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Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component in the overall package effectiveness can improve intervention delivery. We propose an approach to evaluate the effects of a time-varying intervention package in a single study. In some network-randomized studies, only a subset of participants in exposed networks receives the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed themselves under intervention in the network versus no intervention in a control network. We estimated effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using Marginal Structural Models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant-visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (95% confidence interval (CI) = -57%, -13%). These methods will be useful for evaluation of intervention packages in studies with network features.

Objectives The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. Results We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

Mediation analysis, which started in the mid-1980s, is used extensively by applied researchers. Indirect and direct effects are the part of a treatment effect that is mediated by a covariate and the part that is not. Subsequent work on natural indirect and direct effects provides a formal causal interpretation, based on cross-worlds counterfactuals: outcomes under treatment with the mediator set to its value without treatment. Organic indirect and direct effects avoid cross-worlds counterfactuals, using so-called organic interventions on the mediator while keeping the initial treatment fixed at treatment. Organic indirect and direct effects apply also to settings where the mediator cannot be set. In linear models where the outcome model does not have treatment-mediator interaction, both organic and natural indirect and direct effects lead to the same estimators as in the original formulation of mediation analysis. Here, we generalize organic interventions on the mediator to include interventions combined with the initial treatment fixed at no treatment. We show that the product method holds in linear models for organic indirect and direct effects relative to no treatment even if there is treatment-mediator interaction. Moreover, we find a product method for binary mediators. Furthermore, we argue that the organic indirect effect relative to no treatment is very relevant for drug development. We illustrate the benefits of our approach by estimating the organic indirect effect of curative HIV treatments mediated by two HIV persistence measures, using data on interruption of antiretroviral therapy without curative HIV treatments combined with an estimated or hypothesized effect of the curative HIV treatments on these mediators. See video abstract at http://links.lww.com/EDE/B796.

Background Rapid blood culture diagnostics are of unclear benefit for patients with gram-negative bacilli (GNB) bloodstream infections (BSIs). We conducted a multicenter, randomized, controlled trial comparing outcomes of patients with GNB BSIs who had blood culture testing with standard-of-care (SOC) culture and antimicrobial susceptibility testing (AST) vs rapid organism identification (ID) and phenotypic AST using the Accelerate Pheno System (RAPID). Methods Patients with positive blood cultures with Gram stains showing GNB were randomized to SOC testing with antimicrobial stewardship (AS) review or RAPID with AS. The primary outcome was time to first antibiotic modification within 72 hours of randomization. Results Of 500 randomized patients, 448 were included (226 SOC, 222 RAPID). Mean (standard deviation) time to results was faster for RAPID than SOC for organism ID (2.7 [1.2] vs 11.7 [10.5] hours; P Conclusions Rapid organism ID and phenotypic AST led to faster changes in antibiotic therapy for gram-negative BSIs. Clinical Trials Registration NCT03218397.

Background Although statins, angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) are generally well tolerated, the impact of these therapies individually or in combination on the change in neurocognitive function in persons with human immunodeficiency virus infection is unknown. Methods The study included participants in the AIDS Clinical Trials Group Longitudinal Linked Randomized Trials cohort participants not receiving a statin or ACEI/ARB within 30 days of first neurologic assessment (baseline), with assessments by NPZ-3 (z score of averaged Trailmaking A and B tests and digit symbol test [DST]) from ≥2 measurements. Marginal structural models estimated the causal effect of statin or ACEI/ARB initiation on neurocognitive function; initial constant slope was assumed during the first year of treatment and a second constant slope thereafter. Results Of 3949 eligible participants, 16% started therapy with a statin, 11% with an ACEI/ARB, and 5% with both. Statin therapy had no significant effect on the composite NPZ-3 (primary outcome), Trailmaking B test, or DST. A small, nonsignificant positive effect on the Trailmaking A test was seen during year 1 (estimate, 0.088; 95% confidence interval, -.010 to .187; P = .08) and a small but significant negative effect (-0.033; -.058 to -.009; P = .007) in each subsequent year. ACEI/ARB therapy had a significant negative effect on the DST (-0.117; 95% confidence interval, -.217 to .016; P = .02) during year 1 but minimal effect in subsequent years or on other neurocognitive domains. Conclusions In summary, although modest declines in neurocognitive performance were seen in single domains with statin or ACEI/ARB therapy, we did not find consistent evidence that statins or ACEI/ARB have an effect on global neurocognitive function. Future studies should focus on long-term neurocognitive effects.

