 (0.60–11,959.18)	0.078	6.71 (0.52–86.08)	0.143
Uterine irritability
No	Ref.		Ref.		Ref.
Yes	1.02 (0.22–4.73)	0.978	67.61 (1.42–3,225.31)	0.033	2.60 (0.65–10.43)	0.176
Previous preterm labour
No	Ref.		Ref.		Ref.
Yes	4.68 (1.17–18.72)	0.029	4.87 (0.11–208.59)	0.409	4.13 (0.91–18.77)	0.066
Stratified by: race.
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
Ref. = reference category.
Complete observations included by race stratum: White: N = 96; Black: N = 26; Other: N = 67

Exposure-Specific Adjusted Models by Stratum

Use adjust_for when each exposure should be adjusted for the same variables within each stratum. This mirrors multi_reg(adjust_for = ...), but repeats the same workflow separately inside 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

	White		Black		Other
Characteristic	Adjusted OR (95% CI)	p-value	Adjusted OR (95% CI)	p-value	Adjusted OR (95% CI)	p-value
Smoking during pregnancy
No	Ref.		Ref.		Ref.
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
Hypertension
No	Ref.		Ref.		Ref.
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
Uterine irritability
No	Ref.		Ref.		Ref.
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
Previous preterm labour
No	Ref.		Ref.		Ref.
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
Stratified by: race.
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
Ref. = reference category.
Adjusted for age and lwt
Complete observations included by race stratum: White: N = 96; Black: N = 96; Other: N = 96; White: N = 96; Black: N = 26; Other: N = 26; White: N = 26; Black: N = 26; Other: N = 67; White: N = 67; Black: N = 67; Other: N = 67

If a stratum cannot fit a model, the function skips that stratum with a warning and continues. This is intentional: sparse strata are common in real data, and one small subgroup should not erase the whole analysis.

Forest Plot by Stratum

forest_df() can also prepare one stratified regression object for forest_reg(). The variable rows are kept once and each stratum is placed in a side-by-side effect column, which is easier to compare than repeating the full variable list for every subgroup.

strata_forest_data <- forest_df(strata_multi)

forest_reg(
  strata_forest_data,
  ci_col_width = 18
)

What To Inspect

$table: rendered side-by-side table.
$table_display: wide data used to build the table.
$per_stratum: full per-stratum result objects.
$models: fitted models by stratum.
$model_summaries: summaries for the fitted models.
$variable_labels: display labels used in the wide table.
$reg_check: diagnostics for linear models.