Assessing Direct and Spillover Effects of Intervention Packages in Network-randomized Studies.

BACKGROUND: Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery. METHODS: We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the 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. RESULTS: 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 (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87). CONCLUSIONS: These methods will be useful for evaluating intervention packages in studies with network features.

Causal mediation analysis with mediator values below an assay limit.

Causal indirect and direct effects provide an interpretable method for decomposing the total effect of an exposure on an outcome into the indirect effect through a mediator and the direct effect through all other pathways. A natural choice for a mediator in a randomized clinical trial is the treatment's targeted biomarker. However, when the mediator is a biomarker, values can be subject to an assay lower limit. The mediator is affected by the treatment and is a putative cause of the outcome, so the assay lower limit presents a compounded problem in mediation analysis. We propose two approaches to estimate indirect and direct effects with a mediator subject to an assay limit: (1) extrapolation and (2) numerical optimization and integration of the observed likelihood. Since these estimation methods solely rely on the so-called Mediation Formula, they apply to most approaches to causal mediation analysis: natural, separable, and organic indirect, and direct effects. A simulation study compares the two estimation approaches to imputing with half the assay limit. Using HIV interruption study data from the AIDS Clinical Trials Group described in Li et al 2016, AIDS; Lok and Bosch 2021, Epidemiology, we illustrate our methods by estimating the organic/pure indirect effect of a hypothetical HIV curative treatment on viral suppression mediated by two HIV persistence measures: cell-associated HIV-RNA and single-copy plasma HIV-RNA.

Assessing Direct and Spillover Effects of Intervention Packages in Network-Randomized Studies.

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 adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive 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 (Rate Ratio = 0.61; 95% confidence interval= 0.43, 0.87). These methods will be useful to evaluate intervention packages in studies with network features.

Overall, Direct, Spillover, and Composite Effects of Components of a Peer-Driven Intervention Package on Injection Risk Behavior Among People Who Inject Drugs in the HPTN 037 Study.

We sought to disentangle effects of the components of a peer-education intervention on self-reported injection risk behaviors among people who inject drugs (n = 560) in Philadelphia, US. We examined 226 egocentric groups/networks randomized to receive (or not) the intervention. Peer-education training consisted of two components delivered to the intervention network index individual only: (1) an initial training and (2) "booster" training sessions during 6- and 12-month follow up visits. In this secondary data analysis, using inverse-probability-weighted log-binomial mixed effects models, we estimated the effects of the components of the network-level peer-education intervention upon subsequent risk behaviors. This included contrasting outcome rates if a participant is a network member [non-index] under the network exposure versus under the network control condition (i.e., spillover effects). We found that compared to control networks, among intervention networks, the overall rates of injection risk behaviors were lower in both those recently exposed (i.e., at the prior visit) to a booster (rate ratio [95% confidence interval]: 0.61 [0.46-0.82]) and those not recently exposed to it (0.81 [0.67-0.98]). Only the boosters had statistically significant spillover effects (e.g., 0.59 [0.41-0.86] for recent exposure). Thus, both intervention components reduced injection risk behaviors with evidence of spillover effects for the boosters. Spillover should be assessed for an intervention that has an observable behavioral measure. Efforts to fully understand the impact of peer education should include routine evaluation of spillover effects. To maximize impact, boosters can be provided along with strategies to recruit especially committed peer educators and to increase attendance at trainings. Clinical Trials Registration Clinicaltrials.gov NCT00038688 June 5, 2002.

RESUMEN: Intentamos desenmarañar los efectos de los componentes de una intervención de educación entre pares sobre los comportamientos de inyección de riesgo autorreportados entre personas que se inyectan drogas (n = 560; 226 grupos/redes egocéntricos(as)) aleatorizados(as) a recibir (o no) la intervención en Filadelfia, EUA. Dos componentes fueron administrados a índices de redes de intervención: una capacitación inicial y sesiones de "refuerzo" durante visitas de seguimiento. Usando modelos log-binomial de efectos mixtos ponderados por probabilidad inversa, estimamos los efectos de dichos componentes sobre los comportamientos de riesgo posteriores. Encontramos que en comparación con las redes control, en las redes de intervención, las tasas generales de comportamientos de inyección de riesgo fueron más bajas en ambas aquellas expuestas recientemente a un refuerzo (razón de tasas [intervalo de confianza del 95%]: 0.61 [0.46­0.82]) y aquellas no expuestas recientemente (0.81 [0.67­0.98]). Solamente los refuerzos tuvieron efectos derrame (i.e., contraste de las tasas de resultados si es miembro [no índice] de una red en una red con exposición reciente versus bajo la condición control) significativos (p. ej., 0.59 [0.41­0.86] para la exposición reciente).

