scholarly journals Graft survival of Pinus engelmannii Carr. in relation to two grafting techniques with dormant and sprouting buds

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12182
Author(s):  
Alberto Pérez-Luna ◽  
José Ciro Hernández-Díaz ◽  
Christian Wehenkel ◽  
Sergio Leonel Simental-Rodríguez ◽  
Javier Hernández-Velasco ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Survival Models ◽  
Risk Of Death ◽  
Hazards Model ◽  
Grafting Technique ◽  
Average Survival ◽  
Forest Genetic ◽  
Improvement Programs

Developing methods for successfully grafting forest species will be helpful for establishing asexual seed orchards and increasing the success of forest genetic improvement programs in Mexico. In this study we investigated the effects of two grafting techniques (side veneer and top cleft) and two phenological stages of the scion buds (end of latency and beginning of sprouting), in combination with other seven grafting variables, on the sprouting and survival of 120 intraspecific grafts of Pinus engelmannii Carr. The scions used for grafting were taken from a 5.5-year-old commercial forest plantation. The first grafting was performed on January 18 (buds at the end of dormancy) and the second on February 21 (buds at the beginning of sprouting). The data were examined by analysis of variance and a test of means and were fitted to two survival models (the Weibull’s accelerated failure time and the Cox’s proportional hazards model) and the respective hazard ratios were calculated. Survival was higher in the top cleft grafts made with buds at the end of latency, with 80% sprouting and an estimated average survival time of between 164 and 457 days after the end of the 6-month evaluation period. Four variables (grafting technique, phenological stage of the scion buds, scion diameter and rootstock height) significantly affected the risk of graft death in both survival models. Use of top cleft grafts with buds at the end of the latency stage, combined with scion diameters smaller than 11.4 mm and rootstock heights greater than 58.5 cm, was associated with a lower risk of death.

Modelling heterogeneity in survival data

1991 ◽  
Vol 28 (03) ◽  
pp. 695-701 ◽  
Author(s):  
Philip Hougaard
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Mixture Distribution ◽  
Survival Models ◽  
Risk Of Death ◽  
Hazards Model ◽  
Occupational Mortality ◽  
Heterogeneity Distribution ◽  
Biased Estimates

Ordinary survival models implicitly assume that all individuals in a group have the same risk of death. It may, however, be relevant to consider the group as heterogeneous, i.e. a mixture of individuals with different risks. For example, after an operation each individual may have constant hazard of death. If risk factors are not included, the group shows decreasing hazard. This offers two fundamentally different interpretations of the same data. For instance, Weibull distributions with shape parameter less than 1 can be generated as mixtures of constant individual hazards. In a proportional hazards model, neglect of a subset of the important covariates leads to biased estimates of the other regression coefficients. Different choices of distributions for the unobserved covariates are discussed, including binary, gamma, inverse Gaussian and positive stable distributions, which show both qualitative and quantitative differences. For instance, the heterogeneity distribution can be either identifiable or unidentifiable. Both mathematical and interpretational consequences of the choice of distribution are considered. Heterogeneity can be evaluated by the variance of the logarithm of the mixture distribution. Examples include occupational mortality, myocardial infarction and diabetes.

Modelling heterogeneity in survival data

1991 ◽  
Vol 28 (3) ◽  
pp. 695-701 ◽  
Author(s):  
Philip Hougaard
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Mixture Distribution ◽  
Survival Models ◽  
Risk Of Death ◽  
Hazards Model ◽  
Occupational Mortality ◽  
Heterogeneity Distribution ◽  
Biased Estimates

Ordinary survival models implicitly assume that all individuals in a group have the same risk of death. It may, however, be relevant to consider the group as heterogeneous, i.e. a mixture of individuals with different risks. For example, after an operation each individual may have constant hazard of death. If risk factors are not included, the group shows decreasing hazard. This offers two fundamentally different interpretations of the same data. For instance, Weibull distributions with shape parameter less than 1 can be generated as mixtures of constant individual hazards. In a proportional hazards model, neglect of a subset of the important covariates leads to biased estimates of the other regression coefficients. Different choices of distributions for the unobserved covariates are discussed, including binary, gamma, inverse Gaussian and positive stable distributions, which show both qualitative and quantitative differences. For instance, the heterogeneity distribution can be either identifiable or unidentifiable. Both mathematical and interpretational consequences of the choice of distribution are considered. Heterogeneity can be evaluated by the variance of the logarithm of the mixture distribution. Examples include occupational mortality, myocardial infarction and diabetes.

