pkonfound: Perform sensitivity analysis for published studies

In konfound: Quantify the Robustness of Causal Inferences

Description

For published studies, this command calculates (1) how much bias there must be in an estimate to invalidate/sustain an inference; (2) the impact of an omitted variable necessary to invalidate/sustain an inference for a regression coefficient.

Usage

pkonfound(
  est_eff,
  std_err,
  n_obs,
  n_covariates = 1,
  alpha = 0.05,
  tails = 2,
  index = "RIR",
  nu = 0,
  n_treat = NULL,
  switch_trm = TRUE,
  model_type = "ols",
  a = NULL,
  b = NULL,
  c = NULL,
  d = NULL,
  two_by_two_table = NULL,
  test = "fisher",
  replace = "control",
  to_return = "print"
)

Arguments

est_eff	the estimated effect (such as an unstandardized beta coefficient or a group mean difference)
std_err	the standard error of the estimate of the unstandardized regression coefficient
n_obs	the number of observations in the sample
n_covariates	the number of covariates in the regression model
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)
index	whether output is RIR or IT (impact threshold); defaults to "RIR"
nu	what hypothesis to be tested; defaults to testing whether est_eff is significantly different from 0
n_treat	the number of cases associated with the treatment condition; applicable only when model_type = "logistic"
switch_trm	whether to switch the treatment and control cases; defaults to FALSE; applicable only when model_type = "logistic"
model_type	the type of model being estimated; defaults to "ols" for a linear regression model; the other option is "logistic"
a	cell is the number of cases in the control group showing unsuccessful results
b	cell is the number of cases in the control group showing successful results
c	cell is the number of cases in the treatment group showing unsuccessful results
d	cell is the number of cases in the treatment group showing successful results
two_by_two_table	table that is a matrix or can be coerced to one (data.frame, tibble, tribble) from which the a, b, c, and d arguments can be extracted
test	whether using Fisher's Exact Test or A chi-square test; defaults to Fisher's Exact Test
replace	whether using entire sample or the control group to calculate the base rate; default is the entire sample
to_return	whether to return a data.frame (by specifying this argument to equal "raw_output" for use in other analyses) or a plot ("plot"); default is to print ("print") the output to the console; can specify a vector of output to return

Value

prints the bias and the number of cases that would have to be replaced with cases for which there is no effect to invalidate the inference

Examples

# using pkonfound for linear models
pkonfound(2, .4, 100, 3)
pkonfound(-2.2, .65, 200, 3)
pkonfound(.5, 3, 200, 3)
pkonfound(-0.2, 0.103, 20888, 3, n_treat = 17888, model_type = "logistic")

pkonfound(2, .4, 100, 3, to_return = "thresh_plot")
pkonfound(2, .4, 100, 3, to_return = "corr_plot")

pkonfound_output <- pkonfound(2, .4, 200, 3,
  to_return = c("raw_output", "thresh_plot", "corr_plot")
)
summary(pkonfound_output)
pkonfound_output$raw_output
pkonfound_output$thresh_plot
pkonfound_output$corr_plot

# using pkonfound for a 2x2 table
pkonfound(a = 35, b = 17, c = 17, d = 38)
pkonfound(a = 35, b = 17, c = 17, d = 38, alpha = 0.01)
pkonfound(a = 35, b = 17, c = 17, d = 38, alpha = 0.01, switch_trm = FALSE)
pkonfound(a = 35, b = 17, c = 17, d = 38, test = "chisq")

konfound documentation built on June 1, 2021, 9:08 a.m.