The survival-incorporated median vs the median in the survivors or in the always-survivors: What are we measuring? and Why?

Many clinical studies evaluate the benefit of a treatment based on both survival and other continuous/ordinal clinical outcomes, such as quality of life scores. In these studies, when subjects die before the follow-up assessment, the clinical outcomes become undefined and are truncated by death. Treating outcomes as "missing" or "censored" due to death can be misleading for treatment effect evaluation. We show that if we use the median in the survivors or in the always-survivors as estimands to summarize clinical outcomes, we may conclude that a trade-off exists between the probability of survival and good clinical outcomes, even in settings where both the probability of survival and the probability of any good clinical outcome are better for one treatment. Therefore, we advocate not always treating death as a mechanism through which clinical outcomes are missing, but rather as part of the outcome measure. To account for the survival status, we describe the survival-incorporated median as an alternative summary measure for outcomes in the presence of death. The survival-incorporated median is the threshold such that 50% of the population is alive with an outcome above that threshold. Through conceptual examples and an application to a prostate cancer treatment study, we show that the survival-incorporated median provides a simple and useful summary measure to inform clinical practice.

Circulating neutrophil extracellular trap (NET)-forming 'rogue' neutrophil subset, immunotype [DEspR + CD11b +], mediate multi-organ failure in COVID-19-an observational study.

Background: Cumulative research show association of neutrophils and neutrophil extracellular traps (NETs) with poor outcomes in severe COVID-19. However, to date, there is no curative intent therapy able to block neutrophil/NETs-mediated progression of multi-organ dysfunction. Because of emerging neutrophil heterogeneity, the study of subsets of circulating NET-forming neutrophils [NET + Ns] as mediators of multi-organ failure progression among patients with COVID-19 is critical to identification of therapeutic targets. Methods: We conducted a prospective observational study of circulating levels of CD11b + [NET + N] immunotyped for dual endothelin-1/signal peptide receptor (DEspR ±) expression by quantitative immunofluorescence-cytology and causal mediation analysis. In 36 consented adults hospitalized with mod-severe COVID-19, May to September 2020, we measured acute multi-organ failure via SOFA-scores and respiratory failure via SaO2/FiO2 (SF)-ratio at time points t1 (average 5.5 days from ICU/hospital admission) and t2 (the day before ICU-discharge or death), and ICU-free days at day28 (ICUFD). Circulating absolute neutrophil counts (ANC) and [NET + N] subset-specific counts were measured at t1. Spearman correlation and causal mediation analyses were conducted. Results: Spearman correlation analyses showed correlations of t1-SOFA with t2-SOFA (rho r S = 0.80) and ICUFD (r S = -0.76); circulating DEspR + [NET + Ns] with t1-SOFA (r S = 0.71), t2-SOFA (r S = 0.62), and ICUFD (r S = -0.63), and ANC with t1-SOFA (r S = 0.71), and t2-SOFA (r S = 0.61).Causal mediation analysis identified DEspR + [NET + Ns] as mediator of 44.1% [95% CI:16.5,110.6] of the causal path between t1-SOFA (exposure) and t2-SOFA (outcome), with 46.9% [15.8,124.6] eliminated when DEspR + [NET + Ns] were theoretically reduced to zero. Concordantly, DEspR + [NET + Ns] mediated 47.1% [22.0,72.3%] of the t1-SOFA to ICUFD causal path, with 51.1% [22.8,80.4%] eliminated if DEspR + [NET + Ns] were reduced to zero. In patients with t1-SOFA > 1, the indirect effect of a hypothetical treatment eliminating DEspR + [NET + Ns] projected a reduction of t2-SOFA by 0.98 [0.29,2.06] points and ICUFD by 3.0 [0.85,7.09] days. In contrast, there was no significant mediation of SF-ratio through DEspR + [NET + Ns], and no significant mediation of SOFA-score through ANC. Conclusions: Despite equivalent correlations, DEspR + [NET + Ns], but not ANC, mediated progression of multi-organ failure in acute COVID-19, and its hypothetical reduction is projected to improve ICUFD. These translational findings warrant further studies of DEspR + [NET + Ns] as potential patient-stratifier and actionable therapeutic target for multi-organ failure in COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1186/s41231-023-00143-x.