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.

Patient management is not based on a single decision. Rather, it is dynamic: based on a sequence of decisions, with therapeutic adjustments made over time. Adjustments are personalized: tailored to individual patients as new information becomes available. However, strategies allowing for such adjustments are infrequently studied. Traditional antibiotic trials are often nonpragmatic, comparing drugs for definitive therapy when drug susceptibilities are known. COMparing Personalized Antibiotic StrategieS (COMPASS) is a trial design that compares strategies consistent with clinical practice. Strategies are decision rules that guide empiric and definitive therapy decisions. Sequential, multiple-assignment, randomized (SMART) COMPASS allows evaluation when there are multiple, definitive therapy options. SMART COMPASS is pragmatic, mirroring clinical, antibiotic-treatment decision-making and addressing the most relevant issue for treating patients: identification of the patient-management strategy that optimizes the ultimate patient outcomes. SMART COMPASS is valuable in the setting of antibiotic resistance, when therapeutic adjustments may be necessary due to resistance.

Coarse structural nested mean models are tools for estimating treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified over-identification restrictions tests for evaluating a treatment effect model, and show that our test is doubly robust in the sense that, with a correct treatment effect model, the test has the correct Type I error if either the treatment initiation model or a nuisance regression outcome model is correctly specified. In a simulation study, we show that the test has correct Type I error and can detect model misspecification. We use the test to study how the timing of antiretroviral treatment initiation after HIV infection predicts the effect of one year of treatment in HIV-positive patients with acute and early infection.

This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios.

We propose a prediction model for the cumulative incidence functions of competing risks, based on a logit link. Because of a concern about censoring potentially depending on time-varying covariates in our motivating human immunodeficiency virus (HIV) application, we describe an approach for estimating the parameters in the prediction models using inverse probability of censoring weighting under a missingness at random assumption. We then illustrate the application of this methodology to identify predictors of the competing outcomes of virologic failure, an efficacy outcome, and treatment limiting adverse event, a safety outcome, among human immunodeficiency virus-infected patients first starting antiretroviral treatment. Copyright © 2016 John Wiley & Sons, Ltd.

Coarse Structural Nested Mean Models (SNMMs, Robins (2000)) and G-estimation can be used to estimate the causal effect of a time-varying treatment from longitudinal observational studies. However, they rely on an untestable assumption of no unmeasured confounding. In the presence of unmeasured confounders, the unobserved potential outcomes are not missing at random, and standard G-estimation leads to biased effect estimates. To remedy this, we investigate the sensitivity of G-estimators of coarse SNMMs to unmeasured confounding, assuming a nonidentifiable bias function which quantifies the impact of unmeasured confounding on the average potential outcome. We present adjusted G-estimators of coarse SNMM parameters and prove their consistency, under the bias modeling for unmeasured confounding. We apply this to a sensitivity analysis for the effect of the ART initiation time on the mean CD4 count at year 2 after infection in HIV-positive patients, based on the prospective Acute and Early Disease Research Program.

In learn-as-you-go (LAGO) adaptive studies, the intervention is a complex package consisting of multiple components, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice, and desires for practice, in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. When analyzing data from a learn-as-you-go study, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory. We develop methods for estimating the intervention effects in a LAGO study. We prove consistency and asymptotic normality using a novel coupling argument, ensuring the validity of the test for the hypothesis of no overall intervention effect. We develop a confidence set for the optimal intervention package and confidence bands for the success probabilities under alternative package compositions. We illustrate our methods in the BetterBirth Study, which aimed to improve maternal and neonatal outcomes among 157,689 births in Uttar Pradesh, India through a complex, multi-component intervention package.

In observational studies, treatment may be adapted to covariates at several times without a fixed protocol, in continuous time. Treatment influences covariates, which influence treatment, which influences covariates, and so on. Then even time-dependent Cox-models cannot be used to estimate the net treatment effect. Structural nested models have been applied in this setting. Structural nested models are based on counterfactuals: the outcome a person would have had had treatment been withheld after a certain time. Previous work on continuous-time structural nested models assumes that counterfactuals depend deterministically on observed data, while conjecturing that this assumption can be relaxed. This article proves that one can mimic counterfactuals by constructing random variables, solutions to a differential equation, that have the same distribution as the counterfactuals, even given past observed data. These "mimicking" variables can be used to estimate the parameters of structural nested models without assuming the treatment effect to be deterministic.

