scholarly journals Inclusion of time‐varying covariates in cure survival models with an application in fertility studies

2019 ◽  
Vol 183 (1) ◽  
pp. 333-354
Author(s):  
Philippe Lambert ◽  
Vincent Bremhorst
Keyword(s):  
Survival Models ◽  
Time Varying ◽  
Time Varying Covariates

Analyzing first flowering event data using survival models with space and time-varying covariates

2013 ◽  
Vol 24 (5) ◽  
pp. 317-331 ◽  
Author(s):  
Maria A. Terres ◽  
Alan E. Gelfand ◽  
Jenica M. Allen ◽  
John A. Silander
Keyword(s):  
Survival Models ◽  
Time Varying ◽  
Event Data ◽  
Space And Time ◽  
Time Varying Covariates

scholarly journals A competing risks model with binary time varying covariates for estimation of breast cancer risks in BRCA1 families

2021 ◽  
pp. 096228022110089
Author(s):  
Yun-Hee Choi ◽  
Hae Jung ◽  
Saundra Buys ◽  
Mary Daly ◽  
Esther M John ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Competing Risks ◽  
Brca1 Mutation ◽  
Prophylactic Surgery ◽  
Time Varying ◽  
Cancer Risks ◽  
Competing Risks Model ◽  
Time Varying Covariates ◽  
Risk Reducing

Mammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy can potentially reduce breast cancer risks among mutation carriers of BRCA families. The evaluation of these interventions is usually complicated by the fact that their effects on breast cancer may change over time and by the presence of competing risks. We introduce a correlated competing risks model to model breast and ovarian cancer risks within BRCA1 families that accounts for time-varying covariates. Different parametric forms for the effects of time-varying covariates are proposed for more flexibility and a correlated gamma frailty model is specified to account for the correlated competing events.We also introduce a new ascertainment correction approach that accounts for the selection of families through probands affected with either breast or ovarian cancer, or unaffected. Our simulation studies demonstrate the good performances of our proposed approach in terms of bias and precision of the estimators of model parameters and cause-specific penetrances over different levels of familial correlations. We applied our new approach to 498 BRCA1 mutation carrier families recruited through the Breast Cancer Family Registry. Our results demonstrate the importance of the functional form of the time-varying covariate effect when assessing the role of risk-reducing salpingo oophorectomy on breast cancer. In particular, under the best fitting time-varying covariate model, the overall effect of risk-reducing salpingo oophorectomy on breast cancer risk was statistically significant in women with BRCA1 mutation.

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

The third birth in Great Britain

1988 ◽  
Vol 20 (4) ◽  
pp. 489-496 ◽  
Author(s):  
Robert E. Wright ◽  
John F. Ermisch ◽  
P. R. Andrew Hinde ◽  
Heather E. Joshi
Keyword(s):  
Labour Force ◽  
Strong Association ◽  
Labour Force Participation ◽  
Age At First Birth ◽  
Time Varying ◽  
Female Labour ◽  
Subsequent Fertility ◽  
Lower Fertility ◽  
Time Varying Covariates ◽  
The Relationship

SummaryThe relationship between female labour force participation, and other socioeconomic factors, and the probability of having a third birth is examined, using British data collected in the 1980 Women and Employment Survey, by hazard regression modelling with time-varying covariates. The results demonstrate the strong association between demographic factors, e.g. age at first birth and birth interval and subsequent fertility behaviour. Education appears to have little effect. Surprisingly, women who have spent a higher proportion of time as housewives have a lower risk of having a third birth. This finding is in sharp disagreement with the conventional expectation that cumulative labour force participation supports lower fertility. These findings are briefly compared with similar research carried out in Sweden.

Rejoinder to discussions on: A risk-based measure of time-varying prognostic discrimination for survival models

Biometrics ◽  
2016 ◽  
Vol 73 (3) ◽  
pp. 745-748
Author(s):  
C. Jason Liang ◽  
Patrick J. Heagerty
Keyword(s):  
Survival Models ◽  
Time Varying

scholarly journals A General and Robust Estimation Method for the Case-Time-Control Design

2019 ◽  
Vol 49 (1) ◽  
pp. 349-365
Author(s):  
Arvid Sjölander ◽  
Yang Ning
Keyword(s):  
Robust Estimation ◽  
Arbitrary Number ◽  
Estimation Method ◽  
Control Design ◽  
Time Varying ◽  
Time Control ◽  
Measured Time ◽  
Important Extension ◽  
Time Varying Covariates

The case-time-control design is a tool to control for measured, time-varying covariates that increase montonically in time within each subject while also controlling for all unmeasured covariates that are constant within each subject across time. Until recently, the design was restricted to data with only two timepoints and a single binary covariate, or data with a binary exposure. Sjölander (2017) made an important extension that allows for an arbitrary number of timepoints and covariates and a nonbinary exposure. However, his estimation method requires fairly strong model assumptions, and it may create bias if these assumptions are violated. We propose a novel estimation method for the case-time-control design, which to a large extent relaxes the model assumptions in Sjölander. We show in simulations that this estimation method performs well under a range of scenarios and gives consistent estimates when Sjölander’s estimation does not.

scholarly journals Irregular sleep and event schedules are associated with poorer self-reported well-being in US college students

SLEEP ◽  
2019 ◽  
Vol 43 (6) ◽  
Author(s):  
Dorothee Fischer ◽  
Andrew W McHill ◽  
Akane Sano ◽  
Rosalind W Picard ◽  
Laura K Barger ◽  
...  
Keyword(s):  
College Students ◽  
Cluster Analysis ◽  
Sleep Duration ◽  
Well Being ◽  
Early Morning ◽  
Time Varying ◽  
Clinical Trial Registration ◽  
Systematic Relationship ◽  
Phase Deviation ◽  
Time Varying Covariates

Abstract Study Objectives Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric—the Composite Phase Deviation (CPD)—to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students. Methods Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy–Alert, Sad–Happy, Sluggish–Energetic, Sick–Healthy, and Stressed–Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules. Results CPD for sleep was a significant predictor of average well-being (e.g. Stressed–Calm: b = −6.3, p < 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed–Calm, b = 1.0, p < 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular. Conclusions Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being. Clinical Trial Registration NCT02846077.

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