Age of e-cigarette initiation in USA young adults: Findings from the Population Assessment of Tobacco and Health (PATH) study (2013–2017)

Introduction There is a lack of research prospectively estimating the age of e-cigarette initiation in U.S. young adults. Methods Secondary analysis of PATH young adults across 2013–2017 (waves 1–4) were conducted. We prospectively estimated age of initiation of: ever, past 30-day, and fairly regular e-cigarette use using weighted interval-censoring survival analyses. Interval-censoring Cox proportional hazard models adjusting for sex, race/ethnicity, and previous use of six other tobacco products (cigarettes, traditional cigars, filtered cigars, cigarillos, hookah, and smokeless tobacco) were fitted for each of the three e-cigarette initiation outcomes. Results Among never e-cigarette users, by age 21, 16.8% reported ever use, 7.2% reported past 30-day use, and 2.3% reported fairly regular e-cigarette use. Males had increased risk of initiating ever, past 30-day, and fairly regular e-cigarette use at earlier ages compared to females. Hispanic young adults had increased risk of initiating ever and past 30-day e-cigarette use at earlier ages compared to Non-Hispanic White young adults. Previous use of other tobacco products before e-cigarette initiation increased the risk of an earlier age of e-cigarette initiation. Conclusion Prevention and education campaigns should focus on young adults in order to alleviate the public health burden of initiating e-cigarette use at earlier ages.

A functional proportional hazard cure rate model for interval-censored data

Existing survival models involving functional covariates typically rely on the Cox proportional hazards structure and the assumption of right censorship. Motivated by the aim of predicting the time of conversion to Alzheimer’s disease from sparse biomarker trajectories in patients with mild cognitive impairment, we propose a functional mixture cure rate model with both functional and scalar covariates for interval censoring and sparsely sampled functional data. To estimate the nonparametric coefficient function that depicts the effect of the shape of the trajectories on the survival outcome and cure probability, we utilize the functional principal component analysis to extract the functional features from the sparsely and irregularly sampled trajectories. To obtain parameter estimates from the mixture cure rate model with interval censoring, we apply the expectation-maximization algorithm based on Poisson data augmentation. The estimation accuracy of our method is assessed via a simulation study and we apply our model on Alzheimer’s disease Neuroimaging Initiative data set.

Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring

This paper considers the problem of semi-parametric proportional hazards model fitting where observed survival times contain event times and also interval, left and right censoring times. Although this is not a new topic, many existing methods suffer from poor computational performance. In this paper, we adopt a more versatile penalized likelihood method to estimate the baseline hazard and the regression coefficients simultaneously. The baseline hazard is approximated using basis functions such as M-splines. A penalty is introduced to regularize the baseline hazard estimate and also to ease dependence of the estimates on the knots of the basis functions. We propose a Newton–MI (multiplicative iterative) algorithm to fit this model. We also present novel asymptotic properties of our estimates, allowing for the possibility that some parameters of the approximate baseline hazard may lie on the parameter space boundary. Comparisons of our method against other similar approaches are made through an intensive simulation study. Results demonstrate that our method is very stable and encounters virtually no numerical issues. A real data application involving melanoma recurrence is presented and an R package ‘survivalMPL’ implementing the method is available on R CRAN.

Age of initiation of hookah use among young adults: Findings from the Population Assessment of Tobacco and Health (PATH) study, 2013–2017

