SURVIVAL MODELS FOR WITHIN-TREE POPULATIONS OF DENDROCTONUS FRONTALIS (COLEOPTERA: SCOLYTIDAE)

1977 ◽  
Vol 109 (8) ◽  
pp. 1071-1077 ◽  
Author(s):  
Robert N. Coulson ◽  
P. E. Pulley ◽  
J. L. Foltz ◽  
W. C. Martin ◽  
C. L. Kelley
Keyword(s):  
Emerging Adults ◽  
Population Change ◽  
Adult Population ◽  
Developmental Time ◽  
Survival Model ◽  
Dendroctonus Frontalis ◽  
Survival Models ◽  
Infested Tree ◽  
Initial Cohort ◽  
Tree Models

AbstractWithin-tree models of Dendroctonus frontalis generation survival from attacking adults to emerging adults and survivorship from eggs to emergence were developed for five regions of the infested tree bole of Pinus taeda L. The generation survival model (GS) describes the number of D. frontalis/attacking adult as a function of time at a specific height. The form of the model isYGS = 1.0 + C(1–e–20.0X)eA(1.0–X)B + ɛ.The survival model (S) describes the number of D. frontalis/100 eggs as a function of time at a specific height. The form of this model isYS = CeA(1.0–X)B + ɛ.The generation survival model indicated that the rate of survival was primarily a function of generation development time, rather than position on the infested tree bole. The rates also varied in different sections of the tree depending on the initial egg/attacking adult population of D. frontalis. The emergence/attack ratios for the tree sections were slightly greater at the top and bottom than in the middle of the infested bole.The survivorship curves, based on an initial cohort of 100 eggs, were similar for the various sections of the tree bole. Again, the rate of population change was primarily a function of developmental time, rather than position on the tree. The curves for the various tree sections were essentially the same.The combined action of the various biotic and abiotic mortality agents acting in the different sections of the tree resulted in essentially uniform survivorship throughout the infested portion of the tree bole.

scholarly journals An integrated system for modeling tree and stand survival

2017 ◽  
Vol 47 (10) ◽  
pp. 1405-1409 ◽  
Author(s):  
Quang V. Cao
Keyword(s):  
Survival Probability ◽  
Mathematical Structure ◽  
Survival Model ◽  
Integrated System ◽  
Survival Models ◽  
Tree Level ◽  
Individual Tree ◽  
Tree Survival ◽  
Different Levels ◽  
Model Approach

Traditionally, separate models have been used to predict the number of trees per unit area (stand-level survival) and the survival probability of an individual tree (tree-level survival) at a certain age. This study investigated the development of integrated systems in which survival models at different levels of resolution are related in a mathematical structure. Two approaches for modeling tree and stand survival were considered: deriving a stand-level survival model from a tree-level survival model (approach 1) and deriving a tree survival model from a stand survival model (approach 2). Both approaches rely on finding a tree diameter that yields a tree survival probability equal to the stand-level survival probability. The tree and stand survival models from either approach are conceptually compatible with each other but not numerically compatible. Parameters of these models can be estimated either sequentially or simultaneously. Results indicated that approach 2, with parameters estimated sequentially (first from the stand survival model and then from the derived tree survival model), performed best in predicting both tree- and stand-level survival. Although disaggregation did not help improve prediction of tree-level survival, this method can be used when numerical consistency between stand and tree survival is desired.

scholarly journals Joint Modeling of Longitudinal Change and Survival

GeroPsych ◽  
2011 ◽  
Vol 24 (4) ◽  
pp. 177-185 ◽  
Author(s):  
Graciela Muniz Terrera ◽  
Andrea M. Piccinin ◽  
Fiona Matthews ◽  
Scott M. Hofer
Keyword(s):  
Repeated Measures ◽  
Memory Performance ◽  
Joint Modeling ◽  
Disease Diagnosis ◽  
Careful Consideration ◽  
Survival Model ◽  
Longitudinal Change ◽  
Survival Models ◽  
Time Data ◽  
Aging Research