Circulating neutrophil extracellular trap (NET)-forming 'rogue' neutrophil subset, immunotype [DEspR+CD11b+], mediate multi-organ failure in COVID-19 - an observational study.

Background: Cumulative research show association of neutrophils and neutrophil extracellular traps (NETs) with poor outcomes in severe COVID-19. However, to date, no curative intent therapy has been identified to block neutrophil/NETs-mediated progression of multi-organ dysfunction. Because of emerging neutrophil heterogeneity, the study of subsets of circulating neutrophil-extracellular trap (NET)-forming neutrophils [NET+Ns] as mediators of multi-organ failure progression among patients with COVID-19 is critical to identification of therapeutic targets. Methods: We conducted a prospective observational study of circulating levels of CD11b+[NET+N] immunotyped for dual endothelin-1/signal peptide receptor, (DEspR±) expression by quantitative immunofluorescence-cytology and causal mediation analysis. In 36 consented adults hospitalized with mod-severe COVID-19, May to September 2020, we measured acute multi-organ failure via SOFA-scores and respiratory failure via SaO2/FiO2 (SF)ratio at time points t1 (average 5.5 days from ICU/hospital admission) and t2 (the day before ICU-discharge or death), and ICU-free days at day28 (ICUFD). Circulating absolute neutrophil counts (ANC) and [NET+N] subset-specific counts were measured at t1. Spearman correlation and causal mediation analyses were conducted. Results: Spearman correlation analyses showed correlations of t1-SOFA with t2-SOFA ( rho r S =0.80) and ICUFD ( r S =-0.76); circulating DEspR+[NET+Ns] with t1-SOFA ( r S = 0.71), t2-SOFA ( r S =0.62), and ICUFD ( r S =-0.63), and ANC with t1-SOFA ( r S =0.71), and t2-SOFA ( r S =0.61). Causal mediation analysis identified DEspR+[NET+Ns] as mediator of 44.1% [95% CI:16.5,110.6] of the causal path between t1-SOFA (exposure) and t2-SOFA (outcome), with 46.9% [15.8,124.6] eliminated when DEspR+[NET+Ns] were theoretically reduced to zero. Concordantly, DEspR+[NET+Ns] mediated 47.1% [22.0,72.3%] of the t1-SOFA to ICUFD causal path, with 51.1% [22.8,80.4%] eliminated if DEspR+[NET+Ns] were reduced to zero. In patients with t1-SOFA >1, the indirect effect of a hypothetical treatment eliminating DEspR+[NET+Ns] projected a reduction of t2-SOFA by 0.98 [0.29,2.06] points and ICUFD by 3.0 [0.85,7.09] days. In contrast, there was no significant mediation of SF-ratio through DEspR+[NET+Ns], and no significant mediation of SOFA-score through ANC. Conclusions: Despite equivalent correlations, DEspR+[NET+Ns], but not ANC, mediated progression of multi-organ failure in acute COVID-19, and its hypothetical reduction is projected to improve ICUFD. These translational findings warrant further studies of DEspR+[NET+Ns] as potential patient-stratifier and actionable therapeutic target for multi-organ failure in COVID-19.

Predictive value of CD8+ T cell and CD4/CD8 ratio at two years of successful ART in the risk of AIDS and non-AIDS events.

