Dataset Information

Flexibly Accounting for Exposure Misclassification With External Validation Data.

ABSTRACT: Measurement error is common in epidemiology, but few studies use quantitative methods to account for bias due to mismeasurement. One potential barrier is that some intuitive approaches that readily combine with methods to account for other sources of bias, like multiple imputation for measurement error (MIME), rely on internal validation data, which are rarely available. Here, we present a reparameterized imputation approach for measurement error (RIME) that can be used with internal or external validation data. We illustrate the advantages of RIME over a naive approach that ignores measurement error and MIME using a hypothetical example and a series of simulation experiments. In both the example and simulations, we combine MIME and RIME with inverse probability weighting to account for confounding when estimating hazard ratios and counterfactual risk functions. MIME and RIME performed similarly when rich external validation data were available and the prevalence of exposure did not vary between the main study and the validation data. However, RIME outperformed MIME when validation data included only true and mismeasured versions of the exposure or when exposure prevalence differed between the data sources. RIME allows investigators to leverage external validation data to account for measurement error in a wide range of scenarios.

SUBMITTER: Edwards JK

PROVIDER: S-EPMC7608057 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Flexibly Accounting for Exposure Misclassification With External Validation Data.

Edwards Jessie K JK Cole Stephen R SR Fox Matthew P MP

American journal of epidemiology 20200801 8

Measurement error is common in epidemiology, but few studies use quantitative methods to account for bias due to mismeasurement. One potential barrier is that some intuitive approaches that readily combine with methods to account for other sources of bias, like multiple imputation for measurement error (MIME), rely on internal validation data, which are rarely available. Here, we present a reparameterized imputation approach for measurement error (RIME) that can be used with internal or external ...[more]

PMID: 31971584

Similar Datasets

Project description:BackgroundMany dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records.MethodsWe identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters' Dementia Risk Score, Mehta's RxDx-Dementia Risk Index, and Nori's ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models.ResultsWe reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67-0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69-0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62-0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard.ConclusionWe observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.

Dataset Information

Flexibly Accounting for Exposure Misclassification With External Validation Data.

Publications

Flexibly Accounting for Exposure Misclassification With External Validation Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets