Simple discrete‐time metapopulation models of patch occupancy

Oikos ◽  
2021 ◽  
Vol 130 (2) ◽  
pp. 310-320
Author(s):  
Nathan G. Marculis ◽  
Alan Hastings
Keyword(s):  
Discrete Time ◽  
Patch Occupancy ◽  
Metapopulation Models

Interplay between Local Dynamics and Dispersal in Discrete-time Metapopulation Models

2002 ◽  
Vol 218 (3) ◽  
pp. 273-288 ◽  
Author(s):  
ABDUL-AZIZ YAKUBU ◽  
CARLOS CASTILLO-CHAVEZ
Keyword(s):  
Discrete Time ◽  
Local Dynamics ◽  
Metapopulation Models

scholarly journals HABITAT PATCH OCCUPANCY DYNAMICS OF GLAUCOUS-WINGED GULLS (LARUS GLAUCESCENS) I: A DISCRETE-TIME MODEL

2008 ◽  
Vol 18 (4) ◽  
pp. 441-468 ◽  
Author(s):  
KARL W. PHILLIPS ◽  
SMRUTI P. DAMANIA ◽  
JAMES L. HAYWARD ◽  
SHANDELLE M. HENSON ◽  
CLARA J. LOGAN
Keyword(s):  
Discrete Time ◽  
Habitat Patch ◽  
Patch Occupancy ◽  
Time Model ◽  
Discrete Time Model ◽  
Occupancy Dynamics ◽  
Larus Glaucescens

scholarly journals Limit theorems for discrete-time metapopulation models

2010 ◽  
Vol 7 (0) ◽  
pp. 53-83 ◽  
Author(s):  
F.M. Buckley ◽  
P.K. Pollett
Keyword(s):  
Discrete Time ◽  
Limit Theorems ◽  
Metapopulation Models

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.

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