scholarly journals Dementia Adjudication Triggers Associated With Increased Mortality for Older Australians: Evidence From ASPREE

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 161-161
Author(s):  
Jane Banaszak-Holl ◽  
Xiaoping Lin ◽  
Jing Xie ◽  
Stephanie Ward ◽  
Henry Brodaty ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
The Elderly ◽  
Cox Proportional Hazards ◽  
Service Planning ◽  
Cox Proportional Hazards Model ◽  
Cognitive Test ◽  
Risk Of Death ◽  
Hazards Model ◽  
Using Data

Abstract Research Aims: This study seeks to understand whether those with dementia experience higher risk of death, using data from the ASPREE (ASPirin in Reducing Events in the Elderly) clinical trial study. Methods: ASPREE was a primary intervention trial of low-dose aspirin among healthy older people. The Australian cohort included 16,703 dementia-free participants aged 70 years and over at enrolment. Participants were triggered for dementia adjudication if cognitive test results were poorer than expected, self-reporting dementia diagnosis or memory problems, or dementia medications were detected. Incidental dementia was adjudicated by an international adjudication committee using the Diagnostic and Statistical Manual for Mental Disorders (DSM-IV) criteria and results of a neuropsychological battery and functional measures with medical record substantiation. Statistical analyses used a cox proportional hazards model. Results: As previously reported, 1052 participants (5.5%) died during a median of 4.7 years of follow-up and 964 participants had a dementia trigger, of whom, 575 (60%) were adjucated as having dementia. Preliminary analyses has shown that the mortality rate was higher among participants with a dementia trigger, regardless of dementia adjudication outcome, than those without (15% vs 5%, Χ2 = 205, p <.001). Conclusion: This study will provide important analyses of differences in the hazard ratio for mortality and causes of death among people with and without cognitive impairment and has important implications on service planning.

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

G CSF association with prolonged survival in HIV infected patients with disseminated Mycobacterium avium complex infection

1998 ◽  
Vol 9 (7) ◽  
pp. 394-399 ◽  
Author(s):  
Philip Keiser ◽  
Steven Rademacher ◽  
James Smith ◽  
Daniel Skiest
Keyword(s):  
Mycobacterium Avium ◽  
Mycobacterium Avium Complex ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cd4 Count ◽  
Risk Of Death ◽  
Hazards Model

Summary: Clarithromycin can ameliorate symptoms and improve survival in disseminated Mycobacterium avium complex DMAC infection. Optimal combina tions of this drug with other agents remain unknown. Granulocyte colony stimulating factor G CSF is a cytokine that can improve phagocytosis of M. avium complex in vitro . We aim to determine if G CSF administration is associated with improved survival in patients with DMAC in a retrospective, cohort study. Case records from 1991 to 1995 of 91 patients with DMAC at Parkland Memorial Hospital were reviewed for date of initial DMAC diagnosis, baseline CD4 count, race, sex, antiretroviral use, G CSF use, therapy for DMAC clarithromycin, ethambutol, ciprofloxacin and rifabutin and date of death. Of 91 cases identified, 25 were treated with G CSF and 66 never received this drug. Baseline characteristics were similar in each group including CD4 count 40 cells mm 3 vs 33 cells mm 3, P =0.68 , clarithromycin use 18 patients vs 52 patients, P =0.90 , and antiretroviral use 20 patients vs 42 patients, P =0.21 . Subjects treated with G CSF lived longer than those who did not receive this drug 355 days vs 211 days, P 0.01 . In the subgroup treated with clarithromycin, G CSF was also associated with increased survival 377 days vs 252 days, P 0.01 . Cox proportional hazards model showed a decreased risk of death in patients treated with G CSF RH=0.22, P 0.01 , clarithromycin RH=0.13, P 0.01 and ethambutol RH=0.51, P =0.02 . Ciprofloxacin and rifabutin use did not influence survival. These data support the use of clarithromycin and ethambutol in the treatment of DMAC. Addition of G CSF to a regimen of clarithromycin and ethambutol may increase survival in DMAC and should be studied prospectively.

