Measurement error correction in the least absolute shrinkage and selection operator model when validation data are available.
Measurement error correction in the least absolute shrinkage and selection operator model when validation data are available.
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DOI：
10.1177/0962280217734241
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发表时间：
2019-03
影响因子：
2.3
通讯作者：
Guerra S
中科院分区：
文献类型：
--
作者：
Vasquez MM;Hu C;Roe DJ;Halonen M;Guerra S
Measurement of serum biomarkers by multiplex assays may be more variable as compared to single biomarker assays. Measurement error in these data may bias parameter estimates in regression analysis, which could mask true associations of serum biomarkers with an outcome. The Least Absolute Shrinkage and Selection Operator (LASSO) can be used for variable selection in these high-dimensional data. Furthermore, when the distribution of measurement error is assumed to be known or estimated with replication data, a simple measurement error correction method can be applied to the LASSO method. However, in practice the distribution of the measurement error is unknown and is expensive to estimate through replication both in monetary cost and need for greater amount of sample which is often limited in quantity. We adapt an existing bias correction approach by estimating the measurement error using validation data in which a subset of serum biomarkers are re-measured on a random subset of the study sample. We evaluate this method using simulated data and data from the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD). We show that the bias in parameter estimation is reduced and variable selection is improved.
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DOI：
10.1097/mco.0b013e32833cf3bc
发表时间：
2010-09
影响因子：
3.1
作者：
Zhou X;Fragala MS;McElhaney JE;Kuchel GA
通讯作者：
Kuchel GA
影响因子：
9.3
作者：
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通讯作者：
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影响因子：
5
作者：
LEBOWITZ, MD;KNUDSON, RJ;BURROWS, B
通讯作者：
BURROWS, B
影响因子：
2.2
作者：
Bastarache, Julie A.;Koyama, Tatsuki;Ware, Lorraine B.
通讯作者：
Ware, Lorraine B.
影响因子：
1.4
作者：
Sorensen, Oystein;Frigessi, Arnoldo;Thoresen, Magne
通讯作者：
Thoresen, Magne