Background The efficacy of ceftazidime-avibactam-a cephalosporin-β-lactamase inhibitor combination with in vitro activity against Klebsiella pneumoniae carbapenemase-producing carbapenem-resistant Enterobacteriaceae (CRE)-compared with colistin remains unknown. Methods Patients initially treated with either ceftazidime-avibactam or colistin for CRE infections were selected from the Consortium on Resistance Against Carbapenems in Klebsiella and other Enterobacteriaceae (CRACKLE), a prospective, multicenter, observational study. Efficacy, safety, and benefit-risk analyses were performed using intent-to-treat analyses with partial credit and the desirability of outcome ranking approaches. The ordinal efficacy outcome was based on disposition at day 30 after starting treatment (home vs not home but not observed to die in the hospital vs hospital death). All analyses were adjusted for confounding using inverse probability of treatment weighting (IPTW). Results Thirty-eight patients were treated first with ceftazidime-avibactam and 99 with colistin. Most patients received additional anti-CRE agents as part of their treatment. Bloodstream (n = 63; 46%) and respiratory (n = 30; 22%) infections were most common. In patients treated with ceftazidime-avibactam versus colistin, IPTW-adjusted all-cause hospital mortality 30 days after starting treatment was 9% versus 32%, respectively (difference, 23%; 95% bootstrap confidence interval, 9%-35%; P = .001). In an analysis of disposition at 30 days, patients treated with ceftazidime-avibactam, compared with those treated within colistin, had an IPTW-adjusted probability of a better outcome of 64% (95% confidence interval, 57%-71%). Partial credit analyses indicated uniform superiority of ceftazidime-avibactam to colistin. Conclusions Ceftazidime-avibactam may be a reasonable alternative to colistin in the treatment of K. pneumoniae carbapenemase-producing CRE infections. These findings require confirmation in a randomized controlled trial.

The Statistical and Data Management Center (SDMC) provides the Antibacterial Resistance Leadership Group (ARLG) with statistical and data management expertise to advance the ARLG research agenda. The SDMC is active at all stages of a study, including design; data collection and monitoring; data analyses and archival; and publication of study results. The SDMC enhances the scientific integrity of ARLG studies through the development and implementation of innovative and practical statistical methodologies and by educating research colleagues regarding the application of clinical trial fundamentals. This article summarizes the challenges and roles, as well as the innovative contributions in the design, monitoring, and analyses of clinical trials and diagnostic studies, of the ARLG SDMC.

We estimate how the effect of antiretroviral treatment depends on the time from HIV-infection to initiation of treatment, using observational data. A major challenge in making inferences from such observational data arises from biases associated with the nonrandom assignment of treatment, for example bias induced by dependence of time of initiation on disease status. To address this concern, we develop a new class of Structural Nested Mean Models (SNMMs) to estimate the impact of time of initiation of treatment after infection on an outcome measured a fixed duration after initiation, compared to the effect of not initiating treatment. This leads to a SNMM that models the effect of multiple dosages of treatment on a time-dependent outcome, in contrast to most existing SNNMs, which focus on the effect of one dosage of treatment on an outcome measured at the end of the study. Our identifying assumption is that there are no unmeasured confounders. We illustrate our methods using the observational Acute Infection and Early Disease Research Program (AIEDRP) Core01 database on HIV. The current standard of care in HIV-infected patients is Highly Active Anti-Retroviral Treatment (HAART); however, the optimal time to start HAART has not yet been identified. The new class of SNNMs allows estimation of the dependence of the effect of 1 year of HAART on the time between estimated date of infection and treatment initiation, and on patient characteristics. Results of fitting this model imply that early use of HAART substantially improves immune reconstitution in the early and acute phase of HIV-infection.

