Literature DB >> 33748097

Comparison of Quarterly and Yearly Calibration Data for Propensity Score Adjusted Web Survey Estimates.

Katherine E Irimata1, Yulei He1, Bill Cai1, Hee-Choon Shin1, Van L Parsons1, Jennifer D Parker1.   

Abstract

While web surveys have become increasingly popular as a method of data collection, there is concern that estimates obtained from web surveys may not reflect the target population of interest. Web survey estimates can be calibrated to existing national surveys using a propensity score adjustment, although requirements for the size and collection timeline of the reference data set have not been investigated. We evaluate health outcomes estimates from the National Center for Health Statistics' Research and Development web survey. In our study, the 2016 National Health Interview Survey as well as its quarterly subsets are considered as reference datasets for the web data. It is demonstrated that the calibrated health estimates overall vary little when using the quarterly or yearly data, suggesting that there is flexibility in selecting the reference dataset. This finding has many practical implications for constructing reference data, including the reduced cost and burden of a smaller sample size and a more flexible timeline.

Keywords:  National Health Interview Survey; Research and Development Survey; calibration; health survey; propensity score models; web survey

Year:  2020        PMID: 33748097      PMCID: PMC7976170          DOI: 10.13094/SMIF-2020-00018

Source DB:  PubMed          Journal:  Surv Methods Insights Field        ISSN: 2296-4754


  3 in total

1.  Matching using estimated propensity scores: relating theory to practice.

Authors:  D B Rubin; N Thomas
Journal:  Biometrics       Date:  1996-03       Impact factor: 2.571

2.  National Center for Health Statistics Data Presentation Standards for Proportions.

Authors:  Jennifer D Parker; Makram Talih; Donald J Malec; Vladislav Beresovsky; Margaret Carroll; Joe F Gonzalez; Brady E Hamilton; Deborah D Ingram; Kenneth Kochanek; Frances McCarty; Chris Moriarity; Iris Shimizu; Alexander Strashny; Brian W Ward
Journal:  Vital Health Stat 2       Date:  2017-08

3.  Generalizing observational study results: applying propensity score methods to complex surveys.

Authors:  Eva H Dugoff; Megan Schuler; Elizabeth A Stuart
Journal:  Health Serv Res       Date:  2013-07-16       Impact factor: 3.402

  3 in total
  5 in total

1.  The Research and Development Survey (RANDS) during COVID-19.

Authors:  Katherine E Irimata; Paul J Scanlon
Journal:  Stat J IAOS       Date:  2022-03-21

2.  Overview and Initial Results of the National Center for Health Statistics' Research and Development Survey.

Authors:  Jennifer Parker; Kristen Miller; Yulei He; Paul Scanlon; Bill Cai; Hee-Choon Shin; Van Parsons; Katherine Irimata
Journal:  Stat J IAOS       Date:  2020-11-25

3.  Variable inclusion strategies through directed acyclic graphs to adjust health surveys subject to selection bias for producing national estimates.

Authors:  Yan Li; Katherine E Irimata; Yulei He; Jennifer Parker
Journal:  J Off Stat       Date:  2022-09       Impact factor: 1.139

4.  National Health Interview Survey, COVID-19, and Online Data Collection Platforms: Adaptations, Tradeoffs, and New Directions.

Authors:  Stephen J Blumberg; Jennifer D Parker; Brian C Moyer
Journal:  Am J Public Health       Date:  2021-12       Impact factor: 9.308

5.  Impact of Kidney Failure Risk Prediction Clinical Decision Support on Monitoring and Referral in Primary Care Management of CKD: A Randomized Pragmatic Clinical Trial.

Authors:  Lipika Samal; John D D'Amore; Michael P Gannon; John L Kilgallon; Jean-Pierre Charles; Devin M Mann; Lydia C Siegel; Kelly Burdge; Shimon Shaykevich; Stuart Lipsitz; Sushrut S Waikar; David W Bates; Adam Wright
Journal:  Kidney Med       Date:  2022-05-28
  5 in total

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