Biostatistics and Epidemiology最新文献

{"title":"An interview with Constantine Gatsonis.","authors":"Zheyu Wang","doi":"10.1080/24709360.2025.2463817","DOIUrl":"https://doi.org/10.1080/24709360.2025.2463817","url":null,"abstract":"<p><p>Dr. Constantine Achilleos Gatsonis is the Henry Ledyard Goddard University Professor of Biostatistics at Brown. He was born in Velanidia, Kozani - a small village in Western Macedonia, Greece. He came to the US on a scholarship and enrolled in Union College for a year before transferring to Princeton University. He studied mathematics at Princeton, graduating in 1976, then completed a doctorate in mathematical statistics at Cornell in 1981, with the Lawrence Brown as his advisor. After graduation, Dr. Gatsonis had academic appointments at Rutgers University-New Brunswick, the University of Massachusetts Amherst, and a visiting professorship at Carnegie Mellon University, where he continued his research on Bayesian methods and also developed interests in Biostatistics. He joined Harvard Medical School as an assistant professor in 1988, to help form the Department of Health Care Policy. In 1995, he was recruited by Brown, where he founded the Center for Statistical Sciences and later the Department of Biostatistics. Dr. Gatsonis is well known for his work with the evaluation of diagnostic and screening tests, including hierarchical models, ROC meta-analysis, comparative effectiveness, and beyond. He has contributed extensively to the development of methods for medical technology assessment and health services and outcomes research. He was the Network Statistician of the American College of Radiology Imaging Network (ACRIN) since its formation in 1999 and the Group Statistician for the combined collaborative group ECOG-ACRIN which conducts multi-center clinical studies across the spectrum of cancer care founded by the National Cancer Institutes