pkonfound {konfound}

Perform sensitivity analysis for published studies

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",
  sdx,
  sdy,
  R2,
  eff_thr = 0,
  FR2max,
  FR2max_multiplier = 1.3,
  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 control group

sdx

the standard deviation of X

sdy

the standard deviation of Y

R2

the unadjusted,original R2 in the observed function

eff_thr

unstandardized coefficient threshold to change an inference

FR2max

the largest R2, or R2max, in the final model with unobserved confounder

FR2max_multiplier

the multiplier of R2 to get R2max, default is set to 1.3

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")

# 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")

# use pkonfound to calculate delta* and delta_exact 
pkonfound(est_eff = .4, std_err = .1, n_obs = 290, sdx = 2, sdy = 6, R2 = .7,
 eff_thr = 0, FR2max = .8, index = "COP", to_return = "raw_output")
# use pkonfound to calculate rxcv and rycv when preserving standard error
pkonfound(est_eff = .5, std_err = .056, n_obs = 6174, eff_thr = .1, 
sdx = 0.22, sdy = 1, R2 = .3, index = "PSE", to_return = "raw_output")