Página 1 dos resultados de 164 itens digitais encontrados em 0.025 segundos

History-adjusted Marginal Structural Models for Estimating Time-varying Effect Modification

Petersen, Maya L.; Deeks, Steven G.; Martin, Jeffrey N.; van der Laan, Mark J.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
55.92%
Much of epidemiology and clinical medicine is focused on estimating the effects of treatments or interventions administered over time. In such settings of longitudinal treatment, time-dependent confounding is often an important source of bias. Marginal structural models (MSMs) are a powerful tool for estimating the causal effect of a treatment using observational data, particularly when time-dependent confounding is present. In recent statistical work, van der Laan et al. presented a generalized form of MSMs called “history-adjusted” MSMs (Int J Biostat 2005;1:article 4). Unlike standard MSMs, history-adjusted MSMs can be used to estimate modification of treatment effects by time-varying covariates. Estimation of time-dependent causal effect modification is frequently of great practical relevance. For example, clinical researchers are often interested in how the prognostic significance of a biomarker for treatment response can change over time. This article provides a practical introduction to the implementation and interpretation of history-adjusted MSMs. The method is illustrated using a clinical question drawn from the treatment of human immunodeficiency virus infection. Observational cohort data from San Francisco, California...

Assessing Sexual Attitudes and Behaviors of Young Women: A Joint Model with Nonlinear Time Effects, Time Varying Covariates, and Dropouts

Ghosh, Pulak; Tu, Wanzhu
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/12/2008 EN
Relevância na Pesquisa
66.16%
Understanding human sexual behaviors is essential for the effective prevention of sexually transmitted infections. Analysis of longitudinally measured sexual behavioral data, however, is often complicated by zero-inflation of event counts, nonlinear time trend, time-varying covariates, and informative dropouts. Ignoring these complicating factors could undermine the validity of the study findings. In this paper, we put forth a unified joint modeling structure that accommodates these features of the data. Specifically, we propose a pair of simultaneous models for the zero-inflated event counts: Each of these models contains an auto-regressive structure for the accommodation of the effect of recent event history, and a nonparametric component for the modeling of nonlinear time effect. Informative dropout and time varying covariates are modeled explicitly in the process. Model fitting and parameter estimation are carried out in a Bayesian paradigm by the use of a Markov Chain Monte Carlo (MCMC) method. Analytical results showed that adolescent sexual behaviors tended to evolve nonlinearly over time and they were strongly influenced by the day-to-day variations in mood and sexual interests. These findings suggest that adolescent sex is to a large extent driven by intrinsic factors rather than being compelled by circumstances...

Good Self-Control as a Buffering Agent for Adolescent Substance Use: An Investigation in Early Adolescence with Time-Varying Covariates

Wills, Thomas A.; Ainette, Michael G.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /12/2008 EN
Relevância na Pesquisa
65.98%
We tested the prediction that self-control will have buffering effects for adolescent substance use (tobacco, alcohol, and marijuana) with regard to three risk factors: family life events, adolescent life events, and peer substance use. Participants were a sample of public school students (N = 1,767) who were surveyed at four yearly intervals between 6th grade and 9th grade. Good self-control was assessed with multiple indicators including planning and problem solving. Results showed that the impact of all three risk factors on substance use was reduced among persons with higher scores on good self-control. Buffering was found in cross-sectional analyses with multiple regression and in longitudinal analyses in a latent growth model with time-varying covariates. Implications for addressing self-control in prevention programs are discussed.

Non-Markov Multistate Modeling Using Time-Varying Covariates, with Application to Progression of Liver Fibrosis due to Hepatitis C Following Liver Transplant*

Bacchetti, Peter; Boylan, Ross D.; Terrault, Norah A.; Monto, Alexander; Berenguer, Marina
Fonte: Berkeley Electronic Press Publicador: Berkeley Electronic Press
Tipo: Artigo de Revista Científica
Publicado em 20/02/2010 EN
Relevância na Pesquisa
46.05%
Multistate modeling methods are well-suited for analysis of some chronic diseases that move through distinct stages. The memoryless or Markov assumptions typically made, however, may be suspect for some diseases, such as hepatitis C, where there is interest in whether prognosis depends on history. This paper describes methods for multistate modeling where transition risk can depend on any property of past progression history, including time spent in the current stage and the time taken to reach the current stage. Analysis of 901 measurements of fibrosis in 401 patients following liver transplantation found decreasing risk of progression as time in the current stage increased, even when controlled for several fixed covariates. Longer time to reach the current stage did not appear associated with lower progression risk. Analysis of simulation scenarios based on the transplant study showed that greater misclassification of fibrosis produced more technical difficulties in fitting the models and poorer estimation of covariate effects than did less misclassification or error-free fibrosis measurement. The higher risk of progression when less time has been spent in the current stage could be due to varying disease activity over time, with recent progression indicating an “active” period and consequent higher risk of further progression.

