Latent class analysis in medical research

Formann, Anton K.; Kohlmann, Thomas

doi:10.1177/096228029600500205

Cited by 132 publications

(107 citation statements)

References 132 publications

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“…This type of latent variable model uses maximum likelihood methods to estimate the probability that a readmission was truly avoidable based on the independent classifications by multiple reviewers. 14,15 Modelling was done with PROC LCA in SAS 9.2, in which the sensitivity and specificity of each reviewer was allowed to vary independently. 16 Final output for the models was the posterior probability that each readmission represented a truly avoidable readmission.…”

Section: Discussionmentioning

confidence: 99%

Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions

Walraven¹,

Jennings²,

Taljaard³

et al. 2011

CMAJ

148

128

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U rgent, unplanned hospital readmissions are increasingly being used to measure institutional or regional quality of care. 1−4 The public reporting of readmissions and their use in considerations for funding suggest a belief that readmissions indicate the quality of care provided by particular institutions. However, urgent readmissions are an informative metric only if we know what proportion of them are avoidable. If they are rarely avoidable, they would be a poor gauge of the quality of patient care.Current estimates of the proportion of urgent readmissions that are avoidable are unreliable. In a systematic review of 34 studies that reviewed how many readmissions were avoidable, 3 of the studies relied solely on combinations of administrative diagnostic codes, and most used undefined or subjective criteria. 5 In addition, most of the studies were conducted at a single centre and used only one reviewer. The proportion of readmissions deemed avoidable varied widely, from 5.1% 6 to 78.9%, 7 which reflected in part the lack of standardized and reliable methods to identify avoidable readmissions.We conducted a multicentre prospective cohort study to elicit judgments from multiple practising physicians who used standard implicit review methods to determine whether urgent re admissions were potentially avoidable. We analyzed these judgments using a latent class analysis. We also measured the proportion of readmissions deemed avoidable and compared hospital-specific proportions of all-cause and avoidable readmissions. Methods Study designThis was a secondary analysis of a multicentre prospective cohort study involving patients discharged to the community after elective or urgent Research CMAJ Background: Urgent, unplanned hospital readmissions are increasingly being used to gauge the quality of care. We re viewed urgent readmissions to determine which were potentially avoidable and compared rates of allcause and avoidable readmissions. Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions

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Section: Discussionmentioning

confidence: 99%

Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions

Walraven¹,

Jennings²,

Taljaard³

et al. 2011

CMAJ

148

128

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“…Several papers have discussed methods for using replicate data to estimate classification probabilities for more general settings, including parsimonious models for polychotomous W and models that allow the conditional distribution of the replicates given W to involve some dependence [8,[36][37][38][39]. The case of correlated replicates is of particular relevance to risk factors that are measured through self-report, such as dietary or physical activity variables.…”

Section: Estimated Classification Probabilitiesmentioning

confidence: 99%

Corrected score estimation in the proportional hazards model with misclassified discrete covariates

Zucker

Spiegelman

2008

Statistics in Medicine

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SUMMARYWe consider Cox proportional hazards regression when the covariate vector includes error-prone discrete covariates along with error-free covariates, which may be discrete or continuous. The misclassification in the discrete error-prone covariates is allowed to be of any specified form. Building on the work of Nakamura and his colleagues, we present a corrected score method for this setting. The method can handle all three major study designs (internal validation design, external validation design, and replicate measures design), both functional and structural error models, and time-dependent covariates satisfying a certain 'localized error' condition. We derive the asymptotic properties of the method and indicate how to adjust the covariance matrix of the regression coefficient estimates to account for estimation of the misclassification matrix. We present the results of a finite-sample simulation study under Weibull survival with a single binary covariate having known misclassification rates. The performance of the method described here was similar to that of related methods we have examined in previous works. Specifically, our new estimator performed as well as or, in a few cases, better than the full Weibull maximum likelihood estimator. We also present simulation results for our method for the case where the misclassification probabilities are estimated from an external replicate measures study. Our method generally performed well in these simulations. The new estimator has a broader range of applicability than many other estimators proposed in the literature, including those described in our own earlier work, in that it can handle time-dependent covariates with an arbitrary misclassification structure. We illustrate the method on data from a study of the relationship between dietary calcium intake and distal colon cancer.

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“…For further reading on latent class models we recommend the book by McCutcheon 76 and the survey papers by Clogg 77 and Formann and Kohlmann, 78 the latter with special emphasis on applications in medicine.…”

Section: Exploratory Latent Class Modelmentioning

confidence: 99%

“…Latent class models are also often used to investigate rater agreement 78,79 such as agreement among pathologists in their classification of tumors, and for scaling, 80 for instance for investigating the stages and pathways of drug involvement. 81 Extensions of latent class models for longitudinal data include latent transition or latent Markov models [82][83][84] for discovering latent states and estimating transition probabilities between them.…”

Section: Some Applications Of Latent Class Analysis In Medical Researchmentioning

confidence: 99%

Classical latent variable models for medical research

Rabe‐Hesketh

Skrondal

2008

Stat Methods Med Res

120

110

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Latent variable models are commonly used in medical statistics, although often not referred to under this name. In this paper we describe classical latent variable models such as factor analysis, item response theory, latent class models and structural equation models. Their usefulness in medical research is demonstrated using real data. Examples include measurement of forced expiratory flow, measurement of physical disability, diagnosis of myocardial infarction and modelling the determinants of clients' satisfaction with counsellors' interviews.

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Latent class analysis in medical research

Cited by 132 publications

References 132 publications

Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions

Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions

Corrected score estimation in the proportional hazards model with misclassified discrete covariates

Classical latent variable models for medical research

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