sensitivity: Estimate sensitivity

In SAMBA: Selection and Misclassification Bias Adjustment for Logistic Regression Models

Description

sensitivity estimates (1) marginal sensitivity and (2) sensitivity as a function of covariates X for a misclassified binary outcome.

Usage

sensitivity(Dstar, X, prev, r = NULL, weights = NULL)

Arguments

Dstar	Numeric vector containing observed disease status. Should be coded as 0/1
X	Numeric matrix with covariates in sensitivity model. Set to NULL to fit model with no covariates in sensitivity model. 'X' should not contain an intercept
prev	marginal disease prevalence P(D = 1) or patient-specific P(D = 1|X) in population
r	(optional) marginal sampling ratio, P(S = 1|D = 1) / P(S = 1|D = 0). Only one of 'r' and 'weights' can be specified. Default is 'NULL'
weights	Optional vector of patient-specific weights used for selection bias adjustment. Only one of r and weights can be specified. Default is 'NULL'

Details

We are interested in modeling the relationship between binary disease status and covariates Z using a logistic regression model. However, D may be misclassified, and our observed data may not well-represent the population of interest. In this setting, we estimate parameters from the disease model using the following modeling framework.

Notation:

D

Binary disease status of interest.

D*

Observed binary disease status. Potentially a misclassified version of D. We assume D = 0 implies D* = 0.

S

Indicator for whether patient from population of interest is included in the analytical dataset.

Z

Covariates in disease model of interest.

W

Covariates in model for patient inclusion in analytical dataset (selection model).

X

Covariates in model for probability of observing disease given patient has disease (sensitivity model).

Model Structure:

Disease Model

logit(P(D=1|X)) = theta_0 + theta_Z Z

Selection Model

P(S=1|W,D)

Sensitivity Model

logit(P(D* = 1| D = 1, S = 1, X)) = beta_0 + beta_X X

Value

a list with two elements: (1) 'c_marg', marginal sensitivity estimate P(D* = 1|D = 1, S = 1), and (2) 'c_X', sensitivity as a function of X P(D* = 1| D = 1, S = 1, X)

References

Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification Lauren J Beesley and Bhramar Mukherjee medRxiv 2019.12.26.19015859

Examples

library(SAMBA)
# These examples are generated from the vignette. See it for more details.

# Generate IPW weights from the true model
expit <- function(x) exp(x) / (1 + exp(x))
prob.WD <- expit(-0.6 + 1 * samba.df$D + 0.5 * samba.df$W)
weights <- nrow(samba.df) * (1  / prob.WD) / (sum(1 / prob.WD))

# Using marginal sampling ratio r ~ 2 and P(D=1)
sens <- sensitivity(samba.df$Dstar, samba.df$X, mean(samba.df$D),
                    r = 2)
# Using inverse probability of selection weights and P(D=1)
sens <- sensitivity(samba.df$Dstar, samba.df$X, prev = mean(samba.df$D),
                    weights = weights)

SAMBA documentation built on Feb. 20, 2020, 9:07 a.m.