calc_risk_diff_iptw: Calculate Standardized Risk Differences Using IPTW

In riskdiff: Risk Difference Estimation with Multiple Link Functions and Inverse Probability of Treatment Weighting

Calculate Standardized Risk Differences Using IPTW

Description

Calculates standardized risk differences using inverse probability of treatment weighting. This approach estimates causal effects under the assumption of no unmeasured confounding by creating a pseudo-population where treatment assignment is independent of measured confounders.

Usage

calc_risk_diff_iptw(
  data,
  outcome,
  treatment,
  covariates,
  iptw_weights = NULL,
  weight_type = "ATE",
  ps_method = "logistic",
  stabilize = TRUE,
  trim_weights = TRUE,
  alpha = 0.05,
  bootstrap_ci = FALSE,
  boot_n = 1000,
  verbose = FALSE
)

Arguments

data	A data frame containing outcome, treatment, and covariate data
outcome	Character string naming the binary outcome variable
treatment	Character string naming the binary treatment variable
covariates	Character vector of covariate names for propensity score model
iptw_weights	Optional vector of pre-calculated IPTW weights
weight_type	Type of weights if calculating: "ATE", "ATT", or "ATC" (default: "ATE")
ps_method	Method for propensity score estimation (default: "logistic")
stabilize	Whether to use stabilized weights (default: TRUE)
trim_weights	Whether to trim extreme weights (default: TRUE)
alpha	Significance level for confidence intervals (default: 0.05)
bootstrap_ci	Whether to use bootstrap confidence intervals (default: FALSE)
boot_n	Number of bootstrap replicates if bootstrap_ci=TRUE (default: 1000)
verbose	Whether to print diagnostic information (default: FALSE)

Details

Causal Interpretation

IPTW estimates causal effects by weighting observations to create balance on measured confounders. The estimand depends on the weight type:

• ATE: Average treatment effect in the population

• ATT: Average treatment effect among those who received treatment

• ATC: Average treatment effect among those who did not receive treatment

Standard Errors

By default, uses robust (sandwich) standard errors that account for propensity score estimation uncertainty. Bootstrap confidence intervals are available as an alternative that may perform better with small samples.

Assumptions

1. No unmeasured confounding: All confounders are measured and included

2. Positivity: All subjects have non-zero probability of receiving either treatment

3. Correct model specification: Propensity score model is correctly specified

Value

A tibble of class "riskdiff_iptw_result" containing:

treatment_var

Character. Name of treatment variable

rd_iptw

Numeric. IPTW-standardized risk difference

ci_lower

Numeric. Lower confidence interval bound

ci_upper

Numeric. Upper confidence interval bound

p_value

Numeric. P-value for test of null hypothesis

weight_type

Character. Type of weights used

effective_n

Numeric. Effective sample size

risk_treated

Numeric. Risk in treated group

risk_control

Numeric. Risk in control group

Examples

data(cachar_sample)

# Standard ATE estimation
rd_iptw <- calc_risk_diff_iptw(
  data = cachar_sample,
  outcome = "abnormal_screen",
  treatment = "areca_nut",
  covariates = c("age", "sex", "residence", "smoking")
)
print(rd_iptw)

# ATT estimation with bootstrap CI
rd_att <- calc_risk_diff_iptw(
  data = cachar_sample,
  outcome = "head_neck_abnormal",
  treatment = "tobacco_chewing",
  covariates = c("age", "sex", "residence", "areca_nut"),
  weight_type = "ATT",
  bootstrap_ci = TRUE,
  boot_n = 500
)
print(rd_att)

riskdiff documentation built on June 30, 2025, 9:07 a.m.