final_prog_svyglm: Prognostic-weighted survey GLM Prognostic-weighted survey GLM

In svyCausalGLM: Survey-Weighted Modeling Utilities

Prognostic-weighted survey GLM Prognostic-weighted survey GLM

Description

Fits a survey-weighted logistic regression model using stabilized prognostic score weights derived from a model predicting the outcome conditional on baseline covariates and excluding the exposure effect. The function supports design-based inference under complex survey sampling while adjusting for confounding through prognostic weighting

Usage

final_prog_svyglm(
  data,
  dep_var,
  exposure,
  covariates,
  id_var,
  strata_var,
  weight_var,
  outcome_covariates = NULL,
  level = 0.95,
  ...
)

Arguments

data	Data frame
dep_var	Character; binary outcome
exposure	Character; exposure variable
covariates	Character vector; adjustment variables
id_var	Character; PSU
strata_var	Character; strata
weight_var	Character; survey weight
outcome_covariates	Character vector; optional covariates for final model
level	Numeric; CI level
...	Additional args to svyglm

Value

A list with:

• prog_model: Prognostic svyglm.

• final_model: Weighted outcome svyglm.

• OR_table: Odds ratios with CI.

• AUC: Weighted AUC.

• data: Data with prognostic weights.

Examples

set.seed(123)
n <- 1000
dat <- data.frame(
  psu = sample(1:10, n, replace = TRUE),
  strata = sample(1:5, n, replace = TRUE),
  weight = runif(n, 0.5, 2),
  age = rnorm(n, 50, 10),
  sex = factor(sample(c("Male", "Female"), n, replace = TRUE)),
  exposure = rbinom(n, 1, 0.5)
)
dat$outcome <- rbinom(n, 1, plogis(-2 + 0.03*dat$age + 0.5*dat$exposure))
fit<-final_prog_svyglm(data = dat,
 dep_var = "outcome",
 exposure="exposure",
 covariates = c("age", "sex"),
 id_var = "psu",
 strata_var = "strata",
 weight_var = "weight",
 level = 0.95
)
names(fit)
fit$OR_table

svyCausalGLM documentation built on March 3, 2026, 5:08 p.m.