Dataset Information

Propensity scores with misclassified treatment assignment: a likelihood-based adjustment.

ABSTRACT: 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.

SUBMITTER: Braun D

PROVIDER: S-EPMC5862346 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Publications

Propensity scores with misclassified treatment assignment: a likelihood-based adjustment.

Braun Danielle D Gorfine Malka M Parmigiani Giovanni G Arvold Nils D ND Dominici Francesca F Zigler Corwin C

Biostatistics (Oxford, England) 20171001 4

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: ( ...[more]

PMID: 28419189

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Project description:BACKGROUND:Parentage assignment is usually based on a limited number of unlinked, independent genomic markers (microsatellites, low-density single nucleotide polymorphisms (SNPs), etc.). Classical methods for parentage assignment are exclusion-based (i.e. based on loci that violate Mendelian inheritance) or likelihood-based, assuming independent inheritance of loci. For true parent-offspring relations, genotyping errors cause apparent violations of Mendelian inheritance. Thus, the maximum proportion of such violations must be determined, which is complicated by variable call- and genotype error rates among loci and individuals. Recently, genotyping using high-density SNP chips has become available at lower cost and is increasingly used in genetics research and breeding programs. However, dense SNPs are not independently inherited, violating the assumptions of the likelihood-based methods. Hence, parentage assignment usually assumes a maximum proportion of exclusions, or applies likelihood-based methods on a smaller subset of independent markers. Our aim was to develop a fast and accurate trio parentage assignment method for dense SNP data without prior genotyping error- or call rate knowledge among loci and individuals. This genomic relationship likelihood (GRL) method infers parentage by using genomic relationships, which are typically used in genomic prediction models. RESULTS:Using 50 simulated datasets with 53,427 to 55,517 SNPs, genotyping error rates of 1-3% and call rates of?~?80 to 98%, GRL was found to be fast and highly (~?99%) accurate for parentage assignment. An iterative approach was developed for training using the evaluation data, giving similar accuracy. For comparison, we used the Colony2 software that assigns parentage and sibship simultaneously to increase the power of the likelihood-based method and found that it has considerably lower accuracy than GRL. We also compared GRL with an exclusion-based method in which one of the parameters was estimated using GRL assignments.This method was slightly more accurate than GRL. CONCLUSIONS:We show that GRL is a fast and accurate method of parentage assignment that can use dense, non-independent SNPs, with variable call rates and unknown genotyping error rates. By offering an alternative way of assigning parents, GRL is also suitable for estimating the expected proportion of inconsistent parent-offspring genotypes for exclusion-based models.

Dataset Information

Propensity scores with misclassified treatment assignment: a likelihood-based adjustment.

Publications

Propensity scores with misclassified treatment assignment: a likelihood-based adjustment.

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