Objective To prospectively estimate the age of initiation of ever, past 30-day, and fairly regular hookah use among young adults (ages 18–24) overall, by sex, by race/ethnicity, and to explore the association of prior use of other tobacco products with these hookah use behaviors. Methods Secondary data analyses of the first four waves (2013–2017) of the PATH study, a nationally representative longitudinal cohort study of US young adults. Young adult never hookah users at the first wave of adult participation in PATH waves 1–3 (2013–2016) were followed-up into waves 2–4 (2014–2017) to estimate the age of initiation of three outcomes: (i) ever use, (ii) past 30-day use, and (iii) fairly regular hookah use. Weighted interval-censoring Cox proportional hazards regression models were used to examine the differences in the estimated age of initiation by sex and by race/ethnicity while controlling for the total number of other tobacco products ever used at participants’ first wave of PATH participation. In addition, to examine if prior use of other tobacco products was associated with the age of hookah initiation behaviors, six additional Cox models are reported for each hookah initiation behaviors. Results The largest increase in hookah use occurred between ages 18 and 19: 5.8% for ever use and 2.7% for past 30-day hookah use. By age 21, 10.5%, 4.7% and 1.2% reported initiation of ever, past 30-day and fairly regular hookah use, respectively. There were statistically significance differences in the age of initiation of hookah use behaviors by race/ethnicity. Conclusion Educational interventions should target young adults before the age of 21, focusing efforts specifically on males, non-Hispanic Blacks and Hispanics, to stall initiation and progression of hookah use behaviors.

Spontaneous clearance of vertically acquired hepatitis c infection: implications for testing and treatment

Background. Current guidelines recommend that infants born to women with hepatitis C (HCV) viremia are screened for HCV antibody at age 18 months, and if positive, referred for RNA testing at 3 years to confirm chronic infection. This policy is based in part on analyses suggesting 25%-40% of vertically acquired HCV infections clear spontaneously within 4-5 years. Methods. Data on 179 infants with RNA and/or anti-HCV evidence of vertically acquired viraemia (single PCR+) or confirmed infection (2 PCR+ or anti-HCV beyond 18 months) in three prospective European cohorts were investigated. Ages at clearance of viremia and confirmed infection were estimated taking account of interval censoring and delayed entry. We also investigated clearance in infants in whom RNA was not detectable until after 6 weeks. Results. Clearance rates decline rapidly over the first 6 months. An estimated 90.6% (95%CrI: 83.5-95.9) of viremia cleared by 5 years, most within 3 months, and 65.9% (50.1-81.6) of confirmed infection cleared by 5 years, at a median 12.4 (7.1-18.9) months. If treatment began at age 6 months, 18 months or 3 years, at least 59.0% (42.0-76.9), 39.7 (17.9-65.9), and 20.9 (4.6-44.8) of those treated would clear without treatment. In seven (6.6%) confirmed infections, RNA was not detectable until after 6 weeks, and in 2 (1.9%) not until after 6 months. However, all such cases subsequently cleared. Conclusions. Most viraemia clears within 3 months, and most confirmed infection by 3 years. Delaying treatment avoids but does not eliminate over-treatment and should be balanced against loss to follow-up.

The impact of ignoring Interval censoring in progression-free survival in cancer trials: a systematic review

Introduction & Objective: From statistical literature, the bias in treatment effect from ignoring interval censoring in Progression-free survival (PFS) is demonstrated. However, the impact on estimators caused by interval censoring is not carefully took account and investigated by researchers in practice. The objective of this study is to examine the impact of accounting for interval censoring in practice among RCTs used to support FDA approvals anti-cancer drugs between the years 2005 and 2019 that used PFS as an endpoint. Methods: In this systematic review, the differences of hazard ratios between two methods: considering and ignoring interval censoring, are visualized by Kaplan-Meier survival curves and estimated from a Cox proportional hazard model of 87 RCTs. With assumption that these differences and mean differences (bias) follow a normal distribution, limits of agreement of differences and confidence interval of bias are used to represent agreement of two methods. Results: Limits of agreement of difference range from -0.044 to 0.0615, while confidence intervals for the bias range from 0.0026 to 0.0145, which does not include zero, resulting in estimated treatment effect differs for two methods. Conclusion: In general, bias caused by interval censoring in treatment effect exists with large sample studies. Focusing on individual clinical trials, limits of agreement can provide more information for researchers to make decision on how to account for interval censoring.

