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 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

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

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.

Gamma Knife surgery combined with resection for treatment of a single brain metastasis: preliminary results

2010 ◽  
Vol 113 (Special_Supplement) ◽  
pp. 90-96 ◽  
Author(s):  
M. Yashar S. Kalani ◽  
Aristotelis S. Filippidis ◽  
Maziyar A. Kalani ◽  
Nader Sanai ◽  
David Brachman ◽  
...  
Keyword(s):  
Survival Data ◽  
Initial Treatment ◽  
Median Survival Time ◽  
Local Tumor Control ◽  
Survival Duration ◽  
Tumor Control ◽  
Local Tumor ◽  
Systemic Control ◽  
Cranial Metastasis

Object Resection and whole-brain radiation therapy (WBRT) have classically been the standard treatment for a single metastasis to the brain. The objective of this study was to evaluate the use of Gamma Knife surgery (GKS) as an alternative to WBRT in patients who had undergone resection and to evaluate patient survival and local tumor control. Methods The authors retrospectively reviewed the charts of 150 patients treated with a combination of stereotactic radiosurgery and resection of a cranial metastasis at their institution between April 1997 and September 2009. Patients who had multiple lesions or underwent both WBRT and GKS were excluded, as were patients for whom survival data beyond the initial treatment were not available. Clinical and imaging follow-up was assessed using notes from clinic visits and MR imaging studies when available. Follow-up data beyond the initial treatment and survival data were available for 68 patients. Results The study included 37 women (54.4%) and 31 men (45.6%) (mean age 60 years, range 28–89 years). In 45 patients (66.2%) there was systemic control of the primary tumor when the cranial metastasis was identified. The median duration between resection and radiosurgery was 15.5 days. The median volume of the treated cavity was 10.35 cm3 (range 0.9–45.4 cm3), and the median dose to the cavity margin was 15 Gy (range 14–30 Gy), delivered to the 50% isodose line (range 50%–76% isodose line). The patients' median preradiosurgery Karnofsky Performance Scale (KPS) score was 90 (range 40–100). During the follow-up period we identified 27 patients (39.7%) with recurrent tumor located either local or distant to the site of treatment. The median time from primary treatment of metastasis to recurrence was 10.6 months. The patients' median length of survival (interval between first treatment of cerebral metastasis and last follow-up) was 13.2 months. For the patient who died during follow-up, the median time from diagnosis of cerebral metastasis to death was 11.5 months. The median duration of survival from diagnosis of the primary cancer to last follow-up was 30.2 months. Patients with a pretreatment KPS score ≥ 90 had a median survival time of 23.2 months, and patients with a pretreatment KPS score < 90 had a median survival time of 10 months (p < 0.008). Systemic control of disease at the time of metastasis was not predictive of increased survival duration, although it did tend to improve survival. Conclusions Although the debate about the ideal form of radiation treatment after resection continues, these findings indicate that GKS combined with surgery offers comparable survival duration and local tumor control to WBRT for patients with a diagnosis of a single metastasis.

Gamma Knife surgery for the treatment of melanoma metastases: the effect of intratumoral hemorrhage on survival

2008 ◽  
Vol 109 (Supplement) ◽  
pp. 99-105 ◽  
Author(s):  
Andy J. Redmond ◽  
Michael L. DiLuna ◽  
Ryan Hebert ◽  
Jennifer A. Moliterno ◽  
Rani Desai ◽  
...  
Keyword(s):  
Gamma Knife ◽  
Survival Data ◽  
Tumor Burden ◽  
Gamma Knife Surgery ◽  
Survival Sex ◽  
Whole Brain Radiation ◽  
Melanoma Metastases ◽  
History Of ◽  
Independent Association ◽  
Intratumoral Bleeding

Object Gamma Knife surgery (GKS) improves overall survival in patients with malignant melanoma metastatic to the brain. In this study the authors investigated which patient- or treatment-specific factors influence survival of patients with melanoma brain metastases; they pay particular interest to pre- and post-GKS hemorrhage. Methods Demographic, treatment, and survival data on 59 patients with a total of 208 intracranial metastases who underwent GKS between 1998 and 2007 were abstracted from treatment records and from the Connecticut Tumor Registry. Multivariate analysis was used to identify factors that independently affected survival. Results Survival was significantly better in patients with solitary metastasis (p = 0.04), lesions without evidence of pre-GKS hemorrhage (p = 0.004), and in patients with total tumor volume treated < 4 cm3 (p = 0.02). Intratumoral bleeding occurred in 23.7% of patients pre-GKS. Intratumoral bleeding occurred at a mean of 1.8 months post-GKS at a rate of 15.2%. Unlike the marked effect of pretreatment bleeding, posttreatment bleeding did not independently affect survival. Sex, systemic control, race, metastases location, whole-brain radiation therapy, chemotherapy, history of antithrombotic medications, and cranial surgery had no independent association with survival. Conclusions These data corroborate previous findings that tumor burden (either as increased number or total volume of lesions) at the time of GKS is associated with diminished patient survival in those with intracerebral melanoma metastases. Patients who were noted to have hemorrhagic melanoma metastases prior to GKS appear to have a worse prognosis following GKS compared with patients with nonhemorrhagic metastases, despite similar rates of bleeding pre- and post-GKS treatment. Gamma Knife surgery itself does not appear to increase the rate of hemorrhage.

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