Joint longitudinal-survival models are useful when repeated measures and event time data are available and possibly associated. The application of this joint model in aging research is relatively rare, albeit particularly useful, when there is the potential for nonrandom dropout. In this article we illustrate the method and discuss some issues that may arise when fitting joint models of this type. Using prose recall scores from the Swedish OCTO-Twin Longitudinal Study of Aging, we fitted a joint longitudinal-survival model to investigate the association between risk of mortality and individual differences in rates of change in memory. A model describing change in memory scores as following an accelerating decline trajectory and a Weibull survival model was identified as the best fitting. This model adjusted for random effects representing individual variation in initial memory performance and change in rate of decline as linking terms between the longitudinal and survival models. Memory performance and change in rate of memory decline were significant predictors of proximity to death. Joint longitudinal-survival models permit researchers to gain a better understanding of the association between change functions and risk of particular events, such as disease diagnosis or death. Careful consideration of computational issues may be required because of the complexities of joint modeling methodologies.

scholarly journals Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Paul Riviere ◽  
Christopher Tokeshi ◽  
Jiayi Hou ◽  
Vinit Nalawade ◽  
Reith Sarkar ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Claims Data ◽  
Prediction Models ◽  
Survival Model ◽  
Localized Prostate Cancer ◽  
Survival Models ◽  
Accurate Estimation ◽  
Survival Prediction ◽  
Data Set ◽  
Comorbidity Index

PURPOSE Treatment decisions about localized prostate cancer depend on accurate estimation of the patient’s life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient’s health. METHODS We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model’s predictive capacity was compared with existing survival models with a metric of explained randomness (ρ2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS Our noncancer survival model included 143 covariates and had improved survival prediction (ρ2 = 0.60) compared with the Charlson comorbidity index (ρ2 = 0.26) and Elixhauser comorbidity index (ρ2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ2 = 0.71) to the Memorial Sloan Kettering prediction model (ρ2 = 0.68). CONCLUSION Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.

scholarly journals Does Flourishing Reduce Engagement in Unhealthy and Risky Lifestyle Behaviours in Emerging Adults?

2020 ◽  
Vol 17 (24) ◽  
pp. 9472
Author(s):  
Ernesta Sofija ◽  
Neil Harris ◽  
Dung Phung ◽  
Adem Sav ◽  
Bernadette Sebar
Keyword(s):  
Emerging Adults ◽  
Subjective Experience ◽  
Adult Population ◽  
Life Stage ◽  
Self Care ◽  
Sun Protection ◽  
Dietary Behaviour ◽  
Unhealthy Lifestyle ◽  
Cross Sectional Survey ◽  
Lifestyle Behaviours

Emerging adulthood is a transitional life stage with increased probability of risky and unhealthy lifestyle behaviours that are known to have strong links with premature mortality and morbidity. Wellbeing, as a positive subjective experience, is identified as a factor that encourages self-care and may steer individuals away from risky lifestyle behaviours. Investigating wellbeing–behaviour links in the emerging adult population may increase understanding of the factors that lead to, and ways to prevent, engagement in risky behaviours. This study examines the association between flourishing, that is, the experience of both high hedonic and eudaimonic wellbeing, and a broad range of risky and unhealthy lifestyle behaviours among emerging adults in Australia. A cross-sectional survey of 1155 emerging adults aged 18–25 years measured wellbeing, socio-demographics, and six groups of lifestyle behaviours surrounding substance use, physical activity, diet, sex, sun protection, and driving. Bivariate and multivariate statistics were used to analyse the data. The findings revealed that flourishing was negatively associated with more dangerous types of risk behaviours, such as driving under the influence of drugs, and positively associated with self-care behaviours, such as healthier dietary behaviour and sun protection. If enabling emerging adults to flourish can contribute to reduced engagement in risky/unhealthy lifestyle behaviours, then promoting it is an important goal for health promotion efforts not only because flourishing is desirable in its own right, but also to bring about sustainable change in behaviour. Further research is needed to inform the designs of such interventions.

