Abstract
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.
Highlights
Intention-to-treat (ITT) effects from randomized clinical trials (RCTs) are the reference standard for evaluating treatment effects
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 human chorionic gonadotropin (hCG)-detected pregnancy of 8.0 more hCG-detected pregnancies per 100 women in the sample, which is substantially larger than the estimated intention-to-treat estimate of 4.3 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
Summary
Intention-to-treat (ITT) effects from randomized clinical trials (RCTs) are the reference standard for evaluating treatment effects. Several investigators[2,5] have called for a more formal approach to per-protocol effect estimation in RCTs, and several per-protocol analyses[4,6-12] have demonstrated important deviations from ITT estimates when nonadherence is accounted for. When per-protocol effects are targeted in RCTs, all limitations associated with observational studies must be considered, such as confounding bias.[2,4]. Machine learning methods can be used with augmented inverse probability weighting (AIPW) and stacked regression models to overcome some of these limitations[13,14] and to estimate per-protocol effects when adjusting for confounding variables. Many machine learning algorithms can avoid these problems,[15,16] but they have not yet been applied to scenarios in which per-protocol effects are of primary interest
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