Modelling progression of CD4-lymphocyte count and its relationship to survival time.
about
Missing data methods in longitudinal studies: a reviewA joint model for longitudinal measurements and survival data in the presence of multiple failure types.Mixed effects logistic regression models for longitudinal ordinal functional response data with multiple-cause drop-out from the longitudinal study of aging.A mixed effects model for the analysis of ordinal longitudinal pain data subject to informative drop-out.Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data.Jointly modeling time-to-event and longitudinal data: A Bayesian approachJoint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working groupAnalysis of survival data with missing measurements of a time-dependent binary covariate.A Bayesian semiparametric joint hierarchical model for longitudinal and survival data.Jointly Modeling Event Time and Skewed-Longitudinal Data with Missing Response and Mismeasured Covariate for AIDS Studies.Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates.Assessing model fit in joint models of longitudinal and survival data with applications to cancer clinical trials.Joint modeling of longitudinal health-related quality of life data and survival.Semiparametric Bayesian inference on skew-normal joint modeling of multivariate longitudinal and survival data.Latent pattern mixture models for informative intermittent missing data in longitudinal studies.Mixtures of varying coefficient models for longitudinal data with discrete or continuous nonignorable dropout.Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling.An approach to joint analysis of longitudinal measurements and competing risks failure time data.Semiparametric approaches for joint modeling of longitudinal and survival data with time-varying coefficients.A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer.Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with multiple features.Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data.Meta-analysis of studies with missing data.Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical TrialsFactor analytic models of clustered multivariate data with informative censoring.Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data.Joint modeling of longitudinal and survival data via a common frailty.A joint model for nonparametric functional mapping of longitudinal trajectory and time-to-event.Mixed-effect hybrid models for longitudinal data with nonignorable dropout.Robust joint modeling of longitudinal measurements and competing risks failure time dataEstimation in longitudinal or panel data models with random-effect-based missing responses.Bayesian quantile regression for longitudinal studies with nonignorable missing data.Longitudinal Data with Follow-up Truncated by Death: Match the Analysis Method to Research AimsBasic concepts and methods for joint models of longitudinal and survival data.Choosing profile double-sampling designs for survival estimation with application to President's Emergency Plan for AIDS Relief evaluation.A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects.Likelihood-based methods for estimating the association between a health outcome and left- or interval-censored longitudinal exposure data.Joint modeling of longitudinal data and informative dropout time in the presence of multiple changepoints.A Bayesian Shrinkage Model for Incomplete Longitudinal Binary Data with Application to the Breast Cancer Prevention Trial.
P2860
Q28743676-D32D2C5D-279F-4E2C-A7EE-CBF569C7FE7BQ30490523-A00F168C-A05A-4CA7-9D41-940CE4688B23Q30593010-6854A00D-6222-4380-8B49-B00A83A263F7Q30635291-9B35CEF6-5CCB-45CD-97A6-2CB26BEEBD7FQ30669407-EF1C7F5A-9274-49A2-ACD7-C3612E6B1015Q30757208-9E8ED98B-9205-4BD1-BBAE-0EFA65637FB3Q30773626-A9B24204-86A1-4C81-ADF3-FE2A035D3172Q30779044-C47A0B5E-2C63-4624-9D3B-87A88E26757DQ30790798-294C1B4E-A96E-4293-957F-5ABA00E1E067Q30819352-F03B8EAC-4B5D-47E8-9D01-41CAED02983EQ30829465-AE465C7A-8804-40E3-B220-7DDEBD015EBDQ30832318-E18FADBC-9EC6-44E0-BEA1-961E0C3E610BQ30837515-102D356B-FF1A-4E4A-B373-BC107A87654EQ30859974-C17E6C85-49E0-439F-948E-85EBC49C715BQ30870351-E5ABB7FD-3A68-4CB9-8F9D-6E021C3D4DE0Q30936743-3B1740DA-94D4-4886-B563-C55EF9D47585Q30978747-1D1A9AC2-50A9-43ED-B07F-83B4E85D3DEEQ31062039-A6EB1CD6-6921-4D59-8092-452A15925A9DQ31080090-A3071C37-A66A-438C-893A-DE89B3037EB7Q31122828-C48356E6-E8CB-4C8F-9C71-EC403BCEE37DQ31122903-C32B238F-0CCB-47E7-AC18-A60099C80F0CQ31126662-D49A8CD6-685C-4506-9FB6-057188B02932Q31133066-EBE05785-BBB5-4A5A-BB42-8CDFD2F4CF56Q31159797-8509E948-2D18-48E9-9337-7A96BB6BFEFAQ31166420-AF9018EA-1852-4E95-B4BD-FA90C972CFC1Q31881186-23A78511-19E7-4908-8762-2CFD3B532CC8Q32066278-5DADB969-18B3-4588-9B39-53BC6D2856FEQ33210094-67D7F44D-97DA-4C9A-B389-312F89CA9178Q33236632-3416C8FE-0DB7-4B09-9AE2-12C4FC789BB2Q33365207-9154FB2D-B05A-44BC-B037-AE2B36111351Q33406378-54F6D401-F71E-442D-907F-39670343E935Q33443405-6DEB0ADD-1D6F-49B9-96C8-1AE4E5962517Q33449791-AA5FC923-CD33-422B-922D-77323806723FQ33527926-1C8BAA00-AE69-4A4F-AB33-40111CA79861Q33571296-04A71981-0659-44D5-9D05-A5B47C59C394Q33578888-3618C7FD-680D-4BFC-A2A7-350730F51398Q33604991-DBEDB22C-E08B-4390-BBCE-8B34C71C163DQ33613795-58DB3D76-5BD0-4C48-8780-C70ED325B3FEQ33825415-AE3FAF68-A6C5-40FE-A5A5-D81ED9F86006Q33880852-21984CAC-6129-4885-B970-2D38BF5DBB2C
P2860
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
description
1994 nî lūn-bûn
@nan
1994年の論文
@ja
1994年学术文章
@wuu
1994年学术文章
@zh
1994年学术文章
@zh-cn
1994年学术文章
@zh-hans
1994年学术文章
@zh-my
1994年学术文章
@zh-sg
1994年學術文章
@yue
1994年學術文章
@zh-hant
name
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
@en
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
@nl
type
label
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
@en
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
@nl
prefLabel
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
@en
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
@nl
P356
P1433
P1476
Modelling progression of CD4-lymphocyte count and its relationship to survival time.
@en
P2093
P304
P356
10.2307/2533439
P407
P577
1994-12-01T00:00:00Z