Objective: To inform guidelines concerning when to initiate combination antiretroviral therapy (ART), we investigated whether CD4 + T-cell counts (CD4 cell counts) continue to increase over long periods of time on ART. Losses-to-follow-up and some patients discontinuing ART at higher CD4 cell counts hamper such evaluation, but novel statistical methods can help address these issues. We estimated the long-term CD4 cell count trajectory accounting for losses-to-follow-up and treatment discontinuations. Design: The study population included 898 US patients first initiating ART in a randomized trial (AIDS Clinical Trials Group 384); 575 were subsequently prospectively followed in an observational study (AIDS Clinical Trials Group Longitudinal Linked Randomized Trials). Methods: Inverse probability of censoring weighting statistical methods were used to estimate the CD4 cell count trajectory accounting for losses-to-follow-up and ART discontinuations, overall and for pretreatment CD4 cell count categories (≤200, 201-350, 351-500, and >500 cells/μl). Results: Median CD4 cell count increased from 270 cells/μl pre-ART to an estimated 556 cells/μl at 3 and 532 cells/μl at 7 years after starting ART in analyses ignoring treatment discontinuations, and to 570 and 640 cells/μl, respectively, had all patients continued ART. However, even had ART been continued, an estimated 25, 9, 3, and 2% of patients with pretreatment CD4 cell counts of 200 or less, 201-350, 351-500, and more than 500 cells/μl would have had CD4 cell counts of 350 cells/μl or less after 7 years. Conclusion: If patients remain on ART, CD4 cell counts increase in most patients for at least 7 years. However, the substantial percentage of patients starting therapy at low CD4 cell counts who still had low CD4 cell counts after 7 years provides support for ART initiation at higher CD4 cell counts.

In observational studies treatment may be adapted to the patient's state during the course of time. These covariates may in turn also react on the treatment under study, and so on. This makes it hard to distinguish between treatment effect and selection bias. Structural nested models aim at estimating treatment effect in such complicated situations, even when treatment may change at any time. We show that structural nested models can often be calculated with standard software, by using standard models to predict treatment as a tool to estimate treatment effect. Robins ( Survival analysis, Volume 6 of Encyclopedia of Biostatistics , John Wiley and Sons, Chich- ester, 1998) conjectured this, but so far it was unproven. We use a partial likelihood approach to choose the estimators and tests as a subclass of the estimators and tests in Lok (math. ST/0410271 at http://arXiv.org, 2004). We show that this is the class of estimators and tests that can be calcu- lated with standard software. The estimators are consistent and asymptotically normal, and have interesting asymptotic properties.

Natural direct and indirect effects decompose the effect of a treatment into the part that is mediated by a covariate (the mediator) and the part that is not. Their definitions rely on the concept of outcomes under treatment with the mediator 'set' to its value without treatment. Typically, the mechanism through which the mediator is set to this value is left unspecified, and in many applications, it may be challenging to fix the mediator to particular values for each unit or patient. Moreover, how one sets the mediator may affect the distribution of the outcome. This article introduces 'organic' direct and indirect effects, which can be defined and estimated without relying on setting the mediator to specific values. Organic direct and indirect effects can be applied, for example, to estimate how much of the effect of some treatments for HIV/AIDS on mother-to-child transmission of HIV infection is mediated by the effect of the treatment on the HIV viral load in the blood of the mother. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for the situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time-dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so-called time-dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time-varying treatment, and present methods for estimating and testing the parameters of these models.

Objective: Estimates of CD4 response to antiretroviral therapy (ART) obtained by averaging data from patients in care, overestimate population CD4 response and treatment program effectiveness because they do not consider data from patients who are deceased or not in care.We use mathematical methods to assess and adjust for this bias based on patient characteristics. Design: We examined data from 25,261 HIV-positive patients from the East Africa IeDEA Consortium. Methods: We used inverse probability of censoring weighting (IPCW) to represent patients not in care by patients in care with similar characteristics. We address two questions: What would the median CD4 be ''had everyone starting ART remained on observation?'' and ''were everyone starting ART maintained on treatment?'' Results: Routine CD4 count estimates were higher than adjusted estimates even under the best-case scenario of maintaining all patients on treatment. Two years after starting ART, differences between estimates diverged from 30 cells/mL, assuming similar mortality and treatment access among dropouts as patients in care, to over 100 cells/mL assuming 20% lower survival and 50% lower treatment access among dropouts. When considering only patients in care, the proportion of patients with CD4 above 350 cells/mL was 50% adjusted to below 30% when accounting for patients not in care. One-year mortality diverged 614% from the nao¨ve estimates depending on assumptions about access to care among lost patients. Conclusions: Ignoring mortality and loss to care results in over-estimation of ART response for patients starting treatment and exaggerates the efficacy of treatment programs administering it.

