by
Subrata Sarkar;
Seetha Shankaran;
John Barks;
Barbara T. Do;
Abbot R. Laptook;
Abhik Das;
Namasivayam Ambalavanan;
Krisa P. Van Meurs;
Edward F. Bell;
Pablo J. Sanchez;
Susan R. Hintz;
Myra H. Wyckoff;
Barbara Stoll;
Waldemar A. Carlo;
David Carlton;
Ira Adams-Chapman
Objective: To determine the outcome of preterm infants whose cystic periventricular leukomalacia “disappeared” on serial screening cranial imaging studies. Study design: Infants ≤26 weeks of gestation born between 2002 and 2012 who had cranial imaging studies at least twice, the most abnormal study at <28 days of age and another closest to 36 weeks, were reviewed. The outcome of late death (after 36 weeks postmenstrual age) or neurodevelopmental impairment (NDI) in surviving infants at 18-26 months corrected age was compared between the infants with no cystic periventricular leukomalacia on both studies and cystic periventricular leukomalacia that disappeared (cystic periventricular leukomalacia at <28 days but not at 36 weeks), persisted (cystic periventricular leukomalacia on both studies), or appeared late (cystic periventricular leukomalacia only at 36 weeks). Predictors of NDI were evaluated by logistic regression. Results: Of 7063 eligible infants, 433 (6.1%) had cystic periventricular leukomalacia. Among the 433 infants with cystic periventricular leukomalacia, cystic periventricular leukomalacia disappeared in 76 (18%), persisted in 87 (20%), and 270 (62%) had late cystic periventricular leukomalacia. Loss to follow-up ranged between 3% and 13%. Death or NDI was more common in infants with disappeared cystic periventricular leukomalacia compared with those with no cystic periventricular leukomalacia (38 of 72 [53%] vs 1776 of 6376 [28%]; OR [95% CI] 2.8 [1.8-4.6]). Disappeared, persistent, and late cystic periventricular leukomalacia were all also independently associated with NDI (OR 1.17, 1.21, and 1.16, respectively). Conclusions: Infants with “disappeared” cystic periventricular leukomalacia are at increased risk of adverse outcome similar to infants with persistent or late cystic periventricular leukomalacia.
BACKGROUND
Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy. This report describes the survival of children with ALL in the United States using the most comprehensive and up‐to‐date cancer registry data.
METHODS
Data from 37 state cancer registries that cover approximately 80% of the US population were used. Age‐standardized survival up to 5 years was estimated for children aged 0‐14 years who were diagnosed with ALL during 2 periods (2001‐2003 and 2004‐2009).
RESULTS
In total, 17,500 children with ALL were included. The pooled age‐standardized net survival estimates for all US registries combined were 95% at 1 year, 90% at 3 years, and 86% at 5 years for children diagnosed during 2001‐2003, and 96%, 91%, and 88%, respectively, for those diagnosed during 2004‐2009. Black children who were diagnosed during 2001‐2003 had lower 5‐year survival (84%) than white children (87%) and had less improvement in survival by 2004‐2009. For those diagnosed during 2004‐2009, the 1‐year and 5‐year survival estimates were 96% and 89%, respectively, for white children and 96% and 84%, respectively, for black children. During 2004‐2009, survival was highest among children aged 1 to 4 years (95%) and lowest among children aged <1 year (60%).
CONCLUSIONS
The current results indicate that overall net survival from childhood ALL in the United States is high, but disparities by race still exist, especially beyond the first year after diagnosis. Clinical and public health strategies are needed to improve health care access, clinical trial enrollment, treatment, and survivorship care for children with ALL.
Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal.
BACKGROUND: Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data.
OBJECTIVES: We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.
METHODS: We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.
RESULTS: The overall model cross-validation R2 and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R2 = 0.73, regression slope = 0.91) and seasonal (R2 = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m3 between 2004 and 2007 and a decrease of 0.46 μg/m3 between 2008 and 2013.
CONCLUSIONS: Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.
