On Classifying At Risk Latent Zeros Using Zero Inflated Models Article Swipe
Dwivedi Dwivedi
,
M. Venkata Rao
,
Sada Nand Dwivedi
,
S. V. S. Deo
,
Rakesh Shukla
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.6339/jds.201404_12(2).0006
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.6339/jds.201404_12(2).0006
Count data often have excess zeros in many clinical studies. These zeros usually represent “disease-free state”. Although disease (event) free at the time, some of them might be at a high risk of having the putative outcome while others may be at low or no such risk. We postulate these zeros as a one of the two types, either as ‘low risk’ or as ‘high risk’ zeros for the disease process in question. Low risk zeros can arise due to the absence of risk factors for disease initiation/progression and/or due to very early stage of the disease. High risk zeros can arise due to the presence of significant risk factors for disease initiation/ progression or could be, in rare situations, due to misclassification, more specific diagnostic tests, or below the level of detection. We use zero inflated models which allows us to assume that zeros arise from one of the two separate latent processes-one giving low-risk zeros and the other high-risk zeros and subsequently propose a strategy to identify and classify them as such. To illustrate, we use data on the number of involved nodes in breast cancer patients. Of the 1152 patients studied, 38.8% were node- negative (zeros). The model predicted that about a third (11.4%) of negative nodes are “high risk” and the remaining (27.4%) are at “low risk” of nodal positivity. Posterior probability based classification was more appropriate compared to other methods. Our approach indicates that some node negative patients may be re-assessed for their diagnosis about nodal positivity and/or for future clinical management of their disease. The approach developed here is applicable to any scenario where the disease or outcome can be characterized by count-data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.6339/jds.201404_12(2).0006
- https://jds-online.org/journal/JDS/article/442/file/pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2182284466
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2182284466Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.6339/jds.201404_12(2).0006Digital Object Identifier
- Title
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On Classifying At Risk Latent Zeros Using Zero Inflated ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-03-09Full publication date if available
- Authors
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Dwivedi Dwivedi, M. Venkata Rao, Sada Nand Dwivedi, S. V. S. Deo, Rakesh ShuklaList of authors in order
- Landing page
-
https://doi.org/10.6339/jds.201404_12(2).0006Publisher landing page
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https://jds-online.org/journal/JDS/article/442/file/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://jds-online.org/journal/JDS/article/442/file/pdfDirect OA link when available
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Zero (linguistics), Disease, Mathematics, Statistics, Node (physics), Econometrics, Medicine, Internal medicine, Physics, Linguistics, Philosophy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2016: 1Per-year citation counts (last 5 years)
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14Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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