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Methods for Comparative Effectivenes...

Harton, Joanna Grace.

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Methods for Comparative Effectiveness Research with Real World Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Methods for Comparative Effectiveness Research with Real World Data./
Author:	Harton, Joanna Grace.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	95 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968307
ISBN:	9798837502576

Methods for Comparative Effectiveness Research with Real World Data.
Harton, Joanna Grace.

Methods for Comparative Effectiveness Research with Real World Data. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 95 p.

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2022.

Electronic health records (EHR) contain a wealth of information that can potentially be used for research. EHR is particularly valuable for accelerating comparative effectiveness research, which seeks to estimate the relative benefit of alternative treatments in real-world settings. However there are also many challenges and limitations to conducting research with EHR-derived data, including measurement error, systematic differences between EHR and trial populations, and differential quantity and quality of data available across patients. In this dissertation, we first investigate the performance of several methods for mitigating bias due to measurement error applied to propensity scores created from error-prone covariates and provide recommendations for which methods to use in certain scenarios. Next, we develop a method to augment a randomized clinical trial with EHR data in order to create a hybrid control arm. This new method, data-adaptive-weighting, has promising properties when compared to more standard methods as well as another more recent propensity score-based method. Finally, we investigate the scenario of informed presence bias in longitudinal data, which arises when some subjects have more information in the EHR than others based on their intensity of utilization. We extend current work and explore several scenarios in which adjusting for biomarker values obtained at both pre-scheduled and patient-initiated visits in order to determine when there is risk of bias. For each topic we applied alternative methods to real-world EHR-derived data to demonstrate practical implications of alternative approaches in realistic settings.

ISBN: 9798837502576Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Electronic health records

Methods for Comparative Effectiveness Research with Real World Data.
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300 $a 95 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisor: Hubbard, Rebecca A.;Mitra, Nandita.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2022.
520 $a Electronic health records (EHR) contain a wealth of information that can potentially be used for research. EHR is particularly valuable for accelerating comparative effectiveness research, which seeks to estimate the relative benefit of alternative treatments in real-world settings. However there are also many challenges and limitations to conducting research with EHR-derived data, including measurement error, systematic differences between EHR and trial populations, and differential quantity and quality of data available across patients. In this dissertation, we first investigate the performance of several methods for mitigating bias due to measurement error applied to propensity scores created from error-prone covariates and provide recommendations for which methods to use in certain scenarios. Next, we develop a method to augment a randomized clinical trial with EHR data in order to create a hybrid control arm. This new method, data-adaptive-weighting, has promising properties when compared to more standard methods as well as another more recent propensity score-based method. Finally, we investigate the scenario of informed presence bias in longitudinal data, which arises when some subjects have more information in the EHR than others based on their intensity of utilization. We extend current work and explore several scenarios in which adjusting for biomarker values obtained at both pre-scheduled and patient-initiated visits in order to determine when there is risk of bias. For each topic we applied alternative methods to real-world EHR-derived data to demonstrate practical implications of alternative approaches in realistic settings.
590 $a School code: 0175.
650 4 $a Biostatistics. $3 1002712
650 4 $a Information science. $3 554358
653 $a Electronic health records
653 $a Research
653 $a Real world data
690 $a 0308
690 $a 0723
710 2 $a University of Pennsylvania. $b Mathematics. $3 2101086
773 0 $t Dissertations Abstracts International $g 84-02B.
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793 $a English
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