東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Statistical Methods for Comparative ...

Kunz, Lauren Margaret.

Linked to FindBook

Google Book

Amazon

博客來

Statistical Methods for Comparative Effectiveness Research of Medical Devices.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methods for Comparative Effectiveness Research of Medical Devices./
Author:	Kunz, Lauren Margaret.
Description:	131 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Contained By:	Dissertation Abstracts International76-09B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3700496
ISBN:	9781321704877

Statistical Methods for Comparative Effectiveness Research of Medical Devices.
Kunz, Lauren Margaret.

Statistical Methods for Comparative Effectiveness Research of Medical Devices. - 131 p.

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.

Thesis (Ph.D.)--Harvard University, 2015.

This item must not be sold to any third party vendors.

A recent focus in health care policy is on comparative effectiveness of treatments--from drugs to behavioral interventions to medical devices. Medical devices bring a unique set of challenges for comparative effectiveness research. In this dissertation, I develop statistical methods for comparative effectiveness estimation and illustrate the methodology in the context of three different medical devices. In chapter 2, I review approaches for causal inference in the context of observational cohort studies, utilizing a potential outcomes framework demonstrated using data for patients undergoing revascularization surgery with radial versus femoral artery access. Propensity score methods; G-computation; augmented inverse probability of treatment weighting; and targeted maximum likelihood estimation are implemented and their causal and statistical assumptions evaluated. In chapter 3, I undertake a theoretical and simulation-based assessment of differential follow-up information per treatment arm on inference in meta-analysis where applied researchers commonly assume similar follow-up duration across treatment groups. When applied to the implantation of cardiovascular resynchronization therapies to examine comparative survival, only 3 of 8 studies report arm-specific follow-up. I derive the bias of the rate ratio for an individual study using the number of deaths and total patients per arm and show that the bias can be large, even for modest violations of the assumption that follow-up is the same in the two arms. Furthermore, when pooling multiple studies with Bayesian methods for random effects meta-analysis, the direction and magnitude of the bias is unpredictable. In chapter 4, I examine the statistical power for designing a study of devices when it is difficult to blind patients and providers, everyone wants the device, and clustering by hospitals where the devices are implanted needs to be taken into account. In these situations, a stepped wedge design (SWD) cluster randomized design may be used to rigorously assess the roll-out of novel devices. I determine the exact asymptotic theoretical power using Romberg integration over cluster random effects to calculate power in a two-treatment, binary outcome SWD. Over a range of design parameters, the exact method is from 9% to 2.4 times more efficient than designs based on the existing method.

ISBN: 9781321704877Subjects--Topical Terms:

1002712
Biostatistics.

Statistical Methods for Comparative Effectiveness Research of Medical Devices.
LDR:03410nmm a2200301 4500 001 2063208
005 20151027100338.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781321704877
035 $a (MiAaPQ)AAI3700496
035 $a AAI3700496
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kunz, Lauren Margaret. $3 3177709
245 1 0 $a Statistical Methods for Comparative Effectiveness Research of Medical Devices.
300 $a 131 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
500 $a Adviser: Sharon-Lise T. Normand.
502 $a Thesis (Ph.D.)--Harvard University, 2015.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a A recent focus in health care policy is on comparative effectiveness of treatments--from drugs to behavioral interventions to medical devices. Medical devices bring a unique set of challenges for comparative effectiveness research. In this dissertation, I develop statistical methods for comparative effectiveness estimation and illustrate the methodology in the context of three different medical devices. In chapter 2, I review approaches for causal inference in the context of observational cohort studies, utilizing a potential outcomes framework demonstrated using data for patients undergoing revascularization surgery with radial versus femoral artery access. Propensity score methods; G-computation; augmented inverse probability of treatment weighting; and targeted maximum likelihood estimation are implemented and their causal and statistical assumptions evaluated. In chapter 3, I undertake a theoretical and simulation-based assessment of differential follow-up information per treatment arm on inference in meta-analysis where applied researchers commonly assume similar follow-up duration across treatment groups. When applied to the implantation of cardiovascular resynchronization therapies to examine comparative survival, only 3 of 8 studies report arm-specific follow-up. I derive the bias of the rate ratio for an individual study using the number of deaths and total patients per arm and show that the bias can be large, even for modest violations of the assumption that follow-up is the same in the two arms. Furthermore, when pooling multiple studies with Bayesian methods for random effects meta-analysis, the direction and magnitude of the bias is unpredictable. In chapter 4, I examine the statistical power for designing a study of devices when it is difficult to blind patients and providers, everyone wants the device, and clustering by hospitals where the devices are implanted needs to be taken into account. In these situations, a stepped wedge design (SWD) cluster randomized design may be used to rigorously assess the roll-out of novel devices. I determine the exact asymptotic theoretical power using Romberg integration over cluster random effects to calculate power in a two-treatment, binary outcome SWD. Over a range of design parameters, the exact method is from 9% to 2.4 times more efficient than designs based on the existing method.
590 $a School code: 0084.
650 4 $a Biostatistics. $3 1002712
650 4 $a Health care management. $3 2122906
690 $a 0308
690 $a 0769
710 2 $a Harvard University. $b Biostatistics. $3 2104931
773 0 $t Dissertation Abstracts International $g 76-09B(E).
790 $a 0084
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3700496