Joshua Schwab

Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models.

Abstract This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.

Association of Implementation of a Universal Testing and Treatment Intervention With HIV Diagnosis, Receipt of Antiretroviral Therapy, and Viral Suppression in East Africa

Importance Antiretroviral treatment (ART) is now recommended for all HIV-positive persons. UNAIDS has set global targets to diagnose 90% of HIV-positive individuals, treat 90% of diagnosed individuals with ART, and suppress viral replication among 90% of treated individuals, for a population-level target of 73% of all HIV-positive persons with HIV viral suppression. Objective To describe changes in the proportions of HIV-positive individuals with HIV viral suppression, HIV-positive individuals who had received a diagnosis, diagnosed individuals treated with ART, and treated individuals with HIV viral suppression, following implementation of a community-based testing and treatment program in rural East Africa. Design, Setting, and Participants Observational analysis based on interim data from 16 rural Kenyan (n = 6) and Ugandan (n = 10) intervention communities in the SEARCH Study, an ongoing cluster randomized trial. Community residents who were 15 years or older (N = 77 774) were followed up for 2 years (2013-2014 to 2015-2016). HIV serostatus and plasma HIV RNA level were measured annually at multidisease health campaigns followed by home-based testing for nonattendees. All HIV-positive individuals were offered ART using a streamlined delivery model designed to reduce structural barriers, improve patient-clinician relationships, and enhance patient knowledge and attitudes about HIV. Main Outcomes and Measures Primary outcome was viral suppression (plasma HIV RNA<500 copies/mL) among all HIV-positive individuals, assessed at baseline and after 1 and 2 years. Secondary outcomes included HIV diagnosis, ART among previously diagnosed individuals, and viral suppression among those who had initiated ART. Results Among 77 774 residents (male, 45.3%; age 15-24 years, 35.1%), baseline HIV prevalence was 10.3% (7108 of 69 283 residents). The proportion of HIV-positive individuals with HIV viral suppression at baseline was 44.7% (95% CI, 43.5%-45.9%; 3464 of 7745 residents) and after 2 years of intervention was 80.2% (95% CI, 79.1%-81.2%; 5666 of 7068 residents), an increase of 35.5 percentage points (95% CI, 34.4-36.6). After 2 years, 95.9% of HIV-positive individuals had been previously diagnosed (95% CI, 95.3%-96.5%; 6780 of 7068 residents); 93.4% of those previously diagnosed had received ART (95% CI, 92.8%-94.0%; 6334 of 6780 residents); and 89.5% of those treated had achieved HIV viral suppression (95% CI, 88.6%-90.3%; 5666 of 6334 residents). Conclusions and Relevance Among individuals with HIV in rural Kenya and Uganda, implementation of community-based testing and treatment was associated with an increased proportion of HIV-positive adults who achieved viral suppression, along with increased HIV diagnosis and initiation of antiretroviral therapy. In these communities, the UNAIDS population-level viral suppression target was exceeded within 2 years after program implementation. Trial Registration clinicaltrials.gov Identifier: NCT01864683

Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring

Objective:Regular HIV RNA testing for all HIV-positive patients on antiretroviral therapy (ART) is expensive and has low yield since most tests are undetectable. Selective testing of those at higher risk of failure may improve efficiency. We investigated whether a novel analysis of adherence data could correctly classify virological failure and potentially inform a selective testing strategy. Design:Multisite prospective cohort consortium. Methods:We evaluated longitudinal data on 1478 adult patients treated with ART and monitored using the Medication Event Monitoring System (MEMS) in 16 US cohorts contributing to the MACH14 consortium. Because the relationship between adherence and virological failure is complex and heterogeneous, we applied a machine-learning algorithm (Super Learner) to build a model for classifying failure and evaluated its performance using cross-validation. Results:Application of the Super Learner algorithm to MEMS data, combined with data on CD4+ T-cell counts and ART regimen, significantly improved classification of virological failure over a single MEMS adherence measure. Area under the receiver operating characteristic curve, evaluated on data not used in model fitting, was 0.78 (95% confidence interval: 0.75 to 0.80) and 0.79 (95% confidence interval: 0.76 to 0.81) for failure defined as single HIV RNA level >1000 copies per milliliter or >400 copies per milliliter, respectively. Our results suggest that 25%–31% of viral load tests could be avoided while maintaining sensitivity for failure detection at or above 95%, for a cost savings of

On adaptive propensity score truncation in causal inference

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ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data

29 per person-month. Conclusions:Our findings provide initial proof of concept for the potential use of electronic medication adherence data to reduce costs through behavior-driven HIV RNA testing.

Robust variance estimation and inference for causal effect estimation.

The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.