Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Nested Markov compliance class model in the presence of time-varying noncompliance.

ABSTRACT: We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens-Rubin (1997, The Annals of Statistics 25, 305-327) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model where we use time-invariant subject-specific compliance principal strata to summarize longitudinal trends of subject-specific time-varying compliance patterns. The principal strata are formed using Markov models that relate current compliance behavior to compliance history. Treatment effects are estimated as intent-to-treat effects within the compliance principal strata.

SUBMITTER: Lin JY

PROVIDER: S-EPMC2700859 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Nested Markov compliance class model in the presence of time-varying noncompliance.

Lin Julia Y JY Ten Have Thomas R TR Elliott Michael R MR

Biometrics 20090601 2

<h4>Summary</h4>We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens-Rubin (1997, The Annals of Statistics 25, 305-327) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model whe ...[more]

PMID: 18759831

Similar Datasets

Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model.

Project description:MotivationAccumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions.ResultsIn this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota.Availability and implementationThe C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8275348 | biostudies-literature

Cox regression model with time-varying coefficients in nested case-control studies.

Project description:The nested case-control (NCC) design is a cost-effective sampling method to study the relationship between a disease and its risk factors in epidemiologic studies. NCC data are commonly analyzed using Thomas' partial likelihood approach under Cox's proportional hazards model with constant covariate effects. Here, we are interested in studying the potential time-varying effects of covariates in NCC studies and propose an estimation approach based on a kernel-weighted Thomas' partial likelihood. We establish asymptotic properties of the proposed estimator, propose a numerical approach to construct simultaneous confidence bands for time-varying coefficients, and develop a hypothesis testing procedure to detect time-varying coefficients. The proposed inference procedure is evaluated in simulations and applied to an NCC study of breast cancer in the New York University Women's Health Study.

| S-EPMC3294270 | biostudies-literature

Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data.

Project description:The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each "trial," using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional "states" are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate "trials" from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each 'state' were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.

| S-EPMC6160873 | biostudies-literature

Nested g-computation: A causal approach to analysis of censored medical costs in the presence of time-varying treatment.

Project description:Rising medical costs are an emerging challenge in policy decisions and resource allocation planning. When cumulative medical cost is the outcome, right-censoring induces informative missingness due to heterogeneity in cost accumulation rates across subjects. Inverse-weighting approaches have been developed to address the challenge of informative cost trajectories in mean cost estimation, though these approaches generally ignore post-baseline treatment changes. In post-hysterectomy endometrial cancer patients, data from a linked database of Medicare records and the Surveillance, Epidemiology, and End Results program of the National Cancer Institute reveal substantial within-subject variation in treatment over time. In such a setting, the utility of existing intent-to-treat approaches is generally limited. Estimates of population mean cost under a hypothetical time-varying treatment regime can better assist with resource allocation when planning for a treatment policy change; such estimates must inherently take time-dependent treatment and confounding into account. In this paper, we develop a nested g-computation approach to cost analysis to address this challenge, while accounting for censoring. We develop a procedure to evaluate sensitivity to departures from baseline treatment ignorability. We further conduct a variety of simulations and apply our nested g-computation procedure to two-year costs from endometrial cancer patients.

| S-EPMC8186489 | biostudies-literature

Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates.

Project description:Longitudinal data are often segmented by unobserved time-varying factors, which introduce latent heterogeneity at the observation level, in addition to heterogeneity across subjects. We account for this latent structure by a linear mixed hidden Markov model. It integrates subject-specific random effects and Markovian sequences of time-varying effects in the linear predictor. We propose an expectation?-maximization algorithm for maximum likelihood estimation, based on data augmentation. It reduces to the iterative maximization of the expected value of a complete likelihood function, derived from an augmented dataset with case weights, alternated with weights updating. In a case study of the Survey on Stress Aging and Health in Russia, the model is exploited to estimate the influence of the observed covariates under unobserved time-varying factors, which affect the cardiovascular activity of each subject during the observation period.

| S-EPMC4159441 | biostudies-literature

Structural nested mean models for assessing time-varying effect moderation.

Project description:This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time varying and so are the covariates said to moderate its effect. Intermediate causal effects that describe time-varying causal effects of treatment conditional on past covariate history are introduced and considered as part of Robins' structural nested mean model. Two estimators of the intermediate causal effects, and their standard errors, are presented and discussed: The first is a proposed two-stage regression estimator. The second is Robins' G-estimator. The results of a small simulation study that begins to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented. The methodology is illustrated using longitudinal data from a depression study.

| S-EPMC2875310 | biostudies-literature

Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals.