Estimation of longitudinal models of relationship status between all pairs of individuals (dyads) in social networks is challenging due to the complex inter-dependencies among observations and lengthy computation times. To reduce the computational burden of model estimation, a method is developed that subsamples the "always-null" dyads in which no relationships develop throughout the period of observation. The informative sampling process is accounted for by weighting the likelihood contributions of the observations by the inverses of the sampling probabilities. This weighted-likelihood estimation method is implemented using Bayesian computation and evaluated in terms of its bias, efficiency, and speed of computation under various settings. Comparisons are also made to a full information likelihood-based procedure that is only feasible to compute when limited follow-up observations are available. Calculations are performed on two real social networks of very different sizes. The easily computed weighted-likelihood procedure closely approximates the corresponding estimates for the full network, even when using low sub-sampling fractions. The fast computation times make the weighted-likelihood approach practical and able to be applied to networks of any size.
Event-free survival (EFS) is controversial as an end point for speeding approvals in newly diagnosed acute myeloid leukemia (AML). We aimed to examine the robustness of EFS, specifically timing of complete remission (CR) in defining induction failure and impact of hematopoietic cell transplantation (HCT). The study included 1884 untreated AML patients enrolled across 5 trials conducted through Alliance for Clinical Trials in Oncology using anthracycline and cytarabine induction chemotherapy. EFS was defined as time from randomization/registration to induction failure, relapse, or death. Three definitions of induction failure were evaluated: failure to achieve CR by 60 days after randomization/ registration, failure to achieve CR by the end of all protocol-defined induction courses, and failure to achieve CR by the end of all protocol-defined treatment. We considered either censoring or no censoring at time of non-protocol-mandated HCT. Although relapse and death are firm end points, the determination of induction failure was not consistent across studies. There was minimal impact of censoring at HCT on EFS estimates; however, median EFS estimates differed considerably based on the timing of CR in defining induction failure, with the magnitude of difference being large enough in most cases to lead to incorrect conclusions about efficacy in a single-arm trial, if the trial definition was not consistent with the definition used for the historical control. Timing of CR should be carefully examined in the historical control data used to guide the design of single-arm trials using EFS as the primary end point. Trials were registered at www.clinicaltrials.gov as #NCT00085124, #NCT00416598, # NCT00651261, #NCT01238211, and #NCT01253070.
Missing data are common in medical and social science studies and often pose a serious challenge in data analysis. Multiple imputation methods are popular and natural tools for handling missing data, replacing each missing value with a set of plausible values that represent the uncertainty about the underlying values. We consider a case of missing at random (MAR) and investigate the estimation of the marginal mean of an outcome variable in the presence of missing values when a set of fully observed covariates is available. We propose a new nonparametric multiple imputation (MI) approach that uses two working models to achieve dimension reduction and define the imputing sets for the missing observations. Compared with existing nonparametric imputation procedures, our approach can better handle covariates of high dimension, and is doubly robust in the sense that the resulting estimator remains consistent if either of the working models is correctly specified. Compared with existing doubly robust methods, our nonparametric MI approach is more robust to the misspecification of both working models; it also avoids the use of inverse-weighting and hence is less sensitive to missing probabilities that are close to 1. We propose a sensitivity analysis for evaluating the validity of the working models, allowing investigators to choose the optimal weights so that the resulting estimator relies either completely or more heavily on the working model that is likely to be correctly specified and achieves improved efficiency. We investigate the asymptotic properties of the proposed estimator, and perform simulation studies to show that the proposed method compares favorably with some existing methods in finite samples. The proposed method is further illustrated using data from a colorectal adenoma study.
When examining datasets of any dimensionality, researchers frequently aim to identify individual subsets (clusters) of objects within the dataset. The ubiquity of multidimensional data has motivated the replacement of user-guided clustering with fully automated clustering. The fully automated methods are designed to make clustering more accurate, standardized and faster. However, the adoption of these methods is still limited by the lack of intuitive visualization and cluster matching methods that would allow users to readily interpret fully automatically generated clusters. To address these issues, we developed a fully automated subset identification and characterization (SIC) pipeline providing robust cluster matching and data visualization tools for high-dimensional flow/mass cytometry (and other) data. This pipeline automatically (and intuitively) generates two-dimensional representations of high-dimensional datasets that are safe from the curse of dimensionality. This new approach allows more robust and reproducible data analysis,+ facilitating the development of new gold standard practices across laboratories and institutions.
