Analysis of Dependently Truncated Sample Using Inverse Probability Weighted Estimator

Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application

Commun Stat Simul Comput

Truncation is a known feature of bone marrow transplant (BMT) registry data, for which the survival time of a leukemia patient is left truncated by the waiting time to transplant. It was recently noted that a longer waiting time was linked to poorer survival. A straightforward solution is a Cox model on the survival time with the waiting time as both truncation variable and covariate. The Cox model should also include other recognized risk factors as covariates. In this paper we focus on estimating the distribution function of waiting time and the probability of selection under the aforementioned Cox model.CC999999/Intramural CDC HHS/United States2018-07-02T00:00:00Z28725104PMC5513481vault:2390

Cox Model Analysis with the Dependently Left Truncated Data

A truncated sample consists of realizations of a pair of random variables (L, T) subject to the constraint that L ≤T. The major study interest with a truncated sample is to find the marginal distributions of L and T. Many studies have been done with the assumption that L and T are independent. We introduce a new way to specify a Cox model for a truncated sample, assuming that the truncation time is a predictor of T, and this causes the dependence between L and T. We develop an algorithm to obtain the adjusted risk sets and use the Kaplan-Meier estimator to estimate the Marginal distribution of L. We further extend our method to more practical situation, in which the Cox model includes other covariates associated with T. Simulation studies have been conducted to investigate the performances of the Cox model and the new estimators

Estimating Differences in Restricted Mean Lifetime Using Observational Data Subject to Dependent Censoring

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87174/1/j.1541-0420.2010.01503.x.pd

Semiparametric methods for survival analysis of case‐control data subject to dependent censoring

Case‐control sampling can be an efficient and cost‐saving study design, wherein subjects are selected into the study based on the outcome of interest. It was established long ago that proportional hazards regression can be applied to case‐control data. However, each of the various estimation techniques available assumes that failure times are independently censored. Since independent censoring is often violated in observational studies, we propose methods for Cox regression analysis of survival data obtained through case‐control sampling, but subject to dependent censoring. The proposed methods are based on weighted estimating equations, with separate inverse weights used to account for the case‐control sampling and to correct for dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal, and consistent estimators of the asymptotic covariance matrices are derived. Finite‐sample properties of the proposed estimators are examined through simulation studies. The methods are illustrated through an analysis of pre‐transplant mortality among end‐stage liver disease patients obtained from a national organ failure registry. The Canadian Journal of Statistics 42: 365–383; 2014 © 2014 Statistical Society of Canada Résumé L’échantillonnage cas‐témoins peut constituer un plan d'expérience efficace et économique dans le cadre duquel les sujets sont choisis pour l’étude en fonction du phénomène étudié. Il est établi depuis longtemps que le modèle de régression à risques proportionnels peut s'appliquer à des données cas‐témoins. Cependant, toutes les techniques d'estimation existantes supposent que les temps de défaillance sont censurés de façon indépendante. Étant donné que l'indépendance de la censure est souvent bafouée dans le cadre d’études observationnelles, les auteurs proposent des méthodes pour la régression de Cox de données de survie sujettes à la censure dépendante obtenues par un échantillonnage cas‐témoins. Les méthodes proposées se fondent sur des équations d'estimation pondérées dont les poids séparés et inverses permettent de tenir compte de l’échantillonnage cas‐témoins et de corriger le biais lié à la censure dépendante. Les auteurs montrent que les estimateurs proposés sont convergents et asymptotiquement normaux. Ils obtiennent également des estimateurs convergents pour les matrices de covariance asymptotique. Ils examinent les propriétés de ces estimateurs sur des échantillons de taille finie par voie de simulation et illustrent les méthodes au moyen d'une analyse de données sur le taux de mortalité prétransplantation chez les patients atteints d'une maladie hépatique en phase terminale provenant d'un registre national d'organes défaillants. La revue canadienne de statistique 42: 365–383; 2014 © 2014 Société statistique du CanadaPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108283/1/cjs11218-sm-0001-SuppData_S1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108283/2/cjs11218.pd

Study of Bootstrap Estimates in Cox Regression Model with Delayed Entry

summary:In most clinical studies, patients are observed for extended time periods to evaluate influences in treatment such as drug treatment, approaches to surgery, etc. The primary event in these studies is death, relapse, adverse drug reaction, or development of a new disease. The follow-up time may range from few weeks to many years. Although these studies are long term, the number of observed events is small. Longitudinal studies have increased the importance of statistical methods for time-to event data that can incorporate time-dependent covariates. The Cox proportional regression model is a widely used method. It is a statistical technique for exploring the relationship between the survival of a patient and several explanatory variables. We apply Cox regression models when right censoring and delayed entry survival data are considered. Su and Wang (2012) stated that delayed entry produced biased sample. In the paper we present how re-sampling together with effect of delayed entry affect estimated parameters. The possibilities as well as limitations of this approach are demonstrated through the retrospective study of mitral valve replacement in children under 18 years

Event History Analysis in Longitudinal Cohort Studies with Intermittent Inspection Times

Event history studies based on disease clinic data often face several complications. Specifically, patients visit the clinic irregularly, and the intermittent inspection times depend on the history of disease-related variables; this can cause event or failure times to be dependently interval-censored. Furthermore, failure times could be truncated, treatment assignment is non-randomized and can be confounded, and there are competing risks of the failure time outcomes under study. I propose a class of inverse probability weights applied to estimating functions so that the informative inspection scheme and confounded treatment are appropriately dealt with. As a result, the distribution of failure time outcomes can be consistently estimated. I consider parametric, non- and semi-parametric estimation. Monotone smoothing techniques are employed in a two-stage estimation procedure for the non- or semi-parametric estimation. Simulations for a variety of failure time models are conducted for examining the finite sample performances of proposed estimators. This research is initially motivated by the Psoriatic Arthritis (PsA) Toronto Cohort Study at the Toronto Western Hospital and the proposed methodologies are applied to this cohort study as an illustration

Simulation of left-truncated and case-k interval censored survival data with time-varying covariates

This research focuses on simulation of left-truncated and case-k interval censored survival data from the log-normal model with a time-varying covariate. Left-truncated data usually arises in prevalence cohort study where randomly selected individuals from medical records may have contracted certain disease for some duration of time but are free from event of interest at time of entry into a survival study. In this research, we proposed a simulation methodology by fixing the percentage of truncation at 20% and 60% with the width of 4 months of inspection interval. The procedure was computationally demanding due to the presence of left-truncation and time-varying covariates. The suitability of the proposed method was assessed based on the bias, standard error and root mean square of the parameter estimates for the log-normal survival model

Utilization of research technologies within a local community hospital in Ann Arbor

Technology has the ability to change the way clinical trials are conducted. Technology utilization has expanded into research in the form of handheld smartphones, wearables, and social media. This project explored technologies and assessed which of those technologies are being utilized at a community hospital. A survey was designed, developed, and disseminated to principal investigators and co-investigators of research within the hospital. The results showed that few of the technologies included in the assessment are being utilized by the researchers at the hospital. The most popular technology category being utilized by the researchers is smartphone technology. This research could contribute to the knowledge about the utilization of research technologies to society, as well as to the operational directors of research within the community hospital, which could help reveal which technologies are most useful. This research could also aid in the assessment of technology utilization over time within the same hospital