Project description:Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings where both the treatment and effect moderator vary over time. Effect moderation is typically examined using covariate by treatment interactions in regression analyses, but in the longitudinal setting, this approach may be problematic because time-varying moderators of future treatment may be affected by prior treatment-for example, moderators may also be mediators-and naively conditioning on an outcome of treatment in a conventional regression model can lead to bias. This article introduces to sociology moderated intermediate causal effects and the structural nested mean model for analyzing effect moderation in the longitudinal setting. It discusses problems with conventional regression and presents a new approach to estimation that avoids these problems (regression-with-residuals). The method is illustrated using longitudinal data from the PSID to examine whether the effects of time-varying exposures to poor neighborhoods on the risk of adolescent childbearing are moderated by time-varying family income.

| S-EPMC5788466 | biostudies-literature

Adjusting for time-varying confounders in survival analysis using structural nested cumulative survival time models.

Project description:Accounting for time-varying confounding when assessing the causal effects of time-varying exposures on survival time is challenging. Standard survival methods that incorporate time-varying confounders as covariates generally yield biased effect estimates. Estimators using weighting by inverse probability of exposure can be unstable when confounders are highly predictive of exposure or the exposure is continuous. Structural nested accelerated failure time models (AFTMs) require artificial recensoring, which can cause estimation difficulties. Here, we introduce the structural nested cumulative survival time model (SNCSTM). This model assumes that intervening to set exposure at time t to zero has an additive effect on the subsequent conditional hazard given exposure and confounder histories when all subsequent exposures have already been set to zero. We show how to fit it using standard software for generalized linear models and describe two more efficient, double robust, closed-form estimators. All three estimators avoid the artificial recensoring of AFTMs and the instability of estimators that use weighting by the inverse probability of exposure. We examine the performance of our estimators using a simulation study and illustrate their use on data from the UK Cystic Fibrosis Registry. The SNCSTM is compared with a recently proposed structural nested cumulative failure time model, and several advantages of the former are identified.

| S-EPMC7317577 | biostudies-literature

Adjusting the Effect of Integrating Antiretroviral Therapy and Tuberculosis Treatment on Mortality for Noncompliance: A Time-varying Instrumental Variables Analysis.

Project description:BACKGROUND:Using intent-to-treat comparisons, it has been shown that the integration of antiretroviral therapy (ART) and tuberculosis (TB) treatment improves survival. Because the magnitude of the effect of ART initiation during TB treatment on mortality is less well understood owing to noncompliance, we used instrumental variables (IV) analyses. METHODS:We studied 642 HIV-TB co-infected patients from the Starting Antiretroviral Therapy at Three Points in Tuberculosis trial. Patients were assigned to start ART either early or late during TB treatment or after TB treatment completion. We used 2-stage predictor substitution and 2-stage residuals inclusion methods under additive and proportional hazards regressions with a time-fixed measure of compliance defined as the fraction of time on ART during TB treatment. We moreover developed novel IV methods for additive hazards regression with a time-varying measure of compliance. RESULTS:Intent-to-treat results from additive hazards models showed that patients in the early integrated arms had a reduced hazard of -0.05 (95% confidence interval [CI]: -0.09, -0.01) when compared with the sequential arm. Adjustment for noncompliance changed this effect to -0.07 (95% CI: -0.12, -0.01). An additional time-varying IV analysis on the overall effect of ART exposure suggested an effect of -0.29 (95 % CI: -0.54, -0.03). CONCLUSION:IV analyses enable assessment of the effectiveness of TB and ART integration, corrected for noncompliance, and thereby enable a better public health evaluation of the potential impact of this intervention.

| S-EPMC6367706 | biostudies-literature

Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models.

Project description:BackgroundIn many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis.MethodsA SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be applied to case-control data. We also describe how the g-estimates of the SNCFTM parameters can be used to calculate marginal cumulative risks under nondynamic treatment strategies. We examine the performance of this method using simulated data, and present an application of these models by conducting an MR study of alcohol intake and endometrial cancer using longitudinal observational data from the Nurses' Health Study.ResultsOur simulations found that estimates from SNCFTMs which used an IV approach were similar to those obtained from SNCFTMs which adjusted for confounders, and similar to those obtained from the g-formula approach when the outcome was rare. In our data application, the cumulative risk of endometrial cancer from age 45 to age 72 under the "never drink" strategy (4.0%) was similar to that under the "always ½ drink per day" strategy (4.3%).ConclusionsSNCFTMs can be used to conduct MR and other IV analyses with time-varying treatments and failure time outcomes.

| S-EPMC8620657 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data