Early Hb Response to ESA Treatment May Be An Indicator of Better Survival Prognosis

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4587-4587
Author(s):  
Jesse A Berlin ◽  
Peter Bowers ◽  
Sudhakar Rao ◽  
Suresh Aravind ◽  
Steven Sun ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Performance Status ◽  
Epoetin Alfa ◽  
Survival Prognosis ◽  
Risk Of Death ◽  
Hazards Model ◽  
Kaplan Meier ◽  
Increased Risk ◽  
Baseline Covariates

Abstract Chemotherapy induced anemia patients who respond to ESA treatment have hemoglobin increases within 4–8 weeks. Patients with inadequate Hb response after several weeks treatment often have their ESA dose escalated.. We conducted an exploratory analysis to test the hypothesis that safety outcomes in randomized studies of epoetin alfa might differ depending on the Hb response after 4–8 weeks of treatment. Methods: The analysis compared the survival across subsets of epoetin-alfa treated patients. Specifically, a landmark analysis was used, which defines a hemoglobin responder at a pre-specified point in time (in this case 4 & 8 weeks post treatment), and then examines survival subsequent to that point in time.Patients were categorized as “Hb responder” when their Hb increased by >0.5 g/dL; “Hb stable” when Hb change within ≤ 0.5g/dL; “Hb non-responder” when the Hb decreased >0.5 g/dL, compared to the value prior to epoetin-alfa treatment. Survival was estimated using the Kaplan-Meier method and comparisons were made between the responders and non-responders versus the stable group. Cox’s proportional hazards model was used to adjust for the following baseline covariates: hemoglobin prior to treatment, baseline performance status, and advanced disease at baseline. All analyses were stratified by study to account for any differences in the study populations and study conduct. Results: These exploratory findings suggest the possibility that patients identified as non-responders to ESAs after 4 or 8 weeks of ESA treatment may be at increased risk of death, and that this effect is most pronounced in the studies that treated patients beyond the correction of anemia. Although these analyses were adjusted for several key baseline covariates, it is unclear whether these effects result from treatment, or whether patients who fail to respond to epoetin alfa are inherently at increased risk of death (e.g., due underlying malignancy), regardless of their treatment status.

Frailty Approach for the Analysis of Clustered Failure Time Observations in Dental Research

2005 ◽  
Vol 84 (1) ◽  
pp. 54-58 ◽  
Author(s):  
S.K. Chuang ◽  
T. Cai ◽  
C.W. Douglass ◽  
L.J. Wei ◽  
T.B. Dodson
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Implant Failure ◽  
Failure Patterns ◽  
Hazards Model ◽  
Within Subjects ◽  
Immediate Implant

Because dental implant failure patterns tend to cluster within subjects, we hypothesized that the risk of implant failure varies among subjects. To address this hypothesis in the setting of clustered, correlated observations, we considered a retrospective cohort study where we identified a cohort having at least one implant placed. The cohort was composed of 677 patients who had 2349 implants placed. To test the hypothesis, we applied an innovative analytic method, i.e., the Cox proportional hazards model with frailty, to account for correlation within subjects and the heterogeneity of risk, i.e., frailty, among subjects for implant failure. Consistent with our hypothesis, risk for implant failure among subjects varied to a statistically significantly degree (p = 0.041). In addition, the risk for implant failure is significantly associated with several factors, including tobacco use, implant length, immediate implant placement, staging, well size, and proximity of adjacent implants or teeth.

scholarly journals An Inference-based Prognostic Framework for Health Management of Automotive Systems

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Chaitanya Sankavaram ◽  
Anuradha Kodali ◽  
Krishna Pattipati ◽  
Satnam Singh ◽  
Yilu Zhang ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Remaining Useful Life ◽  
Survival Functions ◽  
Time Data ◽  
Automotive Systems ◽  
Multiple Fault Diagnosis ◽  
Hazards Model ◽  
Useful Life

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.

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