p_dfa_xerrors function

Discriminant Function Approach for Estimating Odds Ratio with Normal Exposure Measured in Pools and Potentially Subject to Errors

Archived on 7/23/18. Please use p_ndfa instead.

p_dfa_xerrors(g, y, xtilde, c = NULL, constant_or = TRUE,
  errors = "both", ...)

Arguments

• g: Numeric vector of pool sizes, i.e. number of members in each pool.
• y: Numeric vector of poolwise Y values (number of cases in each pool).
• xtilde: Numeric vector (or list of numeric vectors, if some pools have replicates) with Xtilde values.
• c: Numeric matrix with poolwise ‘C’ values (if any), with one row for each pool. Can be a vector if there is only 1 covariate.
• constant_or: Logical value for whether to assume a constant OR for X, which means that sigsq_1 = sigsq_0. If NULL, model is fit with and without this assumption, and likelihood ratio test is performed to test it.
• errors: Character string specifying the errors that X is subject to. Choices are "neither", "processing" for processing error only, "measurement" for measurement error only, and "both".
• ...: Additional arguments to pass to nlminb.

Returns

List of point estimates, variance-covariance matrix, object returned by nlminb, and AIC, for one or two models depending on constant_or. If constant_or = NULL, also returns result of a likelihood ratio test for H0: sigsq_1 = sigsq_0, which is equivalent to H0: log-OR is constant. If constant_or = NULL, returned objects with names ending in 1 are for model that does not assume constant log-OR, and those ending in 2 are for model that assumes constant log-OR.

Examples

# Load dataset containing poolwise (Y, Xtilde, C) values for pools of size
# 1, 2, and 3. Xtilde values are affected by processing error.
data(pdat1)

# Estimate log-OR for X and Y adjusted for C, ignoring processing error
fit1 <- p_dfa_xerrors(g = pdat1$g, y = pdat1$numcases, xtilde = pdat1$xtilde,
                      c = pdat1$c, errors = "neither")
fit1$estimates

# Repeat, but accounting for processing error. Closer to true log-OR of 0.5.
fit2 <- p_dfa_xerrors(g = pdat1$g, y = pdat1$numcases, xtilde = pdat1$xtilde,
                      c = pdat1$c, errors = "processing")
fit2$estimates

References

Lyles, R.H., Van Domelen, D.R., Mitchell, E.M. and Schisterman, E.F. (2015) "A discriminant function approach to adjust for processing and measurement error When a biomarker is assayed in pooled samples." Int. J. Environ. Res. Public Health 12 (11): 14723--14740.

Schisterman, E.F., Vexler, A., Mumford, S.L. and Perkins, N.J. (2010) "Hybrid pooled-unpooled design for cost-efficient measurement of biomarkers." Stat. Med. 29 (5): 597--613.