Journal of medical statistics and informatics最新文献

{"title":"SAS® Macros on Performing Competing Risks Survival Data: CIF Plot, Backward Elimination Fine & Gray’s Model and Cause-Specific Hazard Model","authors":"Chao Zhang, Yuan Liu, Yaqi Jia","doi":"10.7243/2053-7662-7-2","DOIUrl":"https://doi.org/10.7243/2053-7662-7-2","url":null,"abstract":"Abstract \u0000SAS/STAT® 14.1 released in SAS® 9.4 TS1M3 can perform non-parametric method to calculate cumulative incidence function (CIF) and can create CIF plots by using PROC LIFETEST, but it can’t directly show the","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45544420","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":"Joint modelling of time-to-clinical malaria and parasite count in a cohort in an endemic area.","authors":"Christopher C Stanley,&nbsp;Lawrence N Kazembe,&nbsp;Andrea G Buchwald,&nbsp;Mavuto Mukaka,&nbsp;Don P Mathanga,&nbsp;Michael G Hudgens,&nbsp;Miriam K Laufer,&nbsp;Tobias F Chirwa","doi":"10.7243/2053-7662-7-1","DOIUrl":"https://doi.org/10.7243/2053-7662-7-1","url":null,"abstract":"<p><strong>Background: </strong>In malaria endemic areas such as sub-Saharan Africa, repeated exposure to malaria results in acquired immunity to clinical disease but not infection. In prospective studies, time-to-clinical malaria and longitudinal parasite count trajectory are often analysed separately which may result in inefficient estimates since these two processes can be associated. Including parasite count as a time-dependent covariate in a model of time-to-clinical malaria episode may also be inaccurate because while clinical malaria disease frequently leads to treatment which may instantly affect the level of parasite count, standard time-to-event models require that time-dependent covariates be external to the event process. We investigated whether jointly modelling time-to-clinical malaria disease and longitudinal parasite count improves precision in risk factor estimates and assessed the strength of association between the hazard of clinical malaria and parasite count.</p><p><strong>Methods: </strong>Using a cohort data of participants enrolled with uncomplicated malaria in Malawi, a conventional Cox Proportional Hazards (PH) model of time-to-first clinical malaria episode with time-dependent parasite count was compared with three competing joint models. The joint models had different association structures linking a quasi-Poisson mixed-effects of parasite count and event-time Cox PH sub-models.</p><p><strong>Results: </strong>There were 120 participants of whom 115 (95.8%) had >1 follow-up visit and 100 (87.5%) experienced the episode. Adults >15 years being reference, log hazard ratio for children <5 years was 0.74 (95% CI: 0.17, 1.26) in the joint model with best fit vs. 0.62 (95% CI: 0.04, 1.18) from the conventional Cox PH model. The log hazard ratio for the 5-15 years was 0.72 (95% CI: 0.22, 1.22) in the joint model vs.0.63 (95% CI: 0.11, 1.17) in the Cox PH model. The area under parasite count trajectory was strongly associated with the risk of clinical malaria, with a unit increase corresponding to-0.0012 (95% CI: -0.0021, -0.0004) decrease in log hazard ratio.</p><p><strong>Conclusion: </strong>Jointly modelling longitudinal parasite count and time-to-clinical malaria disease improves precision in log hazard ratio estimates compared to conventional time-dependent Cox PH model. The improved precision of joint modelling may improve study efficiency and allow for design of clinical trials with relatively lower sample sizes with increased power.</p>","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"7 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6594707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37366888","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":"Volume and Value of Big Healthcare Data.","authors":"I. Dinov","doi":"10.7243/2053-7662-4-3","DOIUrl":"https://doi.org/10.7243/2053-7662-4-3","url":null,"abstract":"Modern scientific inquiries require significant data-driven evidence and trans-disciplinary expertise to extract valuable information and gain actionable knowledge about natural processes. Effective evidence-based decisions require collection, processing and interpretation of vast amounts of complex data. The Moore's and Kryder's laws of exponential increase of computational power and information storage, respectively, dictate the need rapid trans-disciplinary advances, technological innovation and effective mechanisms for managing and interrogating Big Healthcare Data. In this article, we review important aspects of Big Data analytics and discuss important questions like: What are the challenges and opportunities associated with this biomedical, social, and healthcare data avalanche? Are there innovative statistical computing strategies to represent, model, analyze and interpret Big heterogeneous data? We present the foundation of a new compressive big data analytics (CBDA) framework for representation, modeling and inference of large, complex and heterogeneous datasets. Finally, we consider specific directions likely to impact the process of extracting information from Big healthcare data, translating that information to knowledge, and deriving appropriate actions.","PeriodicalId":91324,"journal":{"name":"Journal of medical statistics and informatics","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.7243/2053-7662-4-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71382509","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}