Survival analysis with error-prone time-varying covariates: a risk set calibration approach

Liao, Xiaomei; Zucker, David M.; Li, Yi; Spiegelman, Donna
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /03/2011 EN
Relevância na Pesquisa
55.94%
Occupational, environmental, and nutritional epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as cumulative exposure or cumulative updated average exposure, in relation to chronic disease endpoints such as cancer incidence and mortality. From exposure validation studies, it is apparent that many of the variables of interest are measured with moderate to substantial error. Although the ordinary regression calibration approach is approximately valid and efficient for measurement error correction of relative risk estimates from the Cox model with time-independent point exposures when the disease is rare, it is not adaptable for use with time-varying exposures. By re-calibrating the measurement error model within each risk set, a risk set regression calibration method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented, followed by the derivation of an estimate of its variance, resulting in a sandwich estimator. Emphasis is on methods applicable to the main study/external validation study design, which arises in important applications. Simulation studies under several assumptions about the error model were carried out...

Threshold Regression for Survival Data with Time-varying Covariates

Lee, Mei-Ling Ting; Whitmore, G. A.; Rosner, Bernard
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 30/03/2010 EN
Relevância na Pesquisa
66.27%
Time-to-event data with time-varying covariates pose an interesting challenge for statistical modeling and inference, especially where the data require a regression structure but are not consistent with the proportional hazard assumption. Threshold regression (TR) is a relatively new methodology based on the concept that degradation or deterioration of a subject’s health follows a stochastic process and failure occurs when the process first reaches a failure state or threshold (a first-hitting-time). Survival data with time-varying covariates consist of sequential observations on the level of degradation and/or on covariates of the subject, prior to the occurrence of the failure event. Encounters with this type of data structure abound in practical settings for survival analysis and there is a pressing need for simple regression methods to handle the longitudinal aspect of the data. Using a Markov property to decompose a longitudinal record into a series of single records is one strategy for dealing with this type of data. This study looks at the theoretical conditions for which this Markov approach is valid. The approach is called threshold regression with Markov decomposition or Markov TR for short. A number of important special cases...

Using Time-Varying Covariates in Multilevel Growth Models

McCoach, D. Betsy; Kaniskan, Burcu
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 14/06/2010 EN
Relevância na Pesquisa
65.98%
This article provides an illustration of growth curve modeling within a multilevel framework. Specifically, we demonstrate coding schemes that allow the researcher to model discontinuous longitudinal data using a linear growth model in conjunction with time-varying covariates. Our focus is on developing a level-1 model that accurately reflects the shape of the growth trajectory. We demonstrate the importance of adequately modeling the shape of the level-1 growth trajectory in order to make inferences about the importance of both level-1 and level-2 predictors.

A Bayesian proportional hazards regression model with non-ignorably missing time-varying covariates

Bradshaw, Patrick T.; Ibrahim, Joseph G.; Gammon, Marilie D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.16%
Missing covariate data is common in observational studies of time to an event, especially when covariates are repeatedly measured over time. Failure to account for the missing data can lead to bias or loss of efficiency, especially when the data are non-ignorably missing. Previous work has focused on the case of fixed covariates rather than those that are repeatedly measured over the follow-up period, so here we present a selection model that allows for proportional hazards regression with time-varying covariates when some covariates may be non-ignorably missing. We develop a fully Bayesian model and obtain posterior estimates of the parameters via the Gibbs sampler in WinBUGS. We illustrate our model with an analysis of post-diagnosis weight change and survival after breast cancer diagnosis in the Long Island Breast Cancer Study Project (LIBCSP) follow-up study. Our results indicate that post-diagnosis weight gain is associated with lower all-cause and breast cancer specific survival among women diagnosed with new primary breast cancer. Our sensitivity analysis showed only slight differences between models with different assumptions on the missing data mechanism yet the complete case analysis yielded markedly different results.

