scholarly journals Finding the Smoothest Path to Success: Model Complexity and the Consideration of Nonlinear Patterns in Nest-Survival Data

The Condor ◽  
2010 ◽  
Vol 112 (3) ◽  
pp. 421-431 ◽  
Author(s):  
Max Post van der Burg ◽  
Larkin A. Powell ◽  
Andrew J. Tyre
Keyword(s):  
Survival Data ◽  
Nest Survival ◽  
Model Complexity ◽  
Success Model

Nest survival data confirm managed housing is an important component to the conservation of the eastern purple martin

2019 ◽  
Vol 43 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Daniel Raleigh ◽  
James D. Ray ◽  
Blake A. Grisham ◽  
Joe Siegrist ◽  
Daniel U. Greene
Keyword(s):  
Survival Data ◽  
Nest Survival ◽  
Purple Martin

scholarly journals Modeling Heterogeneity in Nest Survival Data

Biometrics ◽  
1999 ◽  
Vol 55 (2) ◽  
pp. 553-559 ◽  
Author(s):  
Ranjini Natarajan ◽  
Charles E. McCulloch
Keyword(s):  
Survival Data ◽  
Nest Survival

scholarly journals Comparison of model-building strategies for excess hazard regression models in the context of cancer epidemiology

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Camille Maringe ◽  
Aurélien Belot ◽  
Francisco Javier Rubio ◽  
Bernard Rachet
Keyword(s):  
Model Selection ◽  
Survival Data ◽  
Cancer Survival ◽  
Explanatory Power ◽  
Age At Diagnosis ◽  
Model Complexity ◽  
Linear Effect ◽  
Cancer Data ◽  
Selection Strategies ◽  
Selection Of Variables

Abstract Background Large and complex population-based cancer data are becoming broadly available, thanks to purposeful linkage between cancer registry data and health electronic records. Aiming at understanding the explanatory power of factors on cancer survival, the modelling and selection of variables need to be understood and exploited properly for improving model-based estimates of cancer survival. Method We assess the performances of well-known model selection strategies developed by Royston and Sauerbrei and Wynant and Abrahamowicz that we adapt to the relative survival data setting and to test for interaction terms. Results We apply these to all male patients diagnosed with lung cancer in England in 2012 (N = 15,688), and followed-up until 31/12/2015. We model the effects of age at diagnosis, tumour stage, deprivation, comorbidity and emergency presentation, as well as interactions between age and all of the above. Given the size of the dataset, all model selection strategies favoured virtually the same model, except for a non-linear effect of age at diagnosis selected by the backward-based selection strategies (versus a linear effect selected otherwise). Conclusion The results from extensive simulations evaluating varying model complexity and sample sizes provide guidelines on a model selection strategy in the context of excess hazard modelling.

scholarly journals Mayfield Logistic Regression: A Practical Approach for Analysis of Nest Survival

The Auk ◽  
2004 ◽  
Vol 121 (3) ◽  
pp. 707-716
Author(s):  
Kirsten R. Hazler
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Survival Data ◽  
Habitat Structure ◽  
Nest Survival ◽  
Seasonal Effects ◽  
Analysis Framework ◽  
Explanatory Variables ◽  
Mayfield Method ◽  
Experimental Treatments

Abstract Mayfield logistic regression is a method for analyzing nest-survival data that extends the traditional Mayfield estimator by incorporating explanatory variables (e.g. habitat structure, seasonal effects, or experimental treatments) in a logistic-regression analysis framework. Although Aebischer (1999) previously showed that logistic regression can be used to fit Mayfield models, few ornithologists have put that finding into practice. My purpose here is to reintroduce this underused method of nest-survival analysis, to compare its performance to that of a dedicated survival-analysis program (MARK), and to provide a practical guide for its use. Like the traditional Mayfield method, Mayfield logistic regression accounts for the num ber of “exposure days” for each nest and allows for uncertain fates (censoring), thus avoiding the bias introduced by typical applications of logistic regression. Mayfield logistic regression should be widely applicable when nests are found at various stages in the nesting cycle and multiple explanatory variables influencing nest survival are of interest.

Increased flexibility for modeling telemetry and nest-survival data using the multistate framework

2014 ◽  
Vol 78 (2) ◽  
pp. 224-230 ◽  
Author(s):  
Olivier Devineau ◽  
William L. Kendall ◽  
Paul F. Doherty ◽  
Tanya M. Shenk ◽  
Gary C. White ◽  
...  
Keyword(s):  
Survival Data ◽  
Nest Survival

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

Corporate Social Responsibility Implementation - Lessons from Uganda

10.33117/512 ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 47-69
Keyword(s):  
Sustainable Development ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Stakeholder Involvement ◽  
Implementation Process ◽  
Structured Interviews ◽  
Implementation Success ◽  
Success Model ◽  
Strategic Csr ◽  
Corporate Social

Purpose: This paper presents aspects of a Corporate Social Responsibility (CSR) Implementation Success Model to guide CSR engagements. Design/methodology/approach: A qualitative case methodology is used to investigate two CSR companies in Uganda. Semi-structured interviews with managers and stakeholders are conducted. Data triangulation includes reviewing CSR reports and documents, and visiting communities and CSR activities/projects mentioned in the case companies’ reports. Grounded theory guides the data analysis and aggregation. Findings: The findings culminate into a “CSR Implementation Success Model. ” Key aspects of CSR implementation success are identified as: (i) involvement of stakeholders and management (i.e., co-production) at the start and during every stage of CSR implementation; (ii) management of challenges and conflicts arising within/outside of the company itself; and (iii) feedback management or performance assessment—i.e., accountability via CSR communications and reporting. Stakeholder involvement and feedback management (accountability) are pivotal, though all three must be considered equally. Research limitations: The studied companies were large and well-established mature companies, so it is unclear whether newer companies and small and medium-sized enterprises would produce similar findings. Practical implications: Successful CSR implementation starts with a common but strategic understanding of what CSR means to the company. However, CSR implementation should (i) yield benefits that are tangible, and (ii) have a sustainable development impact because these two aspects form implementation benchmarks. Additionally, top management should be involved in CSR implementation, but with clear reasons and means. Originality/value: This paper unearths a CSR Implementation Success Model that amplifies views of “creating shared value” for sustainable development. It guides organizations towards strategic CSR, as opposed to the responsive CSR (returning profits to society) that largely dominates in developing countries. Additionally, it explains how to add value to the resource envelope lubricating the entire CSR implementation process

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