BACKGROUND The reported incidence of a subsequent diagnosis of malignancy in patients presenting with deep vein thrombosis (DVT) varies from 2-25%. Risk indicators and diagnostic procedures to be performed in these patients are controversial. METHODS Four hundred consecutive patients with confirmed DVT included in a randomized clinical trial were followed prospectively for 6 months. The incidence of a subsequent diagnosis of malignancy was calculated and compared between patients with unexplained DVT and patients with secondary DVT. Potential risk indicators for subsequent malignant disease were evaluated. RESULTS Of the 400 patients, 70 already had been diagnosed with malignancy; another four patients were lost to follow-up. Of the remaining 326 patients, 10 new malignancies were diagnosed among 137 patients with unexplained DVT (7.3%) and 3 new malignancies were diagnosed in 189 patients with secondary DVT (1.6%). The relative risk was 4.6 (95% confidence interval, 1.3-16; P = 0.009). Age, gender, or location of the DVT had no significant effect on the incidence of diagnosis when adjusted for unexplained DVT. Ten of these 13 patients (77%) had abnormal clinical findings suggestive of malignancy at the time of presentation with DVT. CONCLUSIONS Unexplained DVT is a significant risk indicator of underlying malignancy. The majority of patients with undiagnosed malignancy have some clinical abnormality suggestive of underlying malignancy at the time of presentation with unexplained DVT. A simple clinical evaluation comprised of medical history, physical examination, routine laboratory tests, and chest X-ray can detect such patients. Extensive screening of all patients presenting with unexplained DVT does not appear to be justified. Cancer 1998;83:180-185. © 1998 American Cancer Society.

Efavirenz (EFV) along with two nucleoside reverse transcriptase inhibitors (NRTIs) is a recommended initial antiretroviral regimen. Understanding characteristics related to EFV success is clinically useful.Data from 2220 antiretroviral-naive participants randomized to EFV and two to three NRTIs in four ACTG trials as well as a long-term cohort were analysed.Logistic regression, using inverse probability of censoring weighting to address selective-follow-up bias, was used to identify factors associated with EFV success (no treatment interruptions of30 days, HIV RNA 200 copies/ml) 1 year post initiation and at years 2-5 if successful at year 1.Pretreatment characteristics were median age 38 years, 82% male, 40% white, 10% history of IDU (HxIDU), median CD4+ T-lymphocyte 227 cells/μl and 33% HIV RNA more than 100 ,000 copies/ml. In a multivariable model, factors associated with year 1 EFV success were race [white odds ratio (OR) 1.5; P 0.001; Hispanic OR 1.5; P = 0.003 vs. black], no pretreatment sign/symptom grade 3 or higher (OR 1.7; P = 0.008) and no HxIDU (OR 1.7; P = 0.001). Predictors of EFV success at years 2-5 were no HxIDU (years 2-5; ORs 1.9-2.2); self-reported complete (4 days prior to study visit) adherence during year 1 (years 2-4; ORs 1.6-1.9); fewer missed visits during year 1 (years 2, 4, 5; ORs 0.92-0.98/1% increase); HIV RNA less than 50 copies/ml at year 1 (years 2, 3; ORs 1.9-2.2); and older age (50 vs. ≤30 years) (years 2-4: ORs 2.3-3.7).Characteristics predictive of EFV success in the short-term and longer term differed except for HxIDU. Behaviours occurring during year 1 were associated with EFV success over 5 years.