BACKGROUND: While increased CD8 counts and low CD4/CD8 ratio during treated HIV correlate with immunosenescence, their additional predictive values to identify individuals with HIV at higher risk of clinical events remain controversial. METHODS: We selected treatment-naive individuals initiating ART from ACTG studies 384, 388, A5095, A5142, A5202, and A5257 who had achieved viral suppression at year 2. We examined the effect of CD8+ T cell counts and CD4/CD8 at year 2 on the probability of AIDS and serious non-AIDS events in years 3-7. We used inverse probability weighting methods to address informative censoring, combined with multivariable logistic regression models. FINDINGS: We analyzed 5133 participants with a median age of 38 years; 959 (19%) were female, pre-ART median CD4 counts were 249 (Q1-Q3 91-372) cell/µL. Compared to participants with CD8 counts between 500/µL and 1499/µL, those with >1500/µL had a higher risk of clinical events during years 3-7 (aOR 1.75; 95%CI 1.33-2.32). CD4/CD8 ratio was not predictive of greater risk of events through year 7. Additional analyses revealed consistent CD8 count effect sizes for the risk of AIDS events and noninfectious non-AIDS events, but opposite effects for the risk of severe infections, which were more frequent among individuals with CD8 counts <500/µL (aOR 1.70; 95%CI 1.09-2.65). INTERPRETATION: The results of this analysis with pooled data from clinical trials support the value of the CD8 count as a predictor of clinical progression. People with very high CD8 counts during suppressive ART might benefit from closer monitoring and may be a target population for novel interventions. FUNDING: This research was supported by NIH/NIAID awards UM1 AI068634, UM1 AI068636, and UM1 AI106701 and Carlos III Health Institute and FEDER funds (BA21/00017 and BA21/00022).

Causal Organic Indirect and Direct Effects: Closer to the Original Approach to Mediation Analysis, with a Product Method for Binary Mediators.

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.

ANALYSIS OF "LEARN-AS-YOU-GO" (LAGO) STUDIES.

In Learn-As-you-GO (LAGO) adaptive studies, the intervention is a complex multicomponent package, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. In LAGO study data, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory. We develop an estimator for the intervention effects, and 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 multicomponent intervention package.

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention.

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.

Randomized Trial Evaluating Clinical Impact of RAPid IDentification and Susceptibility Testing for Gram-negative Bacteremia: RAPIDS-GN.

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 < .001) and AST (13.5 [56] vs 44.9 [12.1] hours; P < .001). Median (interquartile range [IQR]) time to first antibiotic modification was faster in the RAPID arm vs the SOC arm for overall antibiotics (8.6 [2.6-27.6] vs 14.9 [3.3-41.1] hours; P = .02) and gram-negative antibiotics (17.3 [4.9-72] vs 42.1 [10.1-72] hours; P < .001). Median (IQR) time to antibiotic escalation was faster in the RAPID arm vs the SOC arm for antimicrobial-resistant BSIs (18.4 [5.8-72] vs 61.7 [30.4-72] hours; P = .01). There were no differences between the arms in patient outcomes. CONCLUSIONS: Rapid organism ID and phenotypic AST led to faster changes in antibiotic therapy for gram-negative BSIs. CLINICAL TRIALS REGISTRATION: NCT03218397.

Sequential, Multiple-Assignment, Randomized Trials for COMparing Personalized Antibiotic StrategieS (SMART-COMPASS).

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.

SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS.

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.

Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms.

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.

Colistin Versus Ceftazidime-Avibactam in the Treatment of Infections Due to Carbapenem-Resistant Enterobacteriaceae.

Background: The efficacy of ceftazidime-avibactam-a cephalosporin-ß-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 Impact of Statin and Angiotensin-Converting Enzyme Inhibitor/Angiotensin Receptor Blocker Therapy on Cognitive Function in Adults With Human Immunodeficiency Virus Infection.

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.

MIMICKING COUNTERFACTUAL OUTCOMES TO ESTIMATE CAUSAL EFFECTS.

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.

Fundamentals and Catalytic Innovation: The Statistical and Data Management Center of the Antibacterial Resistance Leadership Group.

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.

Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible.

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.