{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/26940"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/26940","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"Infection with MCPyV, KIV, WUV, and HPV as potential risk factors for lung cancer","abstract":"<bold>Background</bold>: Liquid bead microarray antibody (LBMA) assays are used to assess pathogen-cancer associations, yet analytic methods differ between studies, limiting comparability. <bold>Methods</bold>: To assess methods for analyzing LBMA data, we generated 10,000 Monte Carlo-type simulations of log-normal antibody distributions (exposure) with 200 cases and 200 controls (outcome). We estimated type I error rates, statistical power, and bias associated with three types of analytic techniques: (a) t-tests; (b) logistic regression with a linear predictor; and (c) logistic regression with predictors dichotomized according to four methods of defining cutpoints: 200 or 400 MFI determined a priori; the mean MFI among controls plus two standard deviations; and the optimal value based upon receiver operating characteristic (ROC) curve analysis. We also applied these models, and data visualizations (kernel density plots, ROC curves, predicted probability plots, Q-Q plots), to empirical data evaluating the association between HPV16 L1 antibody response and colorectal polyps to assess the consistency of the exposure-outcome relationship. <bold>Results</bold>: All strategies had acceptable type I error rates (0.030≤P≤0.048), except for the dichotomization according to optimal sensitivity and specificity (type I error rate = 0.27). Among the remaining methods, logistic regression with a linear predictor and t-tests had the highest power (Power=1.00 for both) to detect a mean difference of 1.0 MFI (median fluorescence intensity) on the log scale and were unbiased. Dichotomization methods upwardly biased the risk estimates. <bold>Conclusion</bold>: Logistic regression with linear predictors and unpaired t-tests were superior to logistic regression with dichotomized predictors for assessing disease associations with LBMA data.","abstract_html":"&lt;bold&gt;Background&lt;/bold&gt;: Liquid bead microarray antibody (LBMA) assays are used to assess pathogen-cancer associations, yet analytic methods differ between studies, limiting comparability. &lt;bold&gt;Methods&lt;/bold&gt;: To assess methods for analyzing LBMA data, we generated 10,000 Monte Carlo-type simulations of log-normal antibody distributions (exposure) with 200 cases and 200 controls (outcome). We estimated type I error rates, statistical power, and bias associated with three types of analytic techniques: (a) t-tests; (b) logistic regression with a linear predictor; and (c) logistic regression with predictors dichotomized according to four methods of defining cutpoints: 200 or 400 MFI determined a priori; the mean MFI among controls plus two standard deviations; and the optimal value based upon receiver operating characteristic (ROC) curve analysis. We also applied these models, and data visualizations (kernel density plots, ROC curves, predicted probability plots, Q-Q plots), to empirical data evaluating the association between HPV16 L1 antibody response and colorectal polyps to assess the consistency of the exposure-outcome relationship. &lt;bold&gt;Results&lt;/bold&gt;: All strategies had acceptable type I error rates (0.030≤P≤0.048), except for the dichotomization according to optimal sensitivity and specificity (type I error rate = 0.27). Among the remaining methods, logistic regression with a linear predictor and t-tests had the highest power (Power=1.00 for both) to detect a mean difference of 1.0 MFI (median fluorescence intensity) on the log scale and were unbiased. Dichotomization methods upwardly biased the risk estimates. &lt;bold&gt;Conclusion&lt;/bold&gt;: Logistic regression with linear predictors and unpaired t-tests were superior to logistic regression with dichotomized predictors for assessing disease associations with LBMA data.","abstract_has_math":false,"creators":["Colombara, Danny V"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Manhart, Lisa E."],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-10-20","date_published":"2014-10-20","updated_at":"2026-07-24T05:58:23Z","subjects":["Antibodies; Human Papillomavirus; Human Polyomavirus; liquid bead microarray; lung cancer; median fluorescence intensity"],"languages":["en_US"],"rights":["Copyright is held by the individual authors."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1773/26940","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Manhart, Lisa E."]},{"key":"dc:creator","label":"Author","values":["Colombara, Danny V"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-10-20T23:35:25Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-10-20T23:35:25Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-10-20"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Antibodies; Human Papillomavirus; Human Polyomavirus; liquid bead microarray; lung cancer; median fluorescence intensity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is held by the individual authors."