Special Regression Models

gtregression 1.2.0 adds focused workflows for common study designs that need more than ordinary linear or logistic regression. Choose a model from the outcome structure and study design, not from the effect measure you would prefer to report.

Choose the right model

Research setting	Function	Main reported effect
Individually matched case-control study	`conditional_logit_reg()`	Matched odds ratio
Patients grouped within one clustering variable	`multilevel_reg()`	Adjusted beta or odds ratio
Ordered outcome with three or more levels	`ordinal_reg()`	Common odds ratio
Unordered outcome with three or more levels	`multinomial_reg()`	Outcome-specific odds ratios
Time-to-event outcome with competing events	`competing_risk_reg()`	Subdistribution hazard ratio

These functions return familiar gtregression objects. Supported results can be modified, merged, plotted, converted to forest data, and exported using the same downstream workflow as the core regression functions.

Matched case-control studies

Use conditional_logit_reg() when cases and controls were matched and each row contains a matched-set identifier. The matched-set variable defines the risk sets; it is not estimated as an ordinary predictor.

matched_result <- conditional_logit_reg(
  matched_data,
  outcome = case,
  exposures = exposure,
  matched_set = matched_set,
  adjust_for = c(age, smoking),
  model_stats = TRUE
)

matched_result
matched_result$model_stats

Do not use ordinary logistic regression merely because the outcome is binary: that would ignore the matching used in the study design.

Multilevel models

Use multilevel_reg() when observations are clustered within one grouping variable, such as patients within clinics. Version 1.2.0 intentionally supports a random-intercept model with one grouping variable.

clinic_result <- multilevel_reg(
  clinic_data,
  outcome = improved,
  exposures = treatment,
  group = clinic,
  adjust_for = c(age, sex),
  approach = "logit",
  model_stats = TRUE
)

clinic_result
clinic_result$model_stats

The table reports fixed effects. The grouping variable is named in the note but does not receive a coefficient. Inspect the cluster count, random-intercept variance, ICC, convergence information, and singular-fit guidance.

Ordered outcomes

Use ordinal_reg() only when the outcome levels have a meaningful order. The factor level order defines the direction of the effect.

recovery_data$recovery <- ordered(
  recovery_data$recovery,
  levels = c("Poor", "Fair", "Good", "Excellent")
)

recovery_result <- ordinal_reg(
  recovery_data,
  outcome = recovery,
  exposures = c(treatment, age),
  adjust_for = sex,
  model_stats = TRUE
)

recovery_result
recovery_result$assumption_tests

An odds ratio above one indicates greater odds of being in a higher outcome category, subject to the proportional-odds assumption. Confirm the displayed outcome order and inspect the term-level assumption checks.

Unordered outcomes

Use multinomial_reg() when the outcome categories have no defensible order. The first factor level is the reference outcome unless it is explicitly relevelled.

destination_data$destination <- stats::relevel(
  destination_data$destination,
  ref = "Home"
)

destination_result <- multinomial_reg(
  destination_data,
  outcome = destination,
  exposures = c(treatment, age),
  adjust_for = frailty,
  model_stats = TRUE
)

destination_result
names(destination_result$per_outcome)
plot_reg(destination_result$per_outcome[["Rehabilitation"]])

The publication table shows one comparison for each non-reference outcome. Use $per_outcome when a plot or forest table should focus on one comparison.

Competing risks

Cumulative incidence estimates absolute event probability while accounting for competing events. Fine-Gray regression models the subdistribution hazard. These answer related but different questions.

cuminc_plot(
  data_competing_risk,
  time = follow_up_months,
  event = outcome,
  event_of_interest = Relapse,
  censor_code = Censored,
  by = treatment
)

fine_gray_result <- competing_risk_reg(
  data_competing_risk,
  time = follow_up_months,
  event = outcome,
  event_of_interest = Relapse,
  censor_code = Censored,
  exposures = treatment,
  adjust_for = c(age, stage),
  model_stats = TRUE
)

fine_gray_result

Do not interpret a subdistribution hazard ratio as a risk ratio. A cause-specific Cox model, Fine-Gray model, cumulative-incidence curve, and Kaplan-Meier curve are not interchangeable.

Continue the workflow

modify_table(fine_gray_result, caption = "Relapse analysis")
plot_reg(fine_gray_result)
forest_reg(forest_df(fine_gray_result))
save_table(fine_gray_result)

For full reproducible checks, see the scripts in dev/manual-tests/ in the source repository. The app exposes these workflows under Special Models and Competing Risks, while keeping core regression choices uncluttered.