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Secondary Analysis Constructor Based on Bayesian Mixture Modeling

Source: R/01_adjMixBayes.R
adjMixBayes.Rd

Specifies the linked data and information on the underlying record linkage process for a mismatch error adjustment using a Bayesian framework based on mixture modeling as developed by Gutman et al. (2016). This framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Posterior inference is carried out via data augmentation or multiple imputation.

Usage

adjMixBayes(linked.data = NULL, priors = NULL)

Arguments

linked.data

A data.frame containing the linked dataset.

priors

A named list (or NULL) of prior specifications. Because the Stan models are pre-compiled, these strings are parsed into numeric hyperparameters and passed to the model's data block. Any missing entries are automatically filled with symmetric defaults dynamically during the model fitting phase.

Value

An object of class c("adjMixBayes", "adjustment"). To minimize memory overhead, the underlying linked.data is stored by reference within an environment inside this object.

Details

Explicit provision of linked.data is strongly recommended for reproducibility and to ensure the adjustment object fully encapsulates the necessary data for downstream model fitting.

References

Gutman, R., Sammartino, C., Green, T., & Montague, B. (2016). Error adjustments for file linking methods using encrypted unique client identifier (eUCI) with application to recently released prisoners who are HIV+. Statistics in Medicine, 35(1), 115–129. doi:10.1002/sim.6586

See also

  • plglm() for generalized linear regression modeling

  • plsurvreg() for parametric survival modeling

Examples

data(lifem)

# lifem data preprocessing
# For computational efficiency in the example, we work with a subset of the lifem data.
lifem <- lifem[order(-(lifem$commf + lifem$comml)), ]
lifem_small <- rbind(
  head(subset(lifem, hndlnk == 1), 100),
  head(subset(lifem, hndlnk == 0), 20)
)

# Construct the Bayesian mixture adjustment object
adj_bayes <- adjMixBayes(
  linked.data = lifem_small,
  priors = list(theta = "beta(2, 2)")
)

class(adj_bayes)
#> [1] "adjMixBayes" "adjustment"