A Time-Varying Effect Model for Intensive Longitudinal Data

Tan, Xianming; Shiyko, Mariya P.; Li, Runze; Li, Yuelin; Dierker, Lisa
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
56.08%
Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This paper introduces time-varying effect models (TVEM) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describes unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and post- cessation period.

Mixed-Poisson Point Process with Partially-Observed Covariates: Ecological Momentary Assessment of Smoking

Neustifter, Benjamin; Rathbun, Stephen L.; Shiffman, Saul
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.05%
Ecological Momentary Assessment is an emerging method of data collection in behavioral research that may be used to capture the times of repeated behavioral events on electronic devices, and information on subjects’ psychological states through the electronic administration of questionnaires at times selected from a probability-based design as well as the event times. A method for fitting a mixed Poisson point process model is proposed for the impact of partially-observed, time-varying covariates on the timing of repeated behavioral events. A random frailty is included in the point-process intensity to describe variation among subjects in baseline rates of event occurrence. Covariate coefficients are estimated using estimating equations constructed by replacing the integrated intensity in the Poisson score equations with a design-unbiased estimator. An estimator is also proposed for the variance of the random frailties. Our estimators are robust in the sense that no model assumptions are made regarding the distribution of the time-varying covariates or the distribution of the random effects. However, subject effects are estimated under gamma frailties using an approximate hierarchical likelihood. The proposed approach is illustrated using smoking data.

Using the Time-Varying Effect Model (TVEM) to Examine Dynamic Associations between Negative Affect and Self Confidence on Smoking Urges: Differences between Successful Quitters and Relapsers

Shiyko, Mariya P.; Lanza, Stephanie T.; Tan, Xianming; Li, Runze; Shiffman, Saul
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /06/2012 EN
Relevância na Pesquisa
56.01%
With technological advances, collection of intensive longitudinal data (ILD), such as ecological momentary assessments, becomes more widespread in prevention science. In ILD studies, researchers are often interested in the effects of time-varying covariates (TVCs) on a time-varying outcome to discover correlates and triggers of target behaviors (e.g., how momentary changes in affect relate to momentary smoking urges). Traditional analytical methods, however, impose important constraints, assuming a constant effect of the TVC on the outcome. In the current paper, we describe a time-varying effect model (TVEM) and its applications to data collected as part of a smoking-cessation study. Differentiating between groups of short-term successful quitters (N=207) and relapsers (N=40), we examine the effects of momentary negative affect and abstinence self-efficacy on the intensity of smoking urges in each subgroup in the 2 weeks following a quit attempt. Successful quitters demonstrated a rapid reduction in smoking urges over time, a gradual decoupling of the association between negative affect and smoking urges, and a consistently strong negative effect of self-efficacy on smoking urges. In comparison, relapsers exhibited a high level of smoking urges throughout the post-quit period...

Generating survival times to simulate Cox proportional hazards models with time-varying covariates

Austin, Peter C
Fonte: Blackwell Publishing Ltd Publicador: Blackwell Publishing Ltd
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.33%
Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow for an examination of the performance of statistical procedures in settings in which analytic and mathematical derivations may not be feasible. A key element in any statistical simulation is the existence of an appropriate data-generating process: one must be able to simulate data from a specified statistical model. We describe data-generating processes for the Cox proportional hazards model with time-varying covariates when event times follow an exponential, Weibull, or Gompertz distribution. We consider three types of time-varying covariates: first, a dichotomous time-varying covariate that can change at most once from untreated to treated (e.g., organ transplant); second, a continuous time-varying covariate such as cumulative exposure at a constant dose to radiation or to a pharmaceutical agent used for a chronic condition; third, a dichotomous time-varying covariate with a subject being able to move repeatedly between treatment states (e.g., current compliance or use of a medication). In each setting, we derive closed-form expressions that allow one to simulate survival times so that survival times are related to a vector of fixed or time-invariant covariates and to a single time-varying covariate. We illustrate the utility of our closed-form expressions for simulating event times by using Monte Carlo simulations to estimate the statistical power to detect as statistically significant the effect of different types of binary time-varying covariates. This is compared with the statistical power to detect as statistically significant a binary time-invariant covariate. Copyright © 2012 John Wiley & Sons...

