Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome
For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washi...
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| Formato: | info:eu-repo/semantics/preprint submittedVersion |
| Lenguaje: | Inglés |
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Arxiv
2024
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| Acceso en línea: | https://repositorio.utdt.edu/handle/20.500.13098/12888 https://doi.org/10.48550/arXiv.2407.04214 |
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I57-R163-20.500.13098-12888 |
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I57-R163-20.500.13098-128882024-07-12T07:00:11Z Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome Rotnitzky, Andrea Wolock, Charles J. Jacob, Susan Bennett, Julia C. Elias-Warren, Anna O’Hanlon, Jessica Kenny, Avi Jewell, Nicholas P. Weil, Ana A. Chu, Helen Y. Carone, Marco Covid-19 Statistical analysis Análisis estadístico Interval censoring Machine Learning nonparametric Survival analysis For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washington, participants were surveyed at least 28 days after testing positive and asked to report current symptom status. This study design yielded current status data: Outcome measurements for each respondent consisted only of the time of survey response and a binary indicator of whether symptoms had resolved by that time. Such study design benefits from limited risk of recall bias, but analyzing the resulting data necessitates specialized statistical tools. Here, we review methods for current status data and describe a novel application of modern nonparametric techniques to this setting. The proposed approach is valid under weaker assumptions compared to existing methods, allows use of flexible machine learning tools, and handles potential survey nonresponse. From the university study, we estimate that 19% of participants experienced ongoing symptoms 30 days after testing positive, decreasing to 7% at 90 days. Female sex, history of seasonal allergies, fatigue during acute infection, and higher viral load were associated with slower symptom resolution. Este preprint fue publicado el 05/07/2024 en Arxiv.org Se archiva en el Repositorio Digital Universidad Torcuato Di Tella para su preservarlo en el tiempo y para ayudar a su difusión. 2024-07-11T20:54:30Z 2024-07-11T20:54:30Z 2024-07-05 info:eu-repo/semantics/preprint info:eu-repo/semantics/submittedVersion https://repositorio.utdt.edu/handle/20.500.13098/12888 https://doi.org/10.48550/arXiv.2407.04214 eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/2.5/ar/ 33 p. application/pdf application/pdf Arxiv |
| institution |
Universidad Torcuato Di Tella |
| institution_str |
I-57 |
| repository_str |
R-163 |
| collection |
Repositorio Digital Universidad Torcuato Di Tella |
| language |
Inglés |
| orig_language_str_mv |
eng |
| topic |
Covid-19 Statistical analysis Análisis estadístico Interval censoring Machine Learning nonparametric Survival analysis |
| spellingShingle |
Covid-19 Statistical analysis Análisis estadístico Interval censoring Machine Learning nonparametric Survival analysis Rotnitzky, Andrea Wolock, Charles J. Jacob, Susan Bennett, Julia C. Elias-Warren, Anna O’Hanlon, Jessica Kenny, Avi Jewell, Nicholas P. Weil, Ana A. Chu, Helen Y. Carone, Marco Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome |
| topic_facet |
Covid-19 Statistical analysis Análisis estadístico Interval censoring Machine Learning nonparametric Survival analysis |
| description |
For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washington, participants were surveyed at least 28 days after testing positive and asked to report current symptom status. This study design yielded current status data: Outcome measurements for each respondent consisted only of the time of survey response and a binary indicator of whether symptoms had resolved by that time. Such study design benefits from limited risk of recall bias, but analyzing the resulting data necessitates specialized statistical tools. Here, we review methods for current status data and describe a novel application of modern nonparametric techniques to this setting. The proposed approach is valid under weaker assumptions compared to existing methods, allows use of flexible machine learning tools, and handles potential survey nonresponse. From the university study, we estimate that 19% of participants experienced ongoing symptoms 30 days after testing positive, decreasing to 7% at 90 days. Female sex, history of seasonal allergies, fatigue during acute infection, and higher viral load were associated with slower symptom resolution. |
| format |
info:eu-repo/semantics/preprint submittedVersion |
| author |
Rotnitzky, Andrea Wolock, Charles J. Jacob, Susan Bennett, Julia C. Elias-Warren, Anna O’Hanlon, Jessica Kenny, Avi Jewell, Nicholas P. Weil, Ana A. Chu, Helen Y. Carone, Marco |
| author_facet |
Rotnitzky, Andrea Wolock, Charles J. Jacob, Susan Bennett, Julia C. Elias-Warren, Anna O’Hanlon, Jessica Kenny, Avi Jewell, Nicholas P. Weil, Ana A. Chu, Helen Y. Carone, Marco |
| author_sort |
Rotnitzky, Andrea |
| title |
Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome |
| title_short |
Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome |
| title_full |
Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome |
| title_fullStr |
Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome |
| title_full_unstemmed |
Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome |
| title_sort |
investigating symptom duration using current status data: a case study of post-acute covid-19 syndrome |
| publisher |
Arxiv |
| publishDate |
2024 |
| url |
https://repositorio.utdt.edu/handle/20.500.13098/12888 https://doi.org/10.48550/arXiv.2407.04214 |
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