OBJECTIVE To assess whether CD8 T-cell activation predicts risk of AIDS and non-AIDS morbidity during suppressive antiretroviral treatment (ART). DESIGN Post-hoc analyses of ART-naive participants in prospective ART studies. Participants with HIV-RNA levels 200 copies/ml or less and CD8 T-cell activation data (%CD38HLA-DR) at year-1 of ART were selected to determine years 2-5 incidence of AIDS and non-AIDS events. METHODS We censored data at time of ART interruption or virologic failure. Inverse probability of censoring-weighted logistic regression was used to correct for informative censoring. RESULTS We included 1025 participants; 82% were men, median age 38 years, pre-ART CD4 cell count 255 cells/μl, and year-1-activated CD8 T cells 24%. Of these, 752 had 5 years of follow-up; 379 remained on ART and had no confirmed plasma HIV-RNA more than 200 copies/ml. The overall probability of an AIDS or non-AIDS event in years 2-5 was estimated at 13% [95% confidence interval (CI) 10-15%] had everyone remained on suppressive ART. Higher year-1-activated CD8 T-cell percentage increased the probability of subsequent events [odds ratio 1.22 per 10% higher (95% CI 1.04-1.44)]; this effect was not significant after adjusting for age. Among those age 50 years at least (n=108 at year 1), the probability of an event in years 2-5 was 37% and the effect of CD8 T-cell activation was more apparent (odds ratio=1.42, P=0.02 unadjusted and adjusted for age). CONCLUSION CD8 T-cell activation is prognostic of clinical events during suppressive ART, although this association is confounded by age. The consequences of HIV-associated immune activation may be more important in patients 50 years and older.

Recommended first-line antiretroviral therapy (ART) for human immunodeficiency virus (HIV) infection includes the use of two nucleoside or nucleotide reverse-transcriptase inhibitors (NRTIs) combined with one or more agents from other antiretroviral drug classes (nonnucleoside reverse-transcriptase inhibitors [NNRTIs], protease inhibitors [PIs], or integrase inhibitors) [1, 2]. Lifelong treatment is required to maintain HIV suppression, improve immune function, and minimize morbidity and mortality associated with HIV disease progression. Therefore, understanding long-term risks associated with ART is paramount. The observational Data Collection on Adverse events of Anti-HIV Drugs (D:A:D) cohort reported that current or recent use of abacavir was associated with increased risk of myocardial infarction (MI) [3]; data from other cohort studies and clinical trials of abacavir have provided conflicting results [4–11]. For example, although analyses of the Strategies for Management of Antiretroviral Therapy (SMART) study found more than a 4-fold increase in risk [4], a GlaxoSmithKline meta-analysis of randomized trials found no increased risk [5] and an association detected in the French Agence Nationale de Recherches sur le SIDA (ANRS) cohort was not apparent after cocaine and intravenous drug users were excluded from the analysis cohort [10]. These differences may be explained by confounding and population differences, as well as differences in analytical approach. Because abacavir was considered an attractive ART for subjects with cardiovascular disease (CVD) risk [6,12–14], confounding is particularly problematic [15–17]. Recent treatment guidelines now caution against the use of abacavir in subjects with high CVD risk [1, 2], which will further confound analyses addressing this issue. Previous analyses have focused on the increased relative risk of MI associated with abacavir. When an event is rare—as is MI in this context—even very large relative risks may have limited population-level relevance, making estimation of absolute risk difference essential. We ascertained population level CVD risk associated with initiating ART with abacavir-containing regimens using a cohort of ART-naive persons randomly assigned ART through prospective clinical trials. Our unique cohort provides a means to address confounding and channeling bias issues of traditional observational cohorts. Our analytical approach and estimation of absolute risk places the CVD risk associated with abacavir for the treatment of ART-naive populations into a broader clinical context.

In longitudinal data arising from observational or experimental studies, dependent subject drop-out is a common occurrence. If the goal is estimation of the parameters of a marginal complete-data model for the outcome, biased inference will result from fitting the model of interest with only uncensored subjects. For example, investigators are interested in estimating a prognostic model for clinical events in HIV-positive patients, under the counterfactual scenario in which everyone remained on ART (when in reality, only a subset had). Inverse probability of censoring weighting (IPCW) is a popular method that relies on correct estimation of the probability of censoring to produce consistent estimation, but is an inefficient estimator in its standard form. We introduce sequentially augmented regression (SAR), an adaptation of the Bang and Robins (2005. Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962-972.) method to estimate a complete-data prediction model, adjusting for longitudinal missing at random censoring. In addition, we propose a closely related non-parametric approach using targeted maximum likelihood estimation (TMLE; van der Laan and Rubin, 2006. Targeted maximum likelihood learning. The International Journal of Biostatistics 2 (1), Article 11). We compare IPCW, SAR, and TMLE (implemented parametrically and with Super Learner) through simulation and the above-mentioned case study.

