conmed_ind_ME: Perform sensitivity analysis for mediation analysis

In linqinyu/ConMed: Assess robustness of a causal mediation inference to the inclusion of an alternative mediator as a potential confounder

Perform sensitivity analysis for mediation analysis

Description

This command calculates the impact of an omitted variable necessary to invalidate/sustain an inference for a mediation effect, allowing for potential measurment error.

Usage

conmed_ind_ME(
  est_eff_a,
  std_err_a,
  est_eff_b,
  std_err_b,
  n_obs,
  n_covariates_a = 0,
  n_covariates_b = 1,
  rel_M = 1,
  rel_Y = 1,
  rel_X = 1,
  sd_X,
  sd_M,
  sd_Y,
  R2_a,
  R2_b,
  alpha = 0.05,
  tails = 2,
  nu = 0
)

Arguments

est_eff_a	the estimated effect for path a (from treatment to mediator)
std_err_a	the standard error of the estimate of path a (from treatment to mediator)
est_eff_b	the estimated effect for path b (from mediator to outcome)
std_err_b	the standard error of the estimate of path b (from mediator to outcome)
n_obs	the number of observations in the sample
n_covariates_a	the number of covariates in the regression model for estimating path a (from treatment to mediator), including the
n_covariates_b	the number of covariates in the regression model for estimating path b (from mediator to outcome)
rel_M	the reliability level of the mediator
rel_Y	the reliability level of the outcome
rel_X	the reliability level of the treatment
sd_X	the standard deviation of the treatment
sd_M	the standard deviation of the mediator
sd_Y	the standard deviation of the outcome
R2_a	the R square of the regression model that estiamtes the a pathway (from treatment to mediator)
R2_b	the R square of the regression model that estiamtes the b pathway (from mediator to outcome)
alpha	probability of rejecting the null hypothesis (defaults to 0.05)
tails	integer whether hypothesis testing is one-tailed (1) or two-tailed (2; defaults to 2)

Value

prints the ITCV to invalidate/sustain the inference & show the impact curve

Examples

conmed_ind_ME(est_eff_a = 0.171, std_err_a = 0.087, est_eff_b = 0.387, std_err_b = 0.077, n_obs = 123, rel_M = 0.9, rel_Y = 0.9, sd_X = 1, sd_M = 1.054093, sd_Y = 1.054093, R2_a = 0.02941117, R2_b = 0.1685979)

linqinyu/ConMed documentation built on March 20, 2023, 5:40 a.m.