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 Recurrent event data analysis with intermittently observed time-varying covariates

2016 ◽  
Vol 35 (18) ◽  
pp. 3049-3065 ◽  
Author(s):  
Shanshan Li ◽  
Yifei Sun ◽  
Chiung-Yu Huang ◽  
Dean A. Follmann ◽  
Richard Krause
Keyword(s):  
Data Analysis ◽  
Recurrent Event ◽  
Time Varying ◽  
Event Data ◽  
Recurrent Event Data ◽  
Time Varying Covariates ◽  
Event Data Analysis

Understanding Distressed Residential Transaction Discounts Across Price Segments and Market Conditions

2020 ◽  
Vol 42 (1) ◽  
pp. 151-182
Author(s):  
Ramya Rajajagadeesan Aroul ◽  
J. Andrew Hansz ◽  
Mauricio Rodriguez
Keyword(s):  
Transaction Costs ◽  
Time Varying ◽  
Market Segment ◽  
Market Conditions ◽  
Large Market ◽  
Space And Time ◽  
Wide Range ◽  
Time Periods ◽  
Market Areas

In the literature, there is a wide range of discounts associated with foreclosures. Comparisons across studies are difficult as they use different methodologies across large areas over different time periods. We employ a consistent methodology across space and time. We find modest discounts, within the range of typical transaction costs, in all but the highest priced market segment. Higher priced segments could explain prior findings of substantial discounts. We find that discounts are time-varying, with discounts increasing with market distress. A one-size-fits-all approach is not appropriate when estimating distressed transaction discounts across large market areas or under changing market conditions.

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 Penalized survival models for the analysis of alternating recurrent event data

Biometrics ◽  
2019 ◽  
Vol 76 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Lili Wang ◽  
Kevin He ◽  
Douglas E. Schaubel
Keyword(s):  
Recurrent Event ◽  
Survival Models ◽  
Event Data ◽  
Recurrent Event Data

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.

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