PURPOSE: Data sharing creates potential cost savings, supports data aggregation, and facilitates reproducibility to ensure quality research; however, data from heterogeneous systems require retrospective harmonization. This is a major hurdle for researchers who seek to leverage existing data. Efforts focused on strategies for data interoperability largely center around the use of standards but ignore the problems of competing standards and the value of existing data. Interoperability remains reliant on retrospective harmonization. Approaches to reduce this burden are needed. METHODS: The Cancer Imaging Archive (TCIA) is an example of an imaging repository that accepts data from a diversity of sources. It contains medical images from investigators worldwide and substantial nonimage data. Digital Imaging and Communications in Medicine (DICOM) standards enable querying across images, but TCIA does not enforce other standards for describing nonimage supporting data, such as treatment details and patient outcomes. In this study, we used 9 TCIA lung and brain nonimage files containing 659 fields to explore retrospective harmonization for cross-study query and aggregation. It took 329.5 hours, or 2.3 months, extended over 6 months to identify 41 overlapping fields in 3 or more files and transform 31 of them. We used the Genomic Data Commons (GDC) data elements as the target standards for harmonization. RESULTS: We characterized the issues and have developed recommendations for reducing the burden of retrospective harmonization. Once we harmonized the data, we also developed a Web tool to easily explore harmonized collections. CONCLUSION: While prospective use of standards can support interoperability, there are issues that complicate this goal. Our work recognizes and reveals retrospective harmonization issues when trying to reuse existing data and recommends national infrastructure to address these issues.
The current goal of initial antiretroviral (ARV) therapy is suppression of plasma human immunodeficiency virus (HIV)-1 RNA levels to below 200 copies per milliliter. A proportion of HIV-infected patients who initiate antiretroviral therapy in clinical practice or antiretroviral clinical trials either fail to suppress HIV-1 RNA or have HIV-1 RNA levels rebound on therapy. Frequently, these patients have sustained CD4 cell counts responses and limited or no clinical symptoms and, therefore, have potentially limited indications for altering therapy which they may be tolerating well despite increased viral replication. On the other hand, increased viral replication on therapy leads to selection of resistance mutations to the antiretroviral agents comprising their therapy and potentially cross-resistance to other agents in the same class decreasing the likelihood of response to subsequent antiretroviral therapy. The optimal time to switch antiretroviral therapy to ensure sustained virologic suppression and prevent clinical events in patients who have rebound in their HIV-1 RNA, yet are stable, is not known. Randomized clinical trials to compare early versus delayed switching have been difficult to design and more difficult to enroll. In some clinical trials, such as the AIDS Clinical Trials Group (ACTG) Study A5095, patients randomized to initial antiretroviral treatment combinations, who fail to suppress HIV-1 RNA or have a rebound of HIV-1 RNA on therapy are allowed to switch from the initial ARV regimen to a new regimen, based on clinician and patient decisions. We delineate a statistical framework to estimate the effect of early versus late regimen change using data from ACTG A5095 in the context of two-stage designs.
In causal inference, a large class of doubly robust estimators are derived through semiparametric theory with applications to missing data problems. This class of estimators is motivated through geometric arguments and relies on large samples for good performance. By now, several authors have noted that a doubly robust estimator may be suboptimal when the outcome model is misspecified even if it is semiparametric efficient when the outcome regression model is correctly specified. Through auxiliary variables, two-stage designs, and within the contextual backdrop of our scientific problem and clinical study, we propose improved doubly robust, locally efficient estimators of a population mean and average causal effect for early versus delayed switching to second-line ARV treatment regimens. Our analysis of the ACTG A5095 data further demonstrates how methods that use auxiliary variables can improve over methods that ignore them. Using the methods developed here, we conclude that patients who switch within 8 weeks of virologic failure have better clinical outcomes, on average, than patients who delay switching to a new second-line ARV regimen after failing on the initial regimen. Ordinary statistical methods fail to find such differences. This article has online supplementary material.