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Throwing out the baby with the bathwater?: Comparing 2 approaches to implausible values of change in body size.

ABSTRACT: In childhood obesity research, the appearance of height loss, or "shrinkage," indicates measurement error. It is unclear whether a common response--excluding "shrinkers" from analysis--reduces bias.Using data from the National Longitudinal Study of Adolescent Health, we sampled 816 female adolescents (?17 years) who had attained adult height by 1996 and for whom adult height was consistently measured in 2001 and 2008 ("gold-standard" height). We estimated adolescent obesity prevalence and the association of maternal education with adolescent obesity under 3 conditions: excluding shrinkers (for whom gold-standard height was less than recorded height in 1996), retaining shrinkers, and retaining shrinkers but substituting their gold-standard height.When we estimated obesity prevalence, excluding shrinkers decreased precision without improving validity. When we regressed obesity on maternal education, excluding shrinkers produced less valid and less precise estimates.In some circumstances, ignoring shrinkage is a better strategy than excluding shrinkers.

SUBMITTER: Gray CL

PROVIDER: S-EPMC4417741 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Throwing out the baby with the bathwater?: Comparing 2 approaches to implausible values of change in body size.

Gray Christine L CL Robinson Whitney R WR

Epidemiology (Cambridge, Mass.) 20140701 4

<h4>Background</h4>In childhood obesity research, the appearance of height loss, or "shrinkage," indicates measurement error. It is unclear whether a common response--excluding "shrinkers" from analysis--reduces bias.<h4>Methods</h4>Using data from the National Longitudinal Study of Adolescent Health, we sampled 816 female adolescents (≥17 years) who had attained adult height by 1996 and for whom adult height was consistently measured in 2001 and 2008 ("gold-standard" height). We estimated adole ...[more]

PMID: 24809955

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Project description:BackgroundIdentifying implausible clinical observations (e.g., laboratory test and vital sign values) in Electronic Health Record (EHR) data using rule-based procedures is challenging. Anomaly/outlier detection methods can be applied as an alternative algorithmic approach to flagging such implausible values in EHRs.MethodsThe primary objectives of this research were to develop and test an unsupervised clustering-based anomaly/outlier detection approach for detecting implausible observations in EHR data as an alternative algorithmic solution to the existing procedures. Our approach is built upon two underlying hypotheses that, (i) when there are large number of observations, implausible records should be sparse, and therefore (ii) if these data are clustered properly, clusters with sparse populations should represent implausible observations. To test these hypotheses, we applied an unsupervised clustering algorithm to EHR observation data on 50 laboratory tests from Partners HealthCare. We tested different specifications of the clustering approach and computed confusion matrix indices against a set of silver-standard plausibility thresholds. We compared the results from the proposed approach with conventional anomaly detection (CAD) approaches, including standard deviation and Mahalanobis distance.ResultsWe found that the clustering approach produced results with exceptional specificity and high sensitivity. Compared with the conventional anomaly detection approaches, our proposed clustering approach resulted in significantly smaller number of false positive cases.ConclusionOur contributions include (i) a clustering approach for identifying implausible EHR observations, (ii) evidence that implausible observations are sparse in EHR laboratory test results, (iii) a parallel implementation of the clustering approach on i2b2 star schema, and (3) a set of silver-standard plausibility thresholds for 50 laboratory tests that can be used in other studies for validation. The proposed algorithmic solution can augment human decisions to improve data quality. Therefore, a workflow is needed to complement the algorithm's job and initiate necessary actions that need to be taken in order to improve the quality of data.

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Throwing out the baby with the bathwater?: Comparing 2 approaches to implausible values of change in body size.

Publications

Throwing out the baby with the bathwater?: Comparing 2 approaches to implausible values of change in body size.

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