Yongqi Zhong

selected publications

1. 

   AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects

   Zhong, Yongqi, Kennedy, Edward H,  Bodnar, Lisa M and 1 more author

   American Journal of Epidemiology Jul 2021

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   An increasing number of recent studies suggest doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, existing programs that implement doubly robust estimators do not all support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed the AIPW package implementing the augmented inverse probability weighting (AIPW) estimation of average causal effects in R. Key features of the AIPW package includes cross-fitting and flexible covariate adjustment for observational studies and randomized trials (RCTs). In this paper, we use a simulated RCT to present the implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations including CausalGAM, npcausal, tmle, and tmle3. Our simulation shows that the xtbfAIPW package yielded comparable performance to other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fit with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies.

   @article{zhong_aipw_2021,
     author = {Zhong, Yongqi and Kennedy, Edward H and Bodnar, Lisa M and Naimi, Ashley I},
     doi = {10.1093/aje/kwab207},
     issn = {0002-9262},
     journal = {American Journal of Epidemiology},
     month = jul,
     number = {kwab207},
     shorttitle = {{AIPW}},
     title = {{AIPW}: {An} {R} {Package} for {Augmented} {Inverse} {Probability} {Weighted} {Estimation} of {Average} {Causal} {Effects}},
     urldate = {2021-07-16},
     year = {2021},
     bdsk-url-1 = {https://doi.org/10.1093/aje/kwab207},
   }

2. Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes: A Secondary Analysis of a Randomized Clinical Trial

   Zhong, Yongqi, Brooks, Maria M.,  Kennedy, Edward H. and 2 more authors

   JAMA Network Open Mar 2022

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   In randomized clinical trials (RCTs), per-protocol effects may be of interest in the presence of nonadherence with the randomized treatment protocol. Using machine learning in per-protocol effect estimation can help avoid model misspecification owing to strong parametric assumptions, as is common with standard methods (eg, logistic regression).To demonstrate the use of ensemble machine learning with augmented inverse probability weighting (AIPW) for per-protocol effect estimation in RCTs and to evaluate the per-protocol effect size of aspirin on pregnancy.This secondary analysis used data from 1227 women in the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial, a multicenter, block-randomized, double-blind, placebo-controlled clinical trial of the effect of daily low-dose aspirin on pregnancy outcomes in women at high risk of pregnancy loss. Participants were recruited at 4 university medical centers in the US from June 15, 2007, to July 15, 2012. Women were followed up for 6 menstrual cycles for attempted pregnancy and 36 weeks of gestation if pregnancy occurred. Follow-up was completed on August 17, 2012. Data analyses were performed on July 9, 2021.Daily low-dose (81 mg) aspirin taken at least 5 of 7 days per week for at least 80% of follow-up time relative to placebo.Pregnancy detected using human chorionic gonadotropin (hCG) levels.Among the 1227 women included in the analysis (mean SD age, 28.74 [4.80] years), 1161 (94.6%) were non-Hispanic White and 858 (69.9%) adhered to the protocol. Five machine learning models were combined into 1 meta-algorithm, which was used to construct an AIPW estimator for the per-protocol effect. Compared with adhering to placebo, adherence to the daily low-dose aspirin protocol for at least 5 of 7 days per week was associated with an increase in the probability of hCG-detected pregnancy of 8.0 (95% CI, 2.5-13.6) more hCG-detected pregnancies per 100 women in the sample, which is substantially larger than the estimated intention-to-treat estimate of 4.3 (95% CI, −1.1 to 9.6) more hCG-detected pregnancies per 100 women in the sample.These findings suggest that a low-dose aspirin protocol is associated with increased hCG-detected pregnancy in women who adhere to treatment for at least 5 days per week. With the presence of nonadherence, per-protocol treatment effect estimates differ from intention-to-treat estimates in the EAGeR trial. The results of this secondary analysis of clinical trial data suggest that machine learning could be used to estimate per-protocol effects by adjusting for confounders related to nonadherence in a more flexible way than traditional regressions.ClinicalTrials.gov Identifier: NCT00467363