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["Colombara_washington_0250E_12754.pdf"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1773/26940"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (Ph.D.)--University of Washington, 2014"]},{"key":"dc:description.abstract","label":"Abstract","values":["<bold>Background</bold>: Liquid bead microarray antibody (LBMA) assays are used to assess pathogen-cancer associations, yet analytic methods differ between studies, limiting comparability. <bold>Methods</bold>: To assess methods for analyzing LBMA data, we generated 10,000 Monte Carlo-type simulations of log-normal antibody distributions (exposure) with 200 cases and 200 controls (outcome). We estimated type I error rates, statistical power, and bias associated with three types of analytic techniques: (a) t-tests; (b) logistic regression with a linear predictor; and (c) logistic regression with predictors dichotomized according to four methods of defining cutpoints: 200 or 400 MFI determined a priori; the mean MFI among controls plus two standard deviations; and the optimal value based upon receiver operating characteristic (ROC) curve analysis. We also applied these models, and data visualizations (kernel density plots, ROC curves, predicted probability plots, Q-Q plots), to empirical data evaluating the association between HPV16 L1 antibody response and colorectal polyps to assess the consistency of the exposure-outcome relationship. <bold>Results</bold>: All strategies had acceptable type I error rates (0.030≤P≤0.048), except for the dichotomization according to optimal sensitivity and specificity (type I error rate = 0.27). Among the remaining methods, logistic regression with a linear predictor and t-tests had the highest power (Power=1.00 for both) to detect a mean difference of 1.0 MFI (median fluorescence intensity) on the log scale and were unbiased. Dichotomization methods upwardly biased the risk estimates. <bold>Conclusion</bold>: Logistic regression with linear predictors and unpaired t-tests were superior to logistic regression with dichotomized predictors for assessing disease associations with LBMA data."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Infection with MCPyV, KIV, WUV, and HPV as potential risk factors for lung cancer"]}]}],"canonical_facts":{"dc:contributor.advisor":["Manhart, Lisa E."],"dc:creator":["Colombara, Danny V"],"dc:date.accessioned":["2014-10-20T23:35:25Z"],"dc:date.available":["2014-10-20T23:35:25Z"],"dc:date.issued":["2014-10-20"],"dc:description":["Thesis (Ph.D.)--University of Washington, 2014"],"dc:description.abstract":["<bold>Background</bold>: Liquid bead microarray antibody (LBMA) assays are used to assess pathogen-cancer associations, yet analytic methods differ between studies, limiting comparability. <bold>Methods</bold>: To assess methods for analyzing LBMA data, we generated 10,000 Monte Carlo-type simulations of log-normal antibody distributions (exposure) with 200 cases and 200 controls (outcome). We estimated type I error rates, statistical power, and bias associated with three types of analytic techniques: (a) t-tests; (b) logistic regression with a linear predictor; and (c) logistic regression with predictors dichotomized according to four methods of defining cutpoints: 200 or 400 MFI determined a priori; the mean MFI among controls plus two standard deviations; and the optimal value based upon receiver operating characteristic (ROC) curve analysis. We also applied these models, and data visualizations (kernel density plots, ROC curves, predicted probability plots, Q-Q plots), to empirical data evaluating the association between HPV16 L1 antibody response and colorectal polyps to assess the consistency of the exposure-outcome relationship. <bold>Results</bold>: All strategies had acceptable type I error rates (0.030≤P≤0.048), except for the dichotomization according to optimal sensitivity and specificity (type I error rate = 0.27). Among the remaining methods, logistic regression with a linear predictor and t-tests had the highest power (Power=1.00 for both) to detect a mean difference of 1.0 MFI (median fluorescence intensity) on the log scale and were unbiased. Dichotomization methods upwardly biased the risk estimates. <bold>Conclusion</bold>: Logistic regression with linear predictors and unpaired t-tests were superior to logistic regression with dichotomized predictors for assessing disease associations with LBMA data."],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["Colombara_washington_0250E_12754.pdf"],"dc:identifier.uri":["http://hdl.handle.net/1773/26940"],"dc:language.iso":["en_US"],"dc:rights":["Copyright is held by the individual authors."],"dc:subject":["Antibodies; Human Papillomavirus; Human Polyomavirus; liquid bead microarray; lung cancer; median fluorescence intensity"],"dc:title":["Infection with MCPyV, KIV, WUV, and HPV as potential risk factors for lung cancer"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T05:58:23Z"}