Judith J. Lok

Undiagnosed malignancy in patients with deep vein thrombosis

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.

Long-term increase in CD4+ T-cell counts during combination antiretroviral therapy for HIV-1 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.

Simple radiographic parameter predicts fracturing in metastatic femoral bone lesions: results from a randomised trial

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=0.02). In 110 femoral metastases, an axial cortical involvement >30 mm significantly predicted fracturing (MV, P=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=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 < or =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 patients condition is limited, irradiation to a higher total dose.

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

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.

Statistical modeling of causal effects in continuous time

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.

The impact of age on the prognostic capacity of CD8 + T-cell activation during suppressive antiretroviral therapy

Objective:To assess whether CD8+ T-cell activation predicts risk of AIDS and non-AIDS morbidity during suppressive antiretroviral treatment (ART). Design:Posthoc 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 (%CD38+HLA-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/&mgr;l, 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.

Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference.

Abstract 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.

Impact of Time to Start Treatment Following Infection with Application to Initiating HAART in HIV‐Positive Patients

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.

CD4 trajectory adjusting for dropout among HIV-positive patients receiving combination antiretroviral therapy in an East African HIV care centre

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.

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.