Evaluating latent class models with conditional dependence in record linkage

Daggy, Joanne; Xu, Huiping; Hui, Siu L.; Grannis, Shaun J.

doi:10.1002/sim.6230

Cited by 13 publications

(10 citation statements)

References 32 publications

(65 reference statements)

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“…The conditional independence assumption can be relaxed by extending the model to add correlated errors, as and when there are extra df available for fitting, or possibly by assuming correlation values to be known, 28‐30 by making alternative assumptions, 31 or by adopting Bayesian methods 32‐34 . In fact, under the reasonable assumption of exchangeability of the repeated observations (ie, whenever their order is not relevant), it should be sufficient to add just one extra parameter to represent the correlation between any given pair of tests on an individual.…”

Section: Discussionmentioning

confidence: 99%

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Walter

2021

Statistics in Medicine

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This article considers how to estimate the accuracy of a diagnostic test when there are repeated observations, but without the availability of a gold standard or reference test. We identify conditions under which the structure of the observed data is rich enough to provide sufficient degrees of freedom, such that a suitable latent class model can be fitted with identifiable accuracy parameters. We show that a Rule of Three applies, specifying that accuracy can be evaluated as long as there are at least three observations per individual with the given test. This rule also applies if the three observations arise from combinations of different test methods, or from a sequential design in which individuals are tested for a maximum number of times with the same test but stopping if a positive (or negative) result occurs. The rule pertains to tests having an arbitrary number of response categories. Accuracy is evaluated by parameters reflecting rates of misclassification among the response categories, and the model also provides estimates of the underlying distribution of the true disease state. These ideas are illustrated by data from two medical studies. Issues discussed include the advantages and disadvantages of analyzing the response variable as binary or multinomial, as well as the feasibility of testing goodness of fit when the model incorporates a large number of parameters. Comparisons are possible between models that do or do not assume equal accuracy rates for the observations, and between models where certain misclassification parameters are or are not assumed to be zero.

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Section: Discussionmentioning

confidence: 99%

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Walter

2021

Statistics in Medicine

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“…This analysis used four manually curated gold-standard analytic datasets, which contained a random selection of true-positive and true-negative matches, [17][18][19][20][21][22][23]. Through the manual curation process, we know the true match status for each record, which is a significant advantage over databases where the true matches are unknown.…”

Section: Datasets and Use Casesmentioning

confidence: 99%

Evaluating the effect of data standardization and validation on patient matching accuracy

Grannis

Vest

et al. 2019

Journal of the American Medical Informatics Association

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Objective: This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. Methods: We used four manually reviewed datasets, containing a random selection of matches and non-matches. Matching datasets included Health Information Exchange (HIE) records, public health registry records, Social Security Death Master records, and newborn screening records. Standardized fields including last name (LN), telephone number (TEL), social security number (SSN), date of birth (DOB), and address (ADDR). Matching performance was evaluated using four metrics: sensitivity, specificity, positive predictive value, and accuracy. Results: Standardizing address was independently associated with improved matching sensitivities for both the public health and HIE datasets of approximately 0.6% and 4.5%. Overall accuracy was unchanged for both datasets due to reduced match specificity. We observed no similar impact for address standardization in the death master file dataset. Standardizing last name yielded improved matching sensitivity of 0.6% for the HIE dataset, while overall accuracy remained the same due to a decrease in match specificity. We noted no similar impact for other datasets. Standardizing other individual fields (telephone, DOB, or SSN) showed no matching improvements. Since standardizing address and last name improved matching sensitivity, we examined the combined effect of address and last name standardization, which showed that standardization improved sensitivity from 81.3% to 91.6% for the HIE dataset. Conclusion: Data standardization can improve match rates, thus ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety.

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“…In Table 3, we summarize the parameter estimates from the aforementioned manually reviewed random sample, from F-S model, a log-linear model with interactions between telephone number and zip code in both classes with different coefficients 20 (LLy), a log-linear model with all 42 (2C 7 2 ) class-specific pairwise two-way interactions among the seven fields (LL-all), and a log-linear model with two-way interactions that met the nominal significance level of 0.05 (LL-auto). The AUCs of all methods are similar, all above 99%.…”

Section: De-duplication Of a Single File Of Patient Recordsmentioning

confidence: 99%

“…In addition, estimation of the GRE models is computationally intensive and relies heavily on the choice of starting values. 7 Winkler used three-way interactions, which requires at least 10 fields. 8 Xu and Craig 5 also employed a correlation plot which can be useful in exploring and visualizing the correlations among fields, which can guide the modeling and serve as a simple diagnostic tool for identifying the conditional dependence.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have demonstrated limitations of the F-S probabilistic linkage method due to its conditional independence assumption, and the lack of robustness of conditional dependence models with respect to the conditional dependence structure. 7,[12][13][14][15][16][17] This lack of robustness leads to suboptimal decision rules for declaring matches and non-matches and subsequent inaccurate classification of record pairs. These findings highlight the importance of identifying the correct dependence structure in conditional dependence models, which has been shown to be a challenging task in many situations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated linkage of patient records from disparate sources

Shen

et al. 2016

Stat Methods Med Res

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We introduce an automated method of record linkage that has two key features, automated selection of match field interactions to include in the model for estimation and automated threshold determination for classifying record pairs to matches or non-matches. We applied our method to two real-world examples. The first example demonstrated results consistent with our earlier work: When data quality is adequate and the match field discriminating power is high, matching algorithms exhibit similar performance. The second example demonstrated that our method yields a lower false positive rate and higher positive predictive value than the Fellegi-Sunter model in the face of low data quality. When compared to the Fellegi-Sunter model, simulation studies suggest that our method exhibits better overall performance as indicated by higher area under the curve, and less biased estimates for both the match prevalence rate and the m- and u-probabilities over a range of data scenarios, especially when the match prevalence is extreme. Computationally, our method is as efficient as the Fellegi-Sunter model. We recommend this method in situations that an unsupervised linking algorithm is needed.

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Evaluating latent class models with conditional dependence in record linkage

Cited by 13 publications

References 32 publications

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Evaluating the effect of data standardization and validation on patient matching accuracy

Automated linkage of patient records from disparate sources

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