Capture-Recapture Estimation
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School of Public Health, University of California at Berkeley, Betkeley, CA 94720 (address for correspondence Mathematics and Statistics Department, University of Minnesota, Duluth, MNKeywords:
Mark and recapture
Biologists often estimate separate survival and movement rites from radiotelemetry and markrecapture data from the same study population.We describe a method for combining these data types in a single model to obtain joint, potentially less biased estimates of survival and movement that use all available data.We furnish an example using wood thrushes (Hylocichla mustelina) captured at the Piedmont National Wildlife Refuge in central Georgia in 1996.The model structure allows estimation of survival and capture probabilities, as well as estimation of movements away from and into the study area.In addition, the model structure provides many possibilities for hypothesis testing.Using the combined model structure, we estimated that weekly survival of wood thrushes was 0.989 ?0.007 (-SE).Survival rates of banded and radiomarked individuals were not different (&[Sradioed, Sbanded] = log[Sradioed/Sbanded] = 0.0239, 95% CI = -0.0196 to 0.0486).Fidelity rates (weekly probability of remaining ih a stratum) did not differ between geographic strata (4 = 0.911 ?0.020; a&["11, •22] = 0.0161, 95% CI = -0.0309 to 0.0631), and recapture rates (p = 0.097 + 0.016) of banded and radiomarked individuals were not different (&[Pradioed, Pbandedl = 0.145, 95% CI = -0.510 to 0.800).Combining these data types in a common model resulted in more precise estimates of movement and recapture rates than separate estimation, but ability to detect stratum or mark-specific differences in parameters was weak.We conducted simulation trials to investigate the effects of varying study designs on parameter accuracy and statistical power to detect important differences.Parameter accuracy was high (relative bias [RBIAS] <2%) and confidence interval coverage close to nominal, except for survival estimates of banded birds for the "off study area" stratum, which were negatively biased (RBIAS -7 to -15%) when sample sizes were small (5-10 banded or radioed animals "released" per time interval).To provide adequate data for useful inference from this model, study designs should seek a minimum of 25 animals of each marking type observed (marked or observed via telemetry) in each time period and geographic stratum.
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USE AND LIMITATIONS OF THE CAPTURE-RECAPTURE METHOD IN DISEASE MONITORING WITH TWO DEPENDENT SOURCES
Capture-recapture techniques are employed increasingly to correct for underascertainment of cases in epidemiologic surveillance. A key assumption of the basic two-source capture-recapture method is the independence of sources, which is often violated in practice. This paper provides a quantitative comparison of the performance of the capture-recapture method and the traditional registration approach in disease monitoring with two dependent sources. If sources are negatively dependent, underascertainment of cases by the traditional registration approach is transformed into overestimation of case numbers with the capture-recapture method. This over-estimation can be extreme under certain conditions. Application of the capture-recapture method is therefore discouraged if negative source dependence is of concern. In other situations, the capture-recapture method can be a valuable tool to correct for underascertainment of cases. Although the correction remains imperfect if notifications from both sources are positively dependent, underestimation of case numbers is typically much less severe than with the traditional registration approach. I illustrate the findings for a broad range of registration scenarios and provide empirical examples from population-based cancer registration. I also discuss strategies that may minimize the degree of source dependence in the design and analysis of capture-recapture studies.
Mark and recapture
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This study is aimed at estimating the number of cases fo meningococcical disease that appeared in Barcelona between the years 1993 and 1994. We develop long-linear models for capture-recapture methods when there are more than two incomplete lists. The capture-recapture model that we used allows for the sources being not independent and for heterogeneity in the probability of being in a source.
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Mark and recapture
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Summary A simple technique of sequential estimation was proposed for capture‐recapture census by the Petersen method. In theory this technique makes it possible to secure automatically a required precision level for the population estimate to be obtained, irrespective of the population size. Some problems about its practical application were discussed.
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We are interested in an estimate of the usual residents in the Netherlands.Capture-recapture estimation with three registers enables us to estimate the size of the total population, of which the usual residents are a part.However, usual residence cannot be used as a covariate because it is not available in one of the registers.We approach this as a missing data problem.There are different methods available to handle missing data.In this manuscript we use Expectation Maximization (EM) algorithm and Predictive Mean Matching (PMM).The EM algorithm is often used in categorical data analysis, but PMM has the advantage of flexibility in the choice for a specific part of the observed data used for the imputation of the missing data.Four scenarios have been identified where the missing data are completed via either the EM algorithm or PMM imputation, resulting in different population size estimates for usual residence.It was found that the different scenarios lead to different population size estimates.Even small changes in the completed data lead to different population size estimates.In this study PMM imputation performs best according flexibility and it is theoretically better motivated.
Imputation (statistics)
Categorical variable
Mark and recapture
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본 연구는 capture-recapture 방법을 통해 두 가지 종류의 데이터를 결합하여 장애 인구수를 추정 하는 것이다. 이를 위해 데이터가 두 가지인 경우에 활용할 수 있는 여러 capture-recapture 추정 방법에 대해 살펴보았다. 등록 장애인 명부와 2017년 장애인 실태조사를 이용하여 기존 장애인구 수 추정 방안을 검토하고 기존의 추정량과 capture-recapture 방법을 활용한 추정량을 비교하여 효율적인 추정 방안을 고찰하였다. capture-recapture 방법을 사용한 추정량은 기존 장애 인구수 추정량보다 더 작은 분산 추정값을 갖고, 보정 가중치를 사용한 추정량은 기존 장애 인구수 추정량과 같은 추정값이지만 보다 적은 평균제곱오차값을 갖는 것을 확인하였다. 이 연구를 통해 여러한 조사 데이터로부터 모집단의 크기를 추정함에 있어 capture-recapture 방법을 활용할 수 있음을 확인하였다.
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