This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where treatment is repeatedly adapted to time-dependent patient characteristics. The treatment effect cannot be estimated by simply conditioning on these time-dependent patient characteristics, as they may themselves be indications of the treatment effect. This time-dependent confounding is common in observational studies. Robins [(1992) Biometrika 79 321--334, (1998b) Encyclopedia of Biostatistics 6 4372--4389] has proposed the so-called structural nested models to estimate treatment effects in the presence of time-dependent confounding. In this article we provide a conceptual framework and formalization for structural nested models in continuous time. We show that the resulting estimators are consistent and asymptotically normal. Moreover, as conjectured in Robins [(1998b) Encyclopedia of Biostatistics 6 4372--4389], a test for whether treatment affects the outcome of interest can be performed without specifying a model for treatment effect. We illustrate the ideas in this article with an example., Comment: Published in at http://dx.doi.org/10.1214/009053607000000820 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Contains fulltext : 58489.pdf (Publisher’s version ) (Closed access) BACKGROUND: Postoperative exercise therapy aims at recovering, as soon as possible, independence in the basic physical activities; but the type, intensity, and therefore the costs of the programs, vary widely. The aim of this study was to compare the effectiveness of a low frequency (once daily, not in the weekend) program with a high frequency (twice daily, including the weekend) one and to assess whether the latter would yield sufficient benefit for the patient to justify higher costs in material and personnel (physiotherapists) after uncomplicated coronary artery bypass graft (CABG) surgery. METHODS: Two-hundred and forty-six patients were randomly allocated to either a low or high frequency exercise program. Endpoints were the functional level as measured by the achievement of five activity milestones, the patient's independence (functional independence measures [FIM]) as assessed by a structured interview, the amount of daily physical activity (activity monitor), and patient satisfaction (questionnaire). Except for patient satisfaction, all measurements were done in the first week after surgery. RESULTS: Patients with the high frequency exercise program achieved functional milestones faster than patients with the low frequency exercise program (p = 0.007). The frequency of the exercise program had no influence on functional independence as measured with the FIM or quantity of physical activity. The satisfaction degree was greater in the high frequency group (p = 0.032), although the low frequency group was not dissatisfied. CONCLUSIONS: A high frequency exercise program leads to earlier performance of functional milestones and yields more satisfaction after uncomplicated CABG surgery and this should lead to an earlier discharge. On the other hand, if the shortage of physiotherapists remains unchanged or even increases, the low frequency program also yields excellent functional results, albeit at the cost of a somewhat longer hospital stay: but it would allow a sensible redistribution of the physiotherapists activity towards complicated and, therefore, more demanding patients.

Background and purpose: In the randomised Dutch Bone Metastasis Study on the palliative effect of a single fraction (SF) of 8 Gy versus six fractions of 4 Gy on painful bone metastases, 14 fractures occurred in 102 patients with femoral metastases. Purpose of the present study was to identify lesional risk factors for fracturing and to evaluate the influence of the treatment schedule. Material and methods: Pretreatment radiographs of femoral metastases were collected. Three observers separately measured the lesions and scored radiographic characteristics. Results: Ten fractures occurred after median 7 weeks in 44 SF patients (23%) and four after median 20 weeks in 58 multiple fraction patients (7%) (UV, P ¼ 0:02). In 110 femoral metastases, an axial cortical involvement .30 mm significantly predicted fracturing (MV, P ¼ 0:02). Twelve out of 14 fractured lesions and 40 out of 96 non-fractured metastases had an axial cortical involvement .30 mm (negative predictive value, 97%). When correcting for the axial cortical involvement, the treatment schedule was not predictive anymore (MV, P ¼ 0:07). Conclusions: Fracturing of the femur mostly depended on the amount of axial cortical involvement of the metastasis. We recommend to treat femoral metastases with an axial cortical involvement #30 mm with an SF of 8 Gy for relief of pain. If the axial cortical involvement is . 30 mm, prophylactic surgery should be performed to minimize the risk of pathological fracturing or, if the patient’s condition is limited, irradiation to a higher total dose. q 2003 Published by Elsevier Ireland Ltd.