Reporting CIF Regression with gtsummary

An event-specific regression table

Fit the direct CIF regression, then pass the result to tbl_regression(). The example uses the anonymized diabetes cohort from the overview. Effects compare low fruit intake with the reference group at 8 years; age and sex enter the nuisance model. See the direct regression vignette for model assumptions and estimation details.

library(cifmodeling)
library(gtsummary)
data(diabetes.complications)
report_data <- diabetes.complications
report_data$fruitq1 <- factor(report_data$fruitq1, levels = c(0, 1),
                            labels = c("Other intake", "Low intake"))
attr(report_data$fruitq1, "label") <- "Fruit intake"
fit <- polyreg(Event(t, epsilon) ~ age + sex, exposure = "fruitq1",
  code.exposure.ref = "Other intake", data = report_data, time.point = 8,
  effect.measure1 = "RR", effect.measure2 = "RR")

retinopathy_table <- tbl_regression(fit, exponentiate = TRUE)
retinopathy_table

Characteristic	RR¹	95% CI	p-value
Fruit intake
Other intake	—	—
Low intake	1.42	1.17, 1.73	<0.001
¹ RR for event 1 at time 8
Abbreviation: CI = Confidence Interval

The default event is event 1, diabetic retinopathy. The table reports exponentiated effect estimates and their saved confidence limits. Standard errors and test statistics remain on the coefficient’s log scale. The reference row describes the contrast; it is not an additional estimated coefficient. These observational estimates do not by themselves establish a causal effect of fruit intake.

Reporting the competing event

Select event 2 explicitly to report macrovascular complications. The numeric results, exposure labels and effect-measure heading all follow the selected event.

macrovascular_table <- tbl_regression(fit, exponentiate = TRUE, event = "event2")
tbl_merge(list(retinopathy_table, macrovascular_table),
  tab_spanner = c("**Retinopathy**", "**Macrovascular complications**"))

Characteristic	Retinopathy			Macrovascular complications
Characteristic	RR¹	95% CI	p-value	RR²	95% CI	p-value
Fruit intake
Other intake	—	—		—	—
Low intake	1.42	1.17, 1.73	<0.001	1.03	0.61, 1.73	>0.9
¹ RR for event 1 at time 8
² RR for event 2 at time 8
Abbreviation: CI = Confidence Interval

For a tidy data frame containing both events, use generics::tidy(fit, event = "both"). Publication regression tables select one event at a time and are combined with tbl_merge().

RR, OR and SHR

Choose the effect measure during estimation, not while formatting the table. RR compares cumulative incidence probabilities, OR compares their odds, and SHR is the subdistribution hazard ratio parameterization. At a fixed time, the latter is the ratio of -log(1 - CIF) values; it is not a cause-specific hazard ratio. The following tables use the same exposure comparison and 8-year target, with three different effect-measure models.

fit_or <- polyreg(Event(t, epsilon) ~ age + sex, exposure = "fruitq1",
  code.exposure.ref = "Other intake", data = report_data, time.point = 8,
  effect.measure1 = "OR", effect.measure2 = "OR")
fit_shr <- polyreg(Event(t, epsilon) ~ age + sex, exposure = "fruitq1",
  code.exposure.ref = "Other intake", data = report_data, time.point = 8,
  effect.measure1 = "SHR", effect.measure2 = "SHR")
tbl_merge(list(tbl_regression(fit, exponentiate = TRUE),
               tbl_regression(fit_or, exponentiate = TRUE),
               tbl_regression(fit_shr, exponentiate = TRUE)),
  tab_spanner = c("**RR model**", "**OR model**", "**SHR model**"))

Characteristic	RR model			OR model			SHR model
Characteristic	RR¹	95% CI	p-value	OR²	95% CI	p-value	SHR³	95% CI	p-value
Fruit intake
Other intake	—	—		—	—		—	—
Low intake	1.42	1.17, 1.73	<0.001	1.66	1.23, 2.24	<0.001	1.53	1.20, 1.95	<0.001
¹ RR for event 1 at time 8
² OR for event 1 at time 8
³ SHR for event 1 at time 8
Abbreviation: CI = Confidence Interval

The two events can also use different effect measures. Headings are read from the fitted estimand information; no effect measure is inferred from the size of a coefficient.

fit_mixed <- polyreg(Event(t, epsilon) ~ age + sex, exposure = "fruitq1",
  code.exposure.ref = "Other intake", data = report_data, time.point = 8,
  effect.measure1 = "RR", effect.measure2 = "SHR")
tbl_merge(list(tbl_regression(fit_mixed, exponentiate = TRUE),
               tbl_regression(fit_mixed, exponentiate = TRUE, event = "event2")),
  tab_spanner = c("**Retinopathy**", "**Macrovascular complications**"))

Characteristic	Retinopathy			Macrovascular complications
Characteristic	RR¹	95% CI	p-value	SHR²	95% CI	p-value
Fruit intake
Other intake	—	—		—	—
Low intake	1.42	1.17, 1.73	<0.001	1.03	0.60, 1.77	>0.9
¹ RR for event 1 at time 8
² SHR for event 2 at time 8
Abbreviation: CI = Confidence Interval

Confidence intervals and labels

With unchanged inference settings, the table extracts the fitted intervals. This also applies to stored bootstrap intervals: they are not replaced by Wald limits during exponentiation. To retain the fitted confidence level explicitly, pass conf.level = fit$conf.int. A request for a different level uses the existing supported reconstruction, or fails if the saved method cannot provide that level without refitting, as with BCa limits.

tbl_regression(fit, exponentiate = TRUE, conf.level = fit$conf.int,
               label = list(fruitq1 ~ "Low fruit intake versus other intake"))

Characteristic	RR¹	95% CI	p-value
Low fruit intake versus other intake
Other intake	—	—
Low intake	1.42	1.17, 1.73	<0.001
¹ RR for event 1 at time 8
Abbreviation: CI = Confidence Interval

For direct extraction, generics::tidy(fit) retains log-scale estimates. Use exponentiate = TRUE for ratios. The broom convention uses logical conf.int and numeric conf.level; the older numeric conf.int argument remains accepted by tidy.polyreg() for compatibility. There is no need to modify the saved fit or supply a custom tidier to produce these tables.

Exporting a regression table

Use the standard gtsummary conversions. Replace the temporary filenames with the intended destination when saving the final report.

html_file <- tempfile(fileext = ".html")
gt::gtsave(as_gt(retinopathy_table), filename = html_file)

word_file <- tempfile(fileext = ".docx")
flextable::save_as_docx(as_flex_table(retinopathy_table), path = word_file)