A Lower Bound Model for Multiple Record Systems Estimation with Heterogeneous Catchability

Rivest, Louis‐Paul

doi:10.2202/1557-4679.1283

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…However, these distributions may not always be appropriate if there exists strong heterogeneity or overdispersion in the data. For example, in Section 5.1, we incorporated covariates to account for heterogeneity in capture probabilities; this modelling strategy works well in practice, however, there still may be residual heterogeneity (see Rivest, for a study on this). At this stage, extending the weighted partial likelihood to account for heterogeneity and overdispersion is not quite clear to us.…”

Section: Discussionmentioning

confidence: 99%

A weighted partial likelihood approach for zero‐truncated models

2019

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Zero‐truncated data arises in various disciplines where counts are observed but the zero count category cannot be observed during sampling. Maximum likelihood estimation can be used to model these data; however, due to its nonstandard form it cannot be easily implemented using well‐known software packages, and additional programming is often required. Motivated by the Rao–Blackwell theorem, we develop a weighted partial likelihood approach to estimate model parameters for zero‐truncated binomial and Poisson data. The resulting estimating function is equivalent to a weighted score function for standard count data models, and allows for applying readily available software. We evaluate the efficiency for this new approach and show that it performs almost as well as maximum likelihood estimation. The weighted partial likelihood approach is then extended to regression modelling and variable selection. We examine the performance of the proposed methods through simulation and present two case studies using real data.

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Section: Discussionmentioning

confidence: 99%

A weighted partial likelihood approach for zero‐truncated models

2019

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“…The “Normal” model incorporates heterogeneity as a Gaussian mixing distribution [ 37 ]. The Poisson, Darroch, and Gamma options incorporate different heterogeneity correction columns into the design matrix.…”

Section: Methodsmentioning

confidence: 99%

Population Size Estimation From Capture-Recapture Studies Using shinyrecap: Design and Implementation of a Web-Based Graphical User Interface

McIntyre

Fellows

Gutreuter

et al. 2022

JMIR Public Health Surveill

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Background Population size estimates (PSE) provide critical information in determining resource allocation for HIV services geared toward those at high risk of HIV, including female sex workers, men who have sex with men, and people who inject drugs. Capture-recapture (CRC) is often used to estimate the size of these often-hidden populations. Compared with the commonly used 2-source CRC, CRC relying on 3 (or more) samples (3S-CRC) can provide more robust PSE but involve far more complex statistical analysis. Objective This study aims to design and describe the Shiny application (shinyrecap), a user-friendly interface that can be used by field epidemiologists to produce PSE. Methods shinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages (eg, Rcapture, dga, LCMCR). Additionally, the application may be accessed online or run locally on the user’s machine. Results The application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by the analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided. Conclusions Through a use case, we demonstrated the broad utility of this powerful tool with 3S-CRC data to produce PSE for female sex workers in a subnational unit of a country in sub-Saharan Africa.

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Estimating population sizes with the Rasch model

Mao

Cui-ying

et al. 2016

Ann Inst Stat Math

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A Lower Bound Model for Multiple Record Systems Estimation with Heterogeneous Catchability

Cited by 7 publications

References 23 publications

A weighted partial likelihood approach for zero‐truncated models

A weighted partial likelihood approach for zero‐truncated models

Population Size Estimation From Capture-Recapture Studies Using shinyrecap: Design and Implementation of a Web-Based Graphical User Interface

Estimating population sizes with the Rasch model

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