Survival Analysis with Time-Varying Covariates Measured at Random Times by Design

Rathbun, Stephen L.; Song, Xiao; Neustifter, Benjamin; Shiffman, Saul
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.12%
Ecological momentary assessment (EMA) is a method for collecting real-time data in subjects’ environments. It often uses electronic devices to obtain information on psychological state through administration of questionnaires at times selected from a probability-based sampling design. This information can be used to model the impact of momentary variation in psychological state on the lifetimes to events such as smoking lapse. Motivated by this, a probability-sampling framework is proposed for estimating the impact of time-varying covariates on the lifetimes to events. Presented as an alternative to joint modeling of the covariate process as well as event lifetimes, this framework calls for sampling covariates at the event lifetimes and at times selected according to a probability-based sampling design. A design-unbiased estimator for the cumulative hazard is substituted into the log likelihood, and the resulting objective function is maximized to obtain the proposed estimator. This estimator has two quantifiable sources of variation, that due to the survival model and that due to sampling the covariates. Data from a nicotine patch trial are used to illustrate the proposed approach.

Bayesian latent structure models with space-time-dependent covariates

Cai, Bo; Lawson, Andrew B; Hossain, Md Monir; Choi, Jungsoon
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/04/2012 EN
Relevância na Pesquisa
46.22%
Spatial-temporal data requires flexible regression models which can model the dependence of responses on space- and time-dependent covariates. In this paper, we describe a semiparametric space-time model from a Bayesian perspective. Nonlinear time dependence of covariates and the interactions among the covariates are constructed by local linear and piecewise linear models, allowing for more flexible orientation and position of the covariate plane by using time-varying basis functions. Space-varying covariate linkage coefficients are also incorporated to allow for the variation of space structures across the geographical location. The formulation accommodates uncertainty in the number and locations of the piecewise basis functions to characterize the global effects, spatially structured and unstructured random effects in relation to covariates. The proposed approach relies on variable selection-type mixture priors for uncertainty in the number and locations of basis functions and in the space-varying linkage coefficients. A simulation example is presented to evaluate the performance of the proposed approach with the competing models. A real data example is used for illustration.

Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates

Skup, Martha; Zhu, Hongtu; Zhang, Heping
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.19%
Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA-GMM) approach to estimate marginal regression models for imaging datasets that contain time-varying, spatially-related responses and some time-varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject’s response image at each time point) as many current neuroimaging analysis techniques do, this method “adaptively smoothes” neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA-GMM’s development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer’s Disease Neuroimaging Initiative are used to assess the performance of MA-GMM.

Changes in Individual Drug-Independent System Parameters during Virtual Paediatric Pharmacokinetic Trials: Introducing Time-Varying Physiology into a Paediatric PBPK Model

Abduljalil, Khaled; Jamei, Masoud; Rostami-Hodjegan, Amin; Johnson, Trevor N.
Fonte: Springer US Publicador: Springer US
Tipo: Artigo de Revista Científica
Publicado em 04/04/2014 EN
Relevância na Pesquisa
46.09%
Although both POPPK and physiologically based pharmacokinetic (PBPK) models can account for age and other covariates within a paediatric population, they generally do not account for real-time growth and maturation of the individuals through the time course of drug exposure; this may be significant in prolonged neonatal studies. The major objective of this study was to introduce age progression into a paediatric PBPK model, to allow for continuous updating of anatomical, physiological and biological processes in each individual subject over time. The Simcyp paediatric PBPK model simulator system parameters were reanalysed to assess the impact of re-defining the individual over the study period. A schedule for re-defining parameters within the Simcyp paediatric simulator, for each subject, over a prolonged study period, was devised to allow seamless prediction of pharmacokinetics (PK). The model was applied to predict concentration-time data from multiday studies on sildenafil and phenytoin performed in neonates. Among PBPK system parameters, CYP3A4 abundance was one of the fastest changing covariates and a 1-h re-sampling schedule was needed for babies below age 3.5 days in order to seamlessly predict PK (<5% change in abundance) with subject maturation. The re-sampling frequency decreased as age increased...

