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Developing and Evaluating Innovative Approaches for Estimating Causal Effects of Low-dose Aspirin on Pregnancy Outcomes

Zhong, Yongqi (2021) Developing and Evaluating Innovative Approaches for Estimating Causal Effects of Low-dose Aspirin on Pregnancy Outcomes. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

First trimester pregnancy loss occurs in one third of all pregnancies, and recurrent pregnancy loss is also prevalent in up to 30% of women with a prior history. Using intention-to-treat, the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial found that low-dose aspirin (LDA) led to 4.3 (95% CI -1.2 to 9.6) per 100 women at high risk of pregnancy loss. However, the estimated effect, which is based on the assignment to a treatment arm, rather than adherence to a particular treatment protocol, limits the understanding of potential benefits of LDA on pregnancy.

Existing methods for adherence adjustment to estimate per-protocol effects in randomized trials are subject to the limitations of observational studies, including model mis-specification due to incorrect confounder selection, or from strong parametric assumptions.

The objective of this dissertation is to evaluate and develop innovative approaches for estimating the adherence-adjusted effects of LDA on pregnancy. First, to mitigate the impact of incorrect confounder selection, we evaluated the performance of causal discovery methods in a simulation study using the data resampled from EAGeR. We found that, the evaluated causal discovery method yielded low accuracy in selecting sufficient confounder adjustment sets in the M- or Butterfly-structured causal diagrams. Second, to avoid strong parametric assumptions, we developed an R package implementing the augmented inverse probability weighting (AIPW), a doubly robust estimator supporting stacking machine learning. Our simulation study suggests that, our AIPW package has excellent performance compared to existing R packages implementing doubly robust estimators. Finally, we used the AIPW package with stacking machine learning to estimate per-protocol effects of LDA in a time-fixed setting from the EAGeR trial. Our results show that LDA led to 8.0 (95% CI 2.5 to 13.6) more pregnancies per 100 women who adhered to the randomized treatment assignment for at least 5/7 days per week over at least 80% person-week of follow-up, consistent with the previous analysis using parametric g-formula in a time-varying setting. In conclusion, this dissertation does not only provide additional evidence of the benefits of LDA on pregnancy, but also the state-of-the-art approaches for effect estimations in epidemiologic studies.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhong, YongqiYOZ21@pitt.eduyoz210000-0002-4042-7450
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairBodnar, Lisabodnar@edc.pitt.edubodnar
Committee CoChairNaimi, Ashleyashley.naimi@emory.edu0000-0002-1510-8175
Committee MemberBrooks, MariaMBROOKS@pitt.edumbrooks
Committee MemberTriantafyllou, SofiaSOT16@pitt.edusot16
Committee MemberKennedy, Edwardedward@stat.cmu.edu
Date: 27 August 2021
Date Type: Publication
Defense Date: 1 July 2021
Approval Date: 27 August 2021
Submission Date: 16 July 2021
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 137
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Epidemiology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Epidemiologic Methods; Per-protocol Analysis; Randomized Controlled Trials; Directed Acyclic Graph; Causal Discovery; Doubly Robust Estimation; Causal Inference; Machine Learning; Pregnancy; Aspirin
Additional Information: The Chapter 3 of this dissertation was published by the American Journal of Epidemiology. To cite this chapter, please use "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. doi:10.1093/aje/kwab207"
Date Deposited: 27 Aug 2021 18:16
Last Modified: 27 Aug 2021 18:16
URI: http://d-scholarship.pitt.edu/id/eprint/41429

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