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Secondary Analysis Constructor Based on Mixture Modeling

Source: R/01_adjMixture.R
adjMixture.Rd

Specifies the linked data and information on the underlying record linkage process for the "General Framework for Regression with Mismatched Data" developed by Slawski et al. (2025). 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. Inference is based on composite likelihood and the EM algorithm. Examples of information about the underlying record linkage process that can be incorporated into the method if available are the assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches.

Usage

adjMixture(
  linked.data = NULL,
  m.formula = ~1,
  m.rate = NULL,
  safe.matches = NULL
)

Arguments

linked.data

A data.frame containing the linked dataset. If NULL, the function attempts to resolve variables specified in m.formula from the environment.

m.formula

A one-sided formula object for the mismatch indicator model, with the covariates on the right of "~". The default is an intercept-only model corresponding to a constant mismatch rate.

m.rate

Numeric; an optional estimate (a proportion between 0 and 1) to constrain the global mismatch rate estimate. Defaults to NULL.

safe.matches

A logical vector or an unquoted variable name found in linked.data; an indicator variable for safe matches (TRUE : record can be treated as a correct match and FALSE : record may be mismatched). The default is FALSE for all matches.

Value

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

Details

The constructor assumes that any variables defined in m.formula and safe.matches are in linked.data or in the same environment. 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

Slawski, M., West, B. T., Bukke, P., Wang, Z., Diao, G., & Ben-David, E. (2025). A general framework for regression with mismatched data based on mixture modelling. Journal of the Royal Statistical Society Series A: Statistics in Society, 188(3), 896-919. doi:10.1093/jrsssa/qnae083

Bukke, P., Ben-David, E., Diao, G., Slawski, M., & West, B. T. (2025). Cox Proportional Hazards Regression Using Linked Data: An Approach Based on Mixture Modeling. In Frontiers of Statistics and Data Science (pp. 181-200). Singapore: Springer Nature Singapore. doi:10.1007/978-981-96-0742-6_8

Slawski, M., Diao, G., Ben-David, E. (2021). A pseudo-likelihood approach to linear regression with partially shuffled data. Journal of Computational and Graphical Statistics. 30(4), 991-1003. doi:10.1080/10618600.2020.1870482

See also

  • plglm() for generalized linear regression modeling

  • plcoxph() for Cox proportional hazards regression modeling

  • plctable() for contingency table analysis

Examples

# Load the LIFE-M demo dataset
data(lifem)

# Phase 1: Adjustment Specification
# We model the correct match indicator via logistic regression using
# name commonness scores (commf, comml) and a 5% expected mismatch rate.
adj_object <- adjMixture(
 linked.data = lifem,
 m.formula = ~ commf + comml,
 m.rate = 0.05,
 safe.matches = hndlnk
)