checksharpSVbound: Check if the Smith and VanderWeele bound is sharp

In SelectionBias: Calculates Bounds for the Selection Bias for Binary Treatment and Outcome Variables

Check if the Smith and VanderWeele bound is sharp

Description

checksharpSVbound() returns a string that indicates if the SV bound is sharp.

Usage

checksharpSVbound(whichEst, sens = NULL, BF = NULL, pY1)

Arguments

whichEst	Input string. Defining the causal estimand of interest. Available options are as follows. (1) Risk ratio in the total population: "RR_tot", (2) Risk ratio in the subpopulation: "RR_sub", (3) Risk difference in the subpopulation: "RD_sub". Note that the SV bound for the risk difference in the total population is not sharp.
sens	Possible method to input bounding factors (BF). sens can be the output from sensitivityparametersM(), a data.frame with columns 'parameter' and 'value', or a name list with correct names (e.g. "BF_00", "BF_10", etc.). If not supplied, bounding factors can be entered manually as specified below.
BF	Input vector. Is c(BF_00, BF_10) for the total population and c(BF_0, BF_1) for the subpopulation. Must be equal to or above 1. Can be inserted directly or as output from sensitivityparametersM().
pY1	Input vector. The probabilities c(P(Y=1|T=1,I_S=1), P(Y=1|T=0,I_S=1)).

Value

A string stating if the SV bound is sharp or not.

References

Smith, Louisa H., and Tyler J. VanderWeele. "Bounding bias due to selection." Epidemiology (Cambridge, Mass.) 30.4 (2019): 509.

Zetterstrom S, Sjölander A, Waernbaum I. "Investigations of sharp bounds for causal effects under selection bias." Statistical Methods in Medical Research (2025).

Examples

# Example where the bounding factor are specified manually.
checksharpSVbound(whichEst = "RR_sub", BF = c(1.56, 2), pY1 = c(0.33, 0.1))

# Example specifying the bounding factors from sensitivityparametersM().
# Risk ratio in the total population. DGP from the zika example.
V = matrix(c(1, 0, 0.85, 0.15), ncol = 2)
U = matrix(c(1, 0, 0.5, 0.5), ncol = 2)
Tr = c(-6.2, 1.75)
Y = c(-5.2, 5.0, -1.0)
S = matrix(c(1.2, 2.2, 0.0, 0.5, 2.0, -2.75, -4.0, 0.0), ncol = 4)
probT1 = 0.286
probT0 = 0.004
senspar = sensitivityparametersM(whichEst = "RR_tot", whichBound = "SV",
 Vval = V,  Uval = U, Tcoef = Tr, Ycoef = Y, Scoef = S, Mmodel = "L",
 pY1_T1_S1 = probT1, pY1_T0_S1 = probT0)
 
checksharpSVbound(whichEst = "RR_tot", sens = senspar, pY1 = c(probT1, probT0))

SelectionBias documentation built on Nov. 5, 2025, 6:48 p.m.