THE LIFE CYCLE OF CORTICEUS GLABER (COLEOPTERA: TENEBRIONIDAE), A. FACULTATIVE PREDATOR OF THE SOUTHERN PINE BEETLE, DENDROCTONUS FRONTALIS (COLEOPTERA: SCOLYTIDAE)

1982 ◽  
Vol 114 (6) ◽  
pp. 535-537 ◽  
Author(s):  
Michael T. Smith ◽  
Richard A. Goyer
Keyword(s):  
Life Cycle ◽  
Head Capsule ◽  
Developmental Time ◽  
Dendroctonus Frontalis ◽  
Southern Pine Beetle ◽  
Southern Pine ◽  
Mature Larva ◽  
Larval Instars ◽  
Pine Beetle ◽  
Coleoptera Tenebrionidae

AbstractThe life cycle of Corticeus glaber (LeConte) was investigated at 25 °C and 60% R.H. The developmental time from egg to adult for C. glaber ranged from 30 to 41 days and five larval instars were determined from head capsule measurements. The mature larva is described.

scholarly journals Exploring University Students’ Coping Strategy Intentions for Cyberbullying

2016 ◽  
Vol 32 (3) ◽  
pp. 446-462 ◽  
Author(s):  
Alexandria Orel ◽  
Marilyn Campbell ◽  
Kelly Wozencroft ◽  
Eliza Leong ◽  
Melanie Kimpton
Keyword(s):  
University Students ◽  
Coping Strategies ◽  
Children And Adolescents ◽  
Emerging Adults ◽  
Coping Strategy ◽  
Adult Population ◽  
Emerging Adult ◽  
Future Research ◽  
Published Research ◽  
University Setting

Most of the published research on cyberbullying has been conducted with children and adolescents, so little is known about cyberbullying in other populations. This study examined cyberbullying within an emerging adult population in a university setting ( N = 282), and explored what coping strategies these individuals intended to use in response to future cyberbullying incidents. Blocking of the sender of the bullying message was found to be the most frequent intention to cope with cyberbullying among these emerging adults. It was also found that both gender and victimisation status (i.e., whether the emerging adult had, in the preceding twelve months, been a victim of cyberbullying) influenced coping strategy intentions. The implications for practice and future research are discussed.

scholarly journals An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

2021 ◽  
pp. 0272989X2110680
Author(s):  
Mathyn Vervaart ◽  
Mark Strong ◽  
Karl P. Claxton ◽  
Nicky J. Welton ◽  
Torbjørn Wisløff ◽  
...  
Keyword(s):  
Early Stage ◽  
Expected Value ◽  
Survival Model ◽  
Survival Models ◽  
Health Technologies ◽  
New Methods ◽  
Sample Information ◽  
Additional Data Collection ◽  
Substantial Uncertainty

Background Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily included any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.

scholarly journals Multiple-Membership Survival Analysis and Its Applications in Organizational Behavior and Management Research

2019 ◽  
pp. 109442811987745
Author(s):  
Hans Tierens ◽  
Nicky Dries ◽  
Mike Smet ◽  
Luc Sels
Keyword(s):  
Organizational Behavior ◽  
Large Scale ◽  
Model Building ◽  
Real Life ◽  
Hierarchical Structures ◽  
Survival Model ◽  
Survival Models ◽  
Data Set ◽  
Potential Applications ◽  
Multiple Membership

Multilevel paradigms have permeated organizational research in recent years, greatly advancing our understanding of organizational behavior and management decisions. Despite the advancements made in multilevel modeling, taking into account complex hierarchical structures in data remains challenging. This is particularly the case for models used for predicting the occurrence and timing of events and decisions—often referred to as survival models. In this study, the authors construct a multilevel survival model that takes into account subjects being nested in multiple environments—known as a multiple-membership structure. Through this article, the authors provide a step-by-step guide to building a multiple-membership survival model, illustrating each step with an application on a real-life, large-scale, archival data set. Easy-to-use R code is provided for each model-building step. The article concludes with an illustration of potential applications of the model to answer alternative research questions in the organizational behavior and management fields.

Sign in / Sign up

Export Citation Format

Share Document