Quantitative interpretation of Sedia LAg Assay test results after HIV diagnosis.

Background Testing for recent HIV infection is common in surveillance, where only population-level estimates (of incidence) are reported. Typically, recent infection is a category, obtained by applying a threshold on an underlying continuous biomarker from some laboratory assay(s). Somehow interpreting the biomarker values obtained for individual subjects, for example, interpreting them as estimates of the date of infection, has obvious potential applications in the context of studies of early infection, and has also for some years attracted significant interest as an extra component of post-test counselling and treatment initiation. The applicable analyses have typically run aground on the complexity of the full biomarker growth model, which is in principle a non-linear mixed-effects model of unknown structure, the fitting of which seems infeasible from realistically obtainable data. Methods It is known that to estimate Mean Duration of Recent Infection (MDRI) at a given value of the recent/non-recent -infection discrimination threshold, one may compress the full biomarker growth model into a relation capturing the probability of a recent test result as a function of time since infection. Noting that the time-derivative (gradient) of this curve (for a value of threshold — h) is identical to the formal likelihood relevant to Bayesian inference of the infection date, for a subject yielding an assay result * h * on the date of their first positive HIV test. This observation bypasses the need for fitting a complex detailed biomarker growth model. Using publicly available data from the CEPHIA collaboration, we calculated curves for a range of thresholds for the Sedia Lag assay and performed Bayesian inference of infection data, given a uniform prior implied by a last negative and first positive test. Results We demonstrate the generation of posteriors for infection date, for patients with various delays between their last negative and first positive HIV test, and a range of LAg assay results (ODn) hypothetically obtained on the date of the first positive result. Conclusion Depending on the last-negative / first-positive interval, there is a range of ODn values that yields posteriors significantly different from the uniform prior one would be left with based merely on interval censoring. Hence, a LAg ODn obtained on the date of, or soon after, diagnosis contains potentially significant information about infection dating. It seems worth analysing other assays with meaningful dynamic range, especially tests already routinely used in primary HIV diagnosis (for example chemiluminescent assays and reader/cartridge lateral flow tests which admit objective variable line intensity readings) which have a sufficient dynamic range that corresponds to a clinically meaningful range of times-since-infection that are worth distinguishing from each other.

Methods to Analyse Time-to-Event Data: The Kaplan-Meier Survival Curve

Studies performed in the field of oxidative medicine and cellular longevity frequently focus on the association between biomarkers of cellular and molecular mechanisms of oxidative stress as well as of aging, immune function, and vascular biology with specific time to event data, such as mortality and organ failure. Indeed, time-to-event analysis is one of the most important methodologies used in clinical and epidemiological research to address etiological and prognostic hypotheses. Survival data require adequate methods of analyses. Among these, the Kaplan-Meier analysis is the most used one in both observational and interventional studies. In this paper, we describe the mathematical background of this technique and the concept of censoring (right censoring, interval censoring, and left censoring) and report some examples demonstrating how to construct a Kaplan-Meier survival curve and how to apply this method to provide an answer to specific research questions.

Experimental Design for the Lifetime Performance Index of Weibull Products Based on the Progressive Type I Interval Censored Sample

In this study, the experimental design is developed based on the testing procedure for the lifetime performance index of products following Weibull lifetime distribution under progressive type I interval censoring. This research topic is related to asymmetrical probability distributions and applications across disciplines. The asymptotic distribution of the maximum likelihood estimator of the lifetime performance index is utilized to develop the testing procedure. In order to reach the given power level, the minimum sample size is determined and tabulated. In order to minimize the total cost that occurred under progressive type I interval censoring, the sampling design is investigated to determine the minimum number of inspection intervals and equal interval lengths when the termination time of experiment is fixed or not fixed. For illustrative aims, one practical example is given for the implementation of our proposed sampling design to collect the progressive type I interval censored sample so that the users can use this sample to test if the lifetime performance index exceeds the desired target level.