Abstract | 1. Introduction | 2. The postlink Architecture | Phase 1: Adjustment Specification | Phase 2: Estimation and Inference | Dual-Access Interface | 3. Adjustment Methods for Secondary Analysis | 3.1 Weighting | 3.2 Inferring true links via latent mixture modeling (EM) | 3.3 Bayesian mixture modeling and multiple imputation | 4. Illustrations | 4.1 The Benchmarks (Models 1--3) | 4.2 Aggregate Information (Model 4) | 4.3 Probabilistic Paradata (Models 5--7) | 4.4 Prior Knowledge (Model 8) | 4.5 Results and Discussion | 4.6 Decoupling Linkage and Analysis via Multiple Imputation | 5. Conclusion | Computational Details | Acknowledgments | Appendix A: Computational and variance estimation details for the ELE model | A.1 Computation of expected design matrices | A.2 Variance estimation and audit sample uncertainty | Appendix B: Default Prior Distributions for Each Family of Distributions | References