TY - JOUR
T1 - Propensity scores with misclassified treatment assignment
T2 - A likelihood-based adjustment
AU - Braun, Danielle
AU - Gorfine, Malka
AU - Parmigiani, Giovanni
AU - Arvold, Nils D.
AU - Dominici, Francesca
AU - Zigler, Corwin
N1 - Publisher Copyright:
© The Author 2017. Published by Oxford University Press. All rights reserved.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment (T) is often measured with error. In the context of a validation data where treatment assignment is accurate, we show that misclassification of treatment assignment can impact three distinct stages of a propensity score analysis: (i) propensity score estimation; (ii) propensity score implementation; and (iii) outcome analysis conducted conditional on the estimated propensity score and its implementation.We examine how the error in T impacts each stage in the context of three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). Using validation data, we propose a two-step likelihood-based approach which fully adjusts for treatment misclassification bias under subclassification. This approach relies on two common measurement error-assumptions; non-differential measurement error and transportability of the measurement error model. We use simulation studies to assess the performance of the adjustment under subclassification, and also investigate the method's performance under matching or IPTW.We apply the methods to Medicare Part A hospital claims data to estimate the effect of resection versus biopsy on 1-year mortality among 10 284 Medicare beneficiaries diagnosed with brain tumors. The ICD9 billing codes from Medicare Part A inaccurately reflect surgical treatment, but SEER-Medicare validation data are available with more accurate information.
AB - Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment (T) is often measured with error. In the context of a validation data where treatment assignment is accurate, we show that misclassification of treatment assignment can impact three distinct stages of a propensity score analysis: (i) propensity score estimation; (ii) propensity score implementation; and (iii) outcome analysis conducted conditional on the estimated propensity score and its implementation.We examine how the error in T impacts each stage in the context of three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). Using validation data, we propose a two-step likelihood-based approach which fully adjusts for treatment misclassification bias under subclassification. This approach relies on two common measurement error-assumptions; non-differential measurement error and transportability of the measurement error model. We use simulation studies to assess the performance of the adjustment under subclassification, and also investigate the method's performance under matching or IPTW.We apply the methods to Medicare Part A hospital claims data to estimate the effect of resection versus biopsy on 1-year mortality among 10 284 Medicare beneficiaries diagnosed with brain tumors. The ICD9 billing codes from Medicare Part A inaccurately reflect surgical treatment, but SEER-Medicare validation data are available with more accurate information.
KW - Comparative effectiveness
KW - Measurement error
KW - Observational data
KW - Propensity scores
KW - Validation data
UR - http://www.scopus.com/inward/record.url?scp=85032457862&partnerID=8YFLogxK
U2 - 10.1093/biostatistics/kxx014
DO - 10.1093/biostatistics/kxx014
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C2 - 28419189
AN - SCOPUS:85032457862
SN - 1465-4644
VL - 18
SP - 695
EP - 710
JO - Biostatistics
JF - Biostatistics
IS - 4
ER -