Incorporating time-dependent covariates in survival analysis using the LVAR method

2006 ◽  
Vol 25 (10) ◽  
pp. 1729-1740 ◽  
Author(s):  
Yali Liu ◽  
Bruce A. Craig
Keyword(s):  
Survival Analysis ◽  
Time Dependent ◽  
Time Dependent Covariates

scholarly journals Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM)

Biostatistics ◽  
2017 ◽  
Vol 19 (4) ◽  
pp. 479-496 ◽  
Author(s):  
Margarita Moreno-Betancur ◽  
John B Carlin ◽  
Samuel L Brilleman ◽  
Stephanie K Tanamas ◽  
Anna Peeters ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Missing Data ◽  
Measurement Error ◽  
Multiple Imputation ◽  
Joint Modeling ◽  
Time Dependent ◽  
Time Dependent Covariates

scholarly journals Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking

2017 ◽  
Vol 59 (6) ◽  
pp. 1261-1276 ◽  
Author(s):  
Dimitris Rizopoulos ◽  
Geert Molenberghs ◽  
Emmanuel M.E.H. Lesaffre
Keyword(s):  
Survival Analysis ◽  
Joint Modeling ◽  
Time Dependent ◽  
Time Dependent Covariates

CONTINUOUSLY RECORDED SUCKLING BEHAVIOUR AND ITS EFFECT ON LACTATIONAL AMENORRHOEA

1999 ◽  
Vol 31 (3) ◽  
pp. 289-310 ◽  
Author(s):  
H. WILLIAM TAYLOR ◽  
MARGARET VÁZQUEZ-GEFFROY ◽  
STEVEN J. SAMUELS ◽  
DONNA M. TAYLOR
Keyword(s):  
Survival Analysis ◽  
New Mexico ◽  
Menstrual Cycle ◽  
Time Dependent ◽  
Rate Of Return ◽  
Specific Rate ◽  
Mexican Women ◽  
New Mexican ◽  
Time Dependent Covariates ◽  
Ovarian Cyclicity

The hypothesis that the month-specific rate of return to ovarian cyclicity after childbirth is causally related to suckling pattern was tested for a population of New Mexican women recruited within the service area of New Mexico Highlands University and for a nationwide USA subpopulation of women recruited through membership of the Couple to Couple League (CCL). Survival analysis for time-dependent covariates was used, and significant predictors of the first postpartum menses were found. Important differences were detected in the suckling pattern for the two groups and a 5:2 differential was found in their respective rates of menstrual cycle recovery. Although the two groups were comparable perinatally, daily and time-windowed breast-feeding performance fell off at twice the rate for the New Mexico population when contrasted with the CCL sample. For both populations, the introduction of solid feeds was a strong and significant predictor of returning menstrual cyclicity, independent of suckling pattern.

scholarly journals Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Colin Griesbach ◽  
Andreas Groll ◽  
Elisabeth Bergherr
Keyword(s):  
Survival Analysis ◽  
Variable Selection ◽  
Time Dependent ◽  
Gradient Boosting ◽  
Joint Models ◽  
Cell Counts ◽  
Longitudinal Outcome ◽  
Event Times ◽  
Time Dependent Covariates ◽  
Updating Procedure

Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likelihood-based boosting techniques to the framework of joint models and propose a novel algorithm in order to improve inference where gradient boosting has said limitations. The algorithm represents a novel boosting approach allowing for time-dependent covariates in survival analysis and in addition offers variable selection for joint models, which is evaluated via simulations and real world application modelling CD4 cell counts of patients infected with human immunodeficiency virus (HIV). Overall, the method stands out with respect to variable selection properties and represents an accessible way to boosting for time-dependent covariates in survival analysis, which lays a foundation for all kinds of possible extensions.

Time-dependent covariates in survival analysis

Biometrika ◽  
1980 ◽  
Vol 67 (3) ◽  
pp. 697-698 ◽  
Author(s):  
EDWARD D. LUSTBADER
Keyword(s):  
Survival Analysis ◽  
Time Dependent ◽  
Time Dependent Covariates

Survival Analysis of Drug Combinations Using a Hazards Model with Time-Dependent Covariates

Biometrics ◽  
1980 ◽  
Vol 36 (3) ◽  
pp. 537 ◽  
Author(s):  
Donald M. Stablein ◽  
Walter H. Carter ◽  
Galen L. Wampler
Keyword(s):  
Survival Analysis ◽  
Drug Combinations ◽  
Time Dependent ◽  
Hazards Model ◽  
Time Dependent Covariates
Sign in / Sign up

Export Citation Format

Share Document