Regression analysis and variable selection for two‐stage multiple‐infection group testing data

Lin, Juexin; Wang, Dewei; Zheng, Qi

doi:10.1002/sim.8311

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…Dorfman [1] introduced group testing to screen syphilis among soldiers. Afterward, it has been widely applied to detect infected samples in a large population, including a variety of infectious diseases such as chlamydia, gonorrhea [2][3][4], HIV, the hepatitis B virus [5], West Nile virus [6], SARS-CoV-2 [7], and COVID-19 [8]. Group testing is also used in many other fields such as genetics [9], blood safety [10], drug discovery [11], and hyopneumoniae [12].…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric Additive Regression for High-Dimensional Group Testing Data

Zuo,

Ding,

Zhang

et al. 2024

Mathematics

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Group testing has been verified as a cost-effective and time-efficient approach, where the individual samples are pooled with a predefined group size for subsequent testing. Recent research has explored the integration of covariate information to improve the modeling of the group testing data. While existing works for high-dimensional data primarily focus on parametric models, this study considers a more flexible generalized nonparametric additive model. Nonlinear components are approximated using B-splines and model estimation under the sparsity assumption is derived employing group lasso. Theoretical results demonstrate that our method selects the true model with a high probability and provides consistent estimates. Numerical studies are conducted to illustrate the good performance of our proposed method, using both simulated and real data.

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Section: Introductionmentioning

confidence: 99%

“…Gregory et al [13] used the adaptive elastic net for group testing with high-dimensional covariates. Lin et al [3] considered variable selection for multiple-infection group testing data. Alternatively, semiparametric or nonparametric regression has also received attention, but it mainly focuses on low-dimensional group testing.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Additive Regression for High-Dimensional Group Testing Data

Zuo,

Ding,

Zhang

et al. 2024

Mathematics

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“…Li et al (2017) also allowed for imperfect diagnosis but studied only some aspects of optimal estimation strategies with pooled responses. Zhang et al (2013b) and Lin et al (2019) developed regression methods with multiple infections where the goals were only parameter estimation (i.e., optimization was not their goal). There is no work in the pooled testing literature that provides optimal strategies for estimation from multistage pooling.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Pooled Testing for Estimating the Prevalence of Multiple Diseases

Warasi

Hungerford

Lahmers

2022

JABES

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Pooled testing can enhance the efficiency of diagnosing individuals with diseases of low prevalence. Often, pooling is implemented using standard groupings (2, 5, 10, etc.). On the other hand, optimization theory can provide specific guidelines in finding the ideal pool size and pooling strategy. This article focuses on optimizing the precision of disease prevalence estimators calculated from multiplex pooled testing data. In the context of a surveillance application of animal diseases, we study the estimation efficiency (i.e., precision) and cost efficiency of the estimators with adjustments for the number of expended tests. This enables us to determine the pooling strategies that offer the highest benefits when jointly estimating the prevalence of multiple diseases, such as theileriosis and anaplasmosis. The outcomes of our work can be used in designing pooled testing protocols, not only in simple pooling scenarios but also in more complex scenarios where individual retesting is performed in order to identify positive cases. A software application using the shiny package in R is provided with this article to facilitate implementation of our methods.

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Post-selection inference in regression models for group testing data

Shen,

Gregory,

Huang

2024

Biometrics

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We develop a methodology for valid inference after variable selection in logistic regression when the responses are partially observed, that is, when one observes a set of error-prone testing outcomes instead of the true values of the responses. Aiming at selecting important covariates while accounting for missing information in the response data, we apply the expectation-maximization algorithm to compute maximum likelihood estimators subject to LASSO penalization. Subsequent to variable selection, we make inferences on the selected covariate effects by extending post-selection inference methodology based on the polyhedral lemma. Empirical evidence from our extensive simulation study suggests that our post-selection inference results are more reliable than those from naive inference methods that use the same data to perform variable selection and inference without adjusting for variable selection.

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Regression analysis and variable selection for two‐stage multiple‐infection group testing data

Cited by 3 publications

References 49 publications

Nonparametric Additive Regression for High-Dimensional Group Testing Data

Nonparametric Additive Regression for High-Dimensional Group Testing Data

Optimizing Pooled Testing for Estimating the Prevalence of Multiple Diseases

Post-selection inference in regression models for group testing data

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