{"title":"Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes.","authors":"Yanqing Wang, Yingqi Zhao, Yingye Zheng","doi":"10.1007/s12561-022-09343-9","DOIUrl":"https://doi.org/10.1007/s12561-022-09343-9","url":null,"abstract":"<p><p>Novel biomarkers, in combination with currently available clinical information, have been sought to enhance clinical decision making in many branches of medicine, including screening, surveillance and prognosis. An individualized clinical decision rule (ICDR) is a decision rule that matches subgroups of patients with tailored medical regimen based on patient characteristics. We proposed new approaches to identify ICDRs by directly optimizing a risk-adjusted clinical benefit function that acknowledges the tradeoff between detecting disease and over-treating patients with benign conditions. In particular, we developed a novel plug-in algorithm to optimize the risk-adjusted clinical benefit function, which leads to the construction of both nonparametric and linear parametric ICDRs. In addition, we proposed a novel approach based on the direct optimization of a smoothed ramp loss function to further enhance the robustness of a linear ICDR. We studied the asymptotic theories of the proposed estimators. Simulation results demonstrated good finite sample performance for the proposed estimators and improved clinical utilities when compared to standard approaches. The methods were applied to a prostate cancer biomarker study.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 3","pages":"564-581"},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270673/pdf/nihms-1901289.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10016922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A generalized interrupted time series model for assessing complex health care interventions.","authors":"Maricela Cruz, Hernando Ombao, Daniel L Gillen","doi":"10.1007/s12561-022-09346-6","DOIUrl":"10.1007/s12561-022-09346-6","url":null,"abstract":"<p><p>Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology able to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS designs primarily focus on continuous-valued outcomes. We propose the \"Generalized Robust ITS\" (GRITS) model appropriate for outcomes whose underlying distribution belongs to the exponential family of distributions, thereby expanding the available methodology to adequately model binary and count responses. GRITS formally implements a test for the existence of a change point in discrete ITS. The methodology proposed is able to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation pre- and post-intervention. The methodology is illustrated by analyzing patient falls from a hospital that implemented and evaluated a new care delivery model in multiple units.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 3","pages":"582-610"},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208393/pdf/nihms-1884816.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9558674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Semiparametric Density Ratio Model for Survival Data with a Cure Fraction","authors":"Weibin Zhong, G. Diao","doi":"10.1007/s12561-022-09357-3","DOIUrl":"https://doi.org/10.1007/s12561-022-09357-3","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"217 - 241"},"PeriodicalIF":1.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46697910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Step-Wise Multiple Testing for Linear Regression Models with Application to the Study of Resting Energy Expenditure","authors":"Junyi Zhang, Zimian Wang, Zhezhen Jin, Zhiliang Ying","doi":"10.1007/s12561-022-09355-5","DOIUrl":"https://doi.org/10.1007/s12561-022-09355-5","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"163 - 192"},"PeriodicalIF":1.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42982280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bayesian Framework for Causal Inference with Principal Stratification and Clusters","authors":"Li He, Yu-Bo Wang, W. Bridges, Zhulin He, S. Che","doi":"10.1007/s12561-022-09351-9","DOIUrl":"https://doi.org/10.1007/s12561-022-09351-9","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"15 1","pages":"114 - 140"},"PeriodicalIF":1.0,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41559868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introduction to Special Issue on Leveraging External Data to Improve Trial Efficiency","authors":"Lanju Zhang, Naitee Ting","doi":"10.1007/s12561-022-09348-4","DOIUrl":"https://doi.org/10.1007/s12561-022-09348-4","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 1","pages":"193 - 196"},"PeriodicalIF":1.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43439217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach","authors":"An-Shun Tai, Chun-Chao Wang, Wen-Ping Hsieh","doi":"10.1007/s12561-022-09344-8","DOIUrl":"https://doi.org/10.1007/s12561-022-09344-8","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45317080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Discriminatory capacity of prenatal ultrasound measures for large-for-gestational-age birth: A Bayesian approach to ROC analysis using placement values.","authors":"Soutik Ghosal, Zhen Chen","doi":"10.1007/s12561-021-09311-9","DOIUrl":"https://doi.org/10.1007/s12561-021-09311-9","url":null,"abstract":"<p><p>Predicting large fetuses at birth is of great interest to obstetricians. Using an NICHD Scandinavian Study that collected longitudinal ultrasound examination data during pregnancy, we estimate diagnostic accuracy parameters of estimated fetal weight (EFW) at various times during pregnancy in predicting large-for-gestational-age. We adopt a placement value based Bayesian regression model with random effects to estimate ROC curves. The use of placement values allows us to model covariate effects directly on the ROC curves and the adoption of a Bayesian approach accommodates the <i>a priori</i> constraint that an ROC curve of EFW near delivery should dominate another further away. The proposed methodology is shown to perform better than some alternative approaches in simulations and its application to the Scandinavian Study data suggests that diagnostic accuracy of EFW can improve about 65% from week 17 to 37 of gestation.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 1","pages":"1-22"},"PeriodicalIF":1.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-021-09311-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9233024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lanju Zhang, Zailong Wang, Li Wang, Lu Cui, J. Sokolove, Ivan S. F. Chan
{"title":"A Simple Approach to Incorporating Historical Control Data in Clinical Trial Design and Analysis","authors":"Lanju Zhang, Zailong Wang, Li Wang, Lu Cui, J. Sokolove, Ivan S. F. Chan","doi":"10.1007/s12561-022-09342-w","DOIUrl":"https://doi.org/10.1007/s12561-022-09342-w","url":null,"abstract":"","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"14 1","pages":"216 - 236"},"PeriodicalIF":1.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43178218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}