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A number of sophisticated estimators of longitudinal effects have been proposed for estimating the intervention-specific mean outcome. However, there is a relative paucity of research comparing these methods directly to one another. In this study, we compare various approaches to estimating a causal effect in a longitudinal treatment setting using both simulated data and data measured from a human immunodeficiency virus cohort. Six distinct estimators are considered: (i) an iterated conditional expectation representation, (ii) an inverse propensity weighted method, (iii) an augmented inverse propensity weighted method, (iv) a double robust iterated conditional expectation estimator, (v) a modified version of the double robust iterated conditional expectation estimator, and (vi) a targeted minimum loss-based estimator. The details of each estimator and its implementation are presented along with nuisance parameter estimation details, which include potentially pooling the observed data across all subjects regardless of treatment history and using data adaptive machine learning algorithms. Simulations are constructed over six time points, with each time point steadily increasing in positivity violations. Estimation is carried out for both the simulations and applied example using each of the six estimators under both stratified and pooled approaches of nuisance parameter estimation. Simulation results show that double robust estimators remained without meaningful bias as long as at least one of the two nuisance parameters were estimated with a correctly specified model. Under full misspecification, the bias of the double robust estimators remained better than that of the inverse propensity estimator under misspecification, but worse than the iterated conditional expectation estimator. Weighted estimators tended to show better performance than the covariate estimators. As positivity violations increased, the mean squared error and bias of all estimators considered became worse, with covariate-based double robust estimators especially susceptible. Applied analyses showed similar estimates at most time points, with the important exception of the inverse propensity estimator which deviated markedly as positivity violations increased. Given its efficiency, ability to respect the parameter space, and observed performance, we recommend the pooled and weighted targeted minimum loss-based estimator.

In conducting studies on an exposure of interest, a systematic roadmap should be applied for translating causal questions into statistical analyses and interpreting the results. In this paper we describe an application of one such roadmap applied to estimating the joint effect of both time to availability of a nurse-based triage system (low risk express care (LREC)) and individual enrollment in the program among HIV patients in East Africa. Our study population is comprised of 16,513 subjects found eligible for this task-shifting program within 15 clinics in Kenya between 2006 and 2009, with each clinic starting the LREC program between 2007 and 2008. After discretizing follow-up into 90-day time intervals, we targeted the population mean counterfactual outcome (i. e. counterfactual probability of either dying or being lost to follow up) at up to 450 days after initial LREC eligibility under three fixed treatment interventions. These were (i) under no program availability during the entire follow-up, (ii) under immediate program availability at initial eligibility, but non-enrollment during the entire follow-up, and (iii) under immediate program availability and enrollment at initial eligibility. We further estimated the controlled direct effect of immediate program availability compared to no program availability, under a hypothetical intervention to prevent individual enrollment in the program. Targeted minimum loss-based estimation was used to estimate the mean outcome, while Super Learning was implemented to estimate the required nuisance parameters. Analyses were conducted with the ltmle R package; analysis code is available at an online repository as an R package. Results showed that at 450 days, the probability of in-care survival for subjects with immediate availability and enrollment was 0.93 (95% CI: 0.91, 0.95) and 0.87 (95% CI: 0.86, 0.87) for subjects with immediate availability never enrolling. For subjects without LREC availability, it was 0.91 (95% CI: 0.90, 0.92). Immediate program availability without individual enrollment, compared to no program availability, was estimated to slightly albeit significantly decrease survival by 4% (95% CI 0.03,0.06, p

ObjectivesTenofovir disoproxil fumarate (TDF) has been associated with renal insufficiency. Co-administration with boosted protease inhibitors, which increases its exposure, may further increase the risk of renal insufficiency.MethodsWe compared the incidence of renal events among women taking TDF co-administered with lopinavir/ritonavir (LPV/r) versus those co-administering TDF with nevirapine (NVP). Renal events were defined as a confirmed drop in creatinine clearance associated with a serum creatinine grade 2 or higher, or that leading to treatment modification.ResultsOverall, 741 HIV-infected women were enrolled into the study. Of these, 24 (3.2%) had reportable renal events (18 in LPV/r arm, six in NVP arm). In multivariate analysis, renal events were significantly associated with the LPV/r arm [odds ratio (OR) 3.12, 95% confidence interval (CI) 1.21, 8.05; P = 0.019], baseline HIV-1 RNA (OR 2.65, 95% CI 1.23, 5.69 per 1 log10 copies/ml higher; P = 0.013) and baseline creatinine clearance (OR 0.83, 95% CI 0.70-0.98 per 10 ml/min higher; P = 0.030). In multivariate analysis evaluating renal events requiring treatment modification, only baseline HIV-1 RNA and creatinine clearance were significantly associated (OR 4.41, 95% CI 1.65, 11.78 per 1 log10 copies/ml higher; P = 0.003 and OR 0.80, 95% CI 0.64, 0.99 per 10 ml/min higher; P = 0.040, respectively).ConclusionThe rates of renal events were relatively low in the two treatment arms. However, patients taking TDF co-administered with LPV/r had significantly more renal events compared to those co-administered with NVP. Furthermore, higher baseline HIV RNA and lower creatinine clearance were associated with the development of renal insufficiency requiring treatment modification.

HIV protease inhibitors (PIs) show antimalarial activity in vitro and in animals. Whether this translates into a clinical benefit in HIV-infected patients residing in malaria-endemic regions is unknown. We studied the incidence of malaria, as defined by blood smear positivity or a positive Plasmodium falciparum histidine-rich protein 2 antigen test, among 444 HIV-infected women initiating antiretroviral treatment (ART) in the OCTANE trial (A5208; ClinicalTrials.gov: NCT00089505). Participants were randomized to treatment with PI-containing vs. PI-sparing ART, and were followed prospectively for ≥48 weeks; 73% also received cotrimoxazole prophylaxis. PI-containing treatment was not associated with protection against malaria in this study population.

HIV protease inhibitors (PIs) show antimalarial activity in vitro and in animals. Whether this translates into a clinical benefit in HIV-infected patients residing in malaria-endemic regions is unknown. We studied the incidence of malaria, as defined by blood smear positivity or a positive Plasmodium falciparum histidine-rich protein 2 antigen test, among 444 HIV-infected women initiating antiretroviral treatment (ART) in the OCTANE trial (A5208; ClinicalTrials.gov: NCT00089505). Participants were randomized to treatment with PI-containing vs. PI-sparing ART, and were followed prospectively for ≥48 weeks; 73% also received cotrimoxazole prophylaxis. PI-containing treatment was not associated with protection against malaria in this study population.