scholarly journals Stopping Out and its Impact on College Graduation Among a Sample of Foster Care Alumni: A Joint Scale-Change Accelerated Failure Time Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 11-39
Author(s):  
Angelique G. Day ◽  
Richard J. Smith ◽  
Emiko A. Tajima
Keyword(s):  
Foster Care ◽  
Failure Time ◽  
College Graduation ◽  
Accelerated Failure Time ◽  
Time Analysis ◽  
Foster Care Alumni ◽  
Scale Change ◽  
Accelerated Failure ◽  
Stopping Out

Bayesian accelerated failure time analysis with application to veterinary epidemiology

2000 ◽  
Vol 19 (2) ◽  
pp. 221-237 ◽  
Author(s):  
Edward J. Bedrick ◽  
Ronald Christensen ◽  
Wesley O. Johnson
Keyword(s):  
Failure Time ◽  
Accelerated Failure Time ◽  
Time Analysis ◽  
Accelerated Failure ◽  
Veterinary Epidemiology

Absolute T Cell Counts At Diagnosis Predict Absolute Lymphocyte Counts Throughout Therapy For Pediatric Acute Lymphoblastic Leukemia: Evidence For Age-Related Loss Of T Cells and Poor Outcome

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2599-2599
Author(s):  
Catherine H. Boston ◽  
Jimin Wu ◽  
Diane Liu ◽  
Michele Guindani ◽  
Patrick A Zweidler-McKay
Keyword(s):  
T Cells ◽  
T Cell ◽  
Older Patients ◽  
Failure Time ◽  
Lymphoblastic Leukemia ◽  
Accelerated Failure Time ◽  
Time Analysis ◽  
Cell Numbers ◽  
Kaplan Meier ◽  
Accelerated Failure

Abstract Background Absolute lymphocyte count (ALC) during early therapy has been established as an independent prognostic factor in pediatric acute lymphoblastic leukemia (ALL). Importantly, ALC can refine minimal residual disease (MRD)-based risk stratification, particularly for MRD-positive patients. However age-dependent effects on ALC, the lymphocyte subtype (T, NK, B) which underlies this finding, and the role of ALC at relapse are not yet known. Methods Patients with de novo pediatric B-cell ALL had blood samples drawn biweekly during induction chemotherapy and lymphocyte subsets analyzed at different time points by flow cytometry. In addition, ALC was recorded at time points throughout induction and early consolidation chemotherapy, in maintenance therapy, and at time of relapse. Demographic, NCI risk, and treatment characteristics were also collected. Continuous variables were analyzed with Pearson’s coefficient, and categorical variables were analyzed with two-sided Barnard’s test. Survival statistics were measured with Kaplan-Meier curves and accelerated failure time analysis. Results A total of 21 children and young adults with B-cell ALL were identified with a median age of 17 years (range 0.5-35 years). Although not unexpectedly, T cells comprise the majority of lymphocytes that make up the ALC in nearly all cases. Interestingly, absolute CD3+ T cell count at diagnosis (ATC-0) correlates with ALC-15 (p=0.0008) and ALC during maintenance chemotherapy (p=0.01), revealing that T cell numbers at diagnosis predict total lymphocyte count recovery during early treatment as well as 8+ months later. The averages of ATC-0= 828 cells/ul and ALC-maintenance= 940 cells/ul were significant cutoffs as 89% of patients with low ATC-0 had low ALC during maintenance, and 75% of patients with high ATC-0 at diagnosis had high ALC during maintenance (p=0.03). In addition, age was inversely correlated with both ATC-0 and ALC-15 (p=0.035 and 0.006 respectively), linking that older patients have lower ALCs, which correlate to poor outcome. Indeed, 80% of patients with ALC-15 over 500 were < 10 years of age, while 87% of patients with ALC-15 under 500 cells/ul were >/= 10 years at diagnosis (p=0.007). Interestingly, patients with high ATC-0 had 100% 5 year OS while patients with low ATC-0 had 52% 5 year OS (p=0.1), a trend toward significance on Kaplan-Meier curves due to the low number of ATC-0 high patients (n=4) we had in this high risk patient population. However, ALC-15, ALC in maintenance, and age at diagnosis all independently had a significant correlation with RFS in accelerated failure time analysis (p=0.01, 0.0001, and 0.03 respectively). And importantly, ALC at relapse significantly correlated with OS in accelerated failure time analysis (p=0.0001). Discussion In this study, we demonstrate that ALC in ALL patients is inversely correlated with age, reflecting the normal decrease in ALC seen in healthy children, adolescents, and young adults. We also identified T cells as the predominant lymphocyte subtype comprising the ALC, which may reflect an immune reservoir in these patients. Importantly, T cell numbers at diagnosis (ATC-0) predict lymphocyte counts throughout treatment and also inversely correlate with age. This suggests that patients with low T cell numbers at diagnosis continue to have low T cell numbers late into treatment. As ALC and ATC decline with age, this could provide one potential mechanism for why older patients have worse outcomes from ALL. Clinically, ATC at diagnosis may identify patients at risk for relapse and help determine appropriate induction and post-induction therapy. In the future, patients with low ATC/ALC may benefit from immune-modulating therapy, which may overcome a limited immune reservoir. Acknowledgments St. Baldrick’s Foundation Fellow Award Kimberly Patterson Leukemia Research Fellowship Disclosures: No relevant conflicts of interest to declare.

Effects of covariates on alternating recurrent events in accelerated failure time models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moumita Chatterjee ◽  
Sugata Sen Roy
Keyword(s):  
Recurrent Events ◽  
Failure Time ◽  
Likelihood Method ◽  
Model Parameters ◽  
Accelerated Failure Time ◽  
Survival Times ◽  
Copula Functions ◽  
Event Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

AbstractIn this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

Accelerated failure time models for the analysis of competing risks

2019 ◽  
Vol 48 (3) ◽  
pp. 315-326
Author(s):  
Sangbum Choi ◽  
Hyunsoon Cho
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

scholarly journals NONPARAMETRIC IDENTIFICATION OF ACCELERATED FAILURE TIME COMPETING RISKS MODELS

2013 ◽  
Vol 29 (5) ◽  
pp. 905-919 ◽  
Author(s):  
Sokbae Lee ◽  
Arthur Lewbel
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Nonparametric Identification ◽  
Accelerated Failure Time ◽  
Rank Condition ◽  
Survivor Function ◽  
Auction Models ◽  
Regression Functions ◽  
Accelerated Failure ◽  
Identification Strategy

We provide new conditions for identification of accelerated failure time competing risks models. These include Roy models and some auction models. In our setup, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identified given covariates that are independent of latent errors, provided that a certain rank condition is satisfied. We present a simple example in which our rank condition for identification is verified. Our identification strategy does not depend on identification at infinity or near zero, and it does not require exclusion assumptions. Given our identification, we show estimation can be accomplished using sieves.

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