Modelling survival in acute severe illness: Cox versus accelerated failure time models

Moran, J.; Bersten, A.; Solomon, P.; Edibam, C.; Hunt, T.
Fonte: Blackwell Science Ltd Publicador: Blackwell Science Ltd
Tipo: Artigo de Revista Científica
Publicado em //2008 EN
Relevância na Pesquisa
76.18%
BACKGROUND: The Cox model has been the mainstay of survival analysis in the critically ill and time-dependent covariates have infrequently been incorporated into survival analysis. OBJECTIVES: To model 28-day survival of patients with acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), and compare the utility of Cox and accelerated failure time (AFT) models. METHODS: Prospective cohort study of 168 adult patients enrolled at diagnosis of ALI in 21 adult ICUs in three Australian States with measurement of survival time, censored at 28 days. Model performance was assessed as goodness-of-fit [GOF, cross-products of quantiles of risk and time intervals (P > or = 0.1), Cox model] and explained variation ('R2', Cox and ATF). RESULTS: Over a 2-month study period (October-November 1999), 168 patients with ALI were identified, with a mean (SD) age of 61.5 (18) years and 30% female. Peak mortality hazard occurred at days 7-8 after onset of ALI/ARDS. In the Cox model, increasing age and female gender, plus interaction, were associated with an increased mortality hazard. Time-varying effects were established for patient severity-of-illness score (decreasing hazard over time) and multiple-organ-dysfunction score (increasing hazard over time). The Cox model was well specified (GOF...

Survival Analysis of Patients with Heart Failure: Implications of Time-Varying Regression Effects in Modeling Mortality

Giolo, Suely Ruiz; Krieger, Jose Eduardo; Mansur, Alfredo Jose; Pereira, Alexandre Costa
Fonte: PUBLIC LIBRARY SCIENCE; SAN FRANCISCO Publicador: PUBLIC LIBRARY SCIENCE; SAN FRANCISCO
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
46.21%
Background: Several models have been designed to predict survival of patients with heart failure. These, while available and widely used for both stratifying and deciding upon different treatment options on the individual level, have several limitations. Specifically, some clinical variables that may influence prognosis may have an influence that change over time. Statistical models that include such characteristic may help in evaluating prognosis. The aim of the present study was to analyze and quantify the impact of modeling heart failure survival allowing for covariates with time-varying effects known to be independent predictors of overall mortality in this clinical setting. Methodology: Survival data from an inception cohort of five hundred patients diagnosed with heart failure functional class III and IV between 2002 and 2004 and followed-up to 2006 were analyzed by using the proportional hazards Cox model and variations of the Cox's model and also of the Aalen's additive model. Principal Findings: One-hundred and eighty eight (188) patients died during follow-up. For patients under study, age, serum sodium, hemoglobin, serum creatinine, and left ventricular ejection fraction were significantly associated with mortality. Evidence of time-varying effect was suggested for the last three. Both high hemoglobin and high LV ejection fraction were associated with a reduced risk of dying with a stronger initial effect. High creatinine...

Time-varying Coefficients Estimation in Differential Equation Models with Noisy Time-varying Covariates

Lian, Heng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/10/2009
Relevância na Pesquisa
55.92%
We study the problem of estimating time-varying coefficients in ordinary differential equations. Current theory only applies to the case when the associated state variables are observed without measurement errors as presented in \cite{chenwu08b,chenwu08}. The difficulty arises from the quadratic functional of observations that one needs to deal with instead of the linear functional that appears when state variables contain no measurement errors. We derive the asymptotic bias and variance for the previously proposed two-step estimators using quadratic regression functional theory.

Full Open Population Capture-Recapture Models with Individual Covariates

Schofield, Matthew R.; Barker, Richard J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/09/2010
Relevância na Pesquisa
46.05%
Traditional analyses of capture-recapture data are based on likelihood functions that explicitly integrate out all missing data. We use a complete data likelihood (CDL) to show how a wide range of capture-recapture models can be easily fitted using readily available software JAGS/BUGS even when there are individual-specific time-varying covariates. The models we describe extend those that condition on first capture to include abundance parameters, or parameters related to abundance, such as population size, birth rates or lifetime. The use of a CDL means that any missing data, including uncertain individual covariates, can be included in models without the need for customized likelihood functions. This approach also facilitates modeling processes of demographic interest rather than the complexities caused by non-ignorable missing data. We illustrate using two examples, (i) open population modeling in the presence of a censored time-varying individual covariate in a full robust-design, and (ii) full open population multi-state modeling in the presence of a partially observed categorical variable.