misclassification: Sensitivity analysis for disease or exposure...

In episensr: Basic Sensitivity Analysis of Epidemiological Results

Sensitivity analysis for disease or exposure misclassification.

Description

Simple sensitivity analysis for disease or exposure misclassification. Confidence interval for odds ratio adjusted using sensitivity and specificity is computed as in Chu et al. (2006), for exposure misclassification.

Usage

misclassification(
  case,
  exposed,
  type = c("exposure", "exposure_pv", "outcome"),
  bias_parms = NULL,
  alpha = 0.05
)

Arguments

case	Outcome variable. If a variable, this variable is tabulated against.
exposed	Exposure variable.
type	Choice of misclassification: exposure: bias analysis for exposure misclassification; corrections using sensitivity and specificity: nondifferential and independent errors, exposure_pv: bias analysis for exposure misclassification; corrections using PPV/NPV: nondifferential and independent errors, outcome: bias analysis for outcome misclassification.
bias_parms	Vector defining the bias parameters. This vector has 4 elements between 0 and 1, in the following order: Sensitivity of exposure (when type = "exposure") or outcome (when type = "outcome") classification among those with the outcome (when type = "exposure") or exposure (when type = "outcome"), Sensitivity of exposure (or outcome) classification among those without the outcome (or exposure), Specificity of exposure (or outcome) classification among those with the outcome (or exposure), and Specificity of exposure (or outcome) classification among those without the outcome (or exposure). If PPV/NPV is chosen in case of exposure misclassification, this vector is the following: Positive predictive value among those with the outcome, Positive predictive value among those without the outcome, Negative predictive value among those with the outcome, Negative predictive value among those without the outcome.
alpha	Significance level.

Details

For exposure misclassification, bias-adjusted measures are available using sensitivity and specificity, or using predictive values.

Value

A list with elements:

obs.data	The analyzed 2 x 2 table from the observed data.
corr.data	The expected observed data given the true data assuming misclassification.
obs.measures	A table of observed relative risk and odds ratio with confidence intervals.
adj.measures	A table of adjusted relative risk and odds ratio with confidence interval for odds ratio.
bias.parms	Input bias parameters.

References

Lash, T.L., Fox, M.P, Fink, A.K., 2009 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.79–108, Springer.

Chu, H., Zhaojie, W., Cole, S.R., Greenland, S., Sensitivity analysis of misclassification: A graphical and a Bayesian approach, Annals of Epidemiology 2006;16:834-841.

Examples

# The data for this example come from:
# Fink, A.K., Lash,  T.L. A null association between smoking during pregnancy
# and breast cancer using Massachusetts registry data (United States).
# Cancer Causes Control 2003;14:497-503.
misclassification(matrix(c(215, 1449, 668, 4296),
dimnames = list(c("Breast cancer+", "Breast cancer-"),
c("Smoker+", "Smoker-")),
nrow = 2, byrow = TRUE),
type = "exposure",
bias_parms = c(.78, .78, .99, .99))

misclassification(matrix(c(4558, 3428, 46305, 46085),
dimnames = list(c("AMI death+", "AMI death-"),
c("Male+", "Male-")),
nrow = 2, byrow = TRUE),
type = "outcome",
bias_parms = c(.53, .53, .99, .99))

# The following example comes from Chu et al. Sensitivity analysis of
# misclassification: A graphical and a Bayesian approach.
# Annals of Epidemiology 2006;16:834-841.
misclassification(matrix(c(126, 92, 71, 224),
dimnames = list(c("Case", "Control"), c("Smoker +", "Smoker -")),
nrow = 2, byrow = TRUE),
type = "exposure",
bias_parms = c(.94, .94, .97, .97))

# The next example, using PPV/NPV, comes from Bodnar et al. Validity of birth
# certificate-derived maternal weight data.
# Paediatric and Perinatal Epidemiology 2014;28:203-212.
misclassification(matrix(c(599, 4978, 31175, 391851),
dimnames = list(c("Preterm", "Term"), c("Underweight", "Normal weight")),
nrow = 2, byrow = TRUE),
type = "exposure_pv",
bias_parms = c(0.65, 0.74, 1, 0.98))

episensr documentation built on Aug. 30, 2023, 5:09 p.m.