Abstract
Measurement error/misclassification is commonplace in research when variable(s) cannot be measured accurately. A number of statistical methods have been developed to tackle this problem in a variety of settings and contexts. However, relatively few methods are available to handle misclassified categorical exposure variable(s) in the Cox proportional hazards regression model. In this article, we aim to review and compare different methods to handle this problem - naive methods, regression calibration, pooled estimation, multiple imputation, corrected score estimation, and MC-SIMEX - by simulation. These methods are also applied to a life course study with recalled data and historical records. In practice, the issue of measurement error/ misclassification should be accounted for in design and analysis, whenever possible. Also, in the analysis, it could be more ideal to implement more than one correction method for estimation and inference, with proper understanding of underlying assumptions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 381-400 |
| Number of pages | 20 |
| Journal | Journal of Statistical Theory and Practice |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 1 2013 |
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Keywords
- ARIC
- Childhood SES
- Cox proportional hazards regression
- Measurement error
- Misclassification
- Recalled error
ASJC Scopus subject areas
- Statistics and Probability
Cite this
Bias correction methods for misclassified covariates in the Cox model : Comparison of five correction methods by simulation and data analysis. / Bang, Heejung; Chiu, Ya Lin; Kaufman, Jay S.; Patel, Mehul D.; Heiss, Gerardo; Rose, Kathryn M.
In: Journal of Statistical Theory and Practice, Vol. 7, No. 2, 01.01.2013, p. 381-400.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bias correction methods for misclassified covariates in the Cox model
T2 - Comparison of five correction methods by simulation and data analysis
AU - Bang, Heejung
AU - Chiu, Ya Lin
AU - Kaufman, Jay S.
AU - Patel, Mehul D.
AU - Heiss, Gerardo
AU - Rose, Kathryn M.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Measurement error/misclassification is commonplace in research when variable(s) cannot be measured accurately. A number of statistical methods have been developed to tackle this problem in a variety of settings and contexts. However, relatively few methods are available to handle misclassified categorical exposure variable(s) in the Cox proportional hazards regression model. In this article, we aim to review and compare different methods to handle this problem - naive methods, regression calibration, pooled estimation, multiple imputation, corrected score estimation, and MC-SIMEX - by simulation. These methods are also applied to a life course study with recalled data and historical records. In practice, the issue of measurement error/ misclassification should be accounted for in design and analysis, whenever possible. Also, in the analysis, it could be more ideal to implement more than one correction method for estimation and inference, with proper understanding of underlying assumptions.
AB - Measurement error/misclassification is commonplace in research when variable(s) cannot be measured accurately. A number of statistical methods have been developed to tackle this problem in a variety of settings and contexts. However, relatively few methods are available to handle misclassified categorical exposure variable(s) in the Cox proportional hazards regression model. In this article, we aim to review and compare different methods to handle this problem - naive methods, regression calibration, pooled estimation, multiple imputation, corrected score estimation, and MC-SIMEX - by simulation. These methods are also applied to a life course study with recalled data and historical records. In practice, the issue of measurement error/ misclassification should be accounted for in design and analysis, whenever possible. Also, in the analysis, it could be more ideal to implement more than one correction method for estimation and inference, with proper understanding of underlying assumptions.
KW - ARIC
KW - Childhood SES
KW - Cox proportional hazards regression
KW - Measurement error
KW - Misclassification
KW - Recalled error
UR - http://www.scopus.com/inward/record.url?scp=84876138375&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876138375&partnerID=8YFLogxK
U2 - 10.1080/15598608.2013.772830
DO - 10.1080/15598608.2013.772830
M3 - Article
AN - SCOPUS:84876138375
VL - 7
SP - 381
EP - 400
JO - Journal of Statistical Theory and Practice
JF - Journal of Statistical Theory and Practice
SN - 1559-8608
IS - 2
ER -