Efavirenz was associated with increased suicidal thoughts/behaviors in an analysis of randomized trials. However, analyses of observational data have found no evidence of increased hazard. To assess whether population differences explain this divergence we transported the effect of efavirenz from these trials to a specific target population. Using inverse odds weights and multiple imputation, the effect of efavirenz on suicidal thoughts/behaviors in these randomized trials (enrolled 2001–2007) was transported to a trials-eligible cohort of adults in the United States initiating antiretroviral therapy while receiving HIV clinical care at medical centers between 1999–2015. Overall, 8,291 cohort and 3,949 trial participants were eligible. Antidepressant prescription (19% vs. 13%) and injection drug history (16% vs. 10%) were more frequent in the cohort versus trials. Compared to the effect in trials, the estimated hazard ratio for efavirenz on suicidal thoughts/behaviors was attenuated in our target population (trials=2.3, 95%CI: 1.2, 4.4; transported=1.8, 95%CI: 0.90, 4.4), whereas the incidence rate difference was similar (trials=5.1, 95%CI: 1.6, 8.7; transported=5.4, 95%CI: -0.4, 11.4). In our target population, there was over 20% attenuation of the hazard ratio estimate compared to the trials-only estimate. Transporting results from trials to a target population is informative for addressing external validity.
Flexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy-related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimate noncrossing, nonlinear quantile curves. We expand the conditional distribution function of the response in I-spline basis functions where the covariate-dependent coefficients are modeled using neural networks. By leveraging the approximation power of splines and neural networks, our model can approximate any continuous quantile function. Compared to existing models, our model estimates all rather than a finite subset of quantiles, scales well to high dimensions, and accounts for estimation uncertainty. While the model is arbitrarily flexible, interpretable marginal quantile effects are estimated using accumulative local effect plots and variable importance measures. A simulation study shows that our model can better recover quantiles of the response distribution when the data are sparse, and an analysis of birth weight data is presented.
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