p_logreg {pooling}

Poolwise Logistic Regression

Description

Fit homogeneous-pools logistic regression model described by Weinberg & Umbach (1999).

Usage

p_logreg(g, y, x, method = "glm", prev = NULL, samp_y1y0 = NULL,
  estimate_var = TRUE, start = 0.01, lower = -Inf, upper = Inf,
  nlminb_list = list(control = list(trace = 1, eval.max = 500, iter.max =
  500)), hessian_list = list(method.args = list(r = 4)))

Arguments

g

Numeric vector with pool sizes, i.e. number of members in each pool.

y

Numeric vector with poolwise Y values, coded 0 if all members are controls and 1 if all members are cases.

x

Numeric matrix with poolwise X values, with one row for each pool. Can be a vector if there is only 1 predictor.

method

Character string specifying method to use for estimation. Choices are "glm" for glm function and "ml" for maximum likelihood.

prev

Numeric value specifying disease prevalence, allowing for valid estimation of the intercept with case-control sampling. Can specify samp_y1y0 instead if sampling rates are known.

samp_y1y0

Numeric vector of length 2 specifying sampling probabilities for cases and controls, allowing for valid estimation of the intercept with case-control sampling. Can specify prev instead if it's easier.

estimate_var

Logical value for whether to return variance-covariance matrix for parameter estimates.

start

Numeric value specifying starting values for parameters. Only used if method = "ml".

lower

Numeric value specifying lower bounds for parameters. Only used if method = "ml".

upper

Numeric value specifying upper bounds for parameters. Only used if method = "ml".

nlminb_list

List of arguments to pass to nlminb for log-likelihood maximization.

hessian_list

List of arguments to pass to hessian for approximating the Hessian matrix. Only used if method = "ml" and estimate_var = TRUE.

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Fitted glm object (if method = "glm") or returned nlminb object (if method = "ml").

4. Akaike information criterion (AIC).

References

Weinberg, C.R. and Umbach, D.M. (1999) "Using pooled exposure assessment to improve efficiency in case-control studies." Biometrics 55: 718–726.

Weinberg, C.R. and Umbach, D.M. (2014) "Correction to 'Using pooled exposure assessment to improve efficiency in case-control studies' by Clarice R. Weinberg and David M. Umbach; 55, 718–726, September 1999." Biometrics 70: 1061.

Examples

# Load dataset containing (Y, Xtilde, C) values for pools of size 1, 2, and 3
data(pdat1)

# Estimate log-OR for Xtilde and Y adjusted for C
fit <- p_logreg(g = pdat1$g, y = pdat1$allcases, x = pdat1[, c("xtilde", "c")])
fit$theta.hat