A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 143

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

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

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

Biostatistics

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Statistics, Tel Aviv University, Tel Aviv, Israel and St. Luke's Radiation Oncology Associates, St. Luke's Regional Cancer Center, and Whiteside Institute for Clinical Research / University of Minnesota Duluth, Duluth, MN, USA.

Published: October 2017

AI Article Synopsis

  • Propensity score methods are essential for analyzing treatment effects in healthcare research using claims data, but they face challenges due to inaccuracies in treatment assignment.
  • Misclassification of treatment groups significantly affects three key stages of propensity score analysis: estimation, implementation, and outcome analysis.
  • A new two-step likelihood-based approach is proposed to correct for treatment misclassification bias, specifically in subclassification, and is tested using Medicare claims data related to brain tumor treatments.

Article 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862346PMC
http://dx.doi.org/10.1093/biostatistics/kxx014DOI Listing

Publication Analysis

Top Keywords

propensity score
24
treatment assignment
16
claims data
12
validation data
12
treatment
8
billing codes
8
score implementation
8
measurement error
8
propensity
7
score
6

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!

A PHP Error was encountered

Severity: Notice

Message: fwrite(): Write of 34 bytes failed with errno=28 No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 272

Backtrace:

A PHP Error was encountered

Severity: Warning

Message: session_write_close(): Failed to write session data using user defined save handler. (session.save_path: /var/lib/php/sessions)

Filename: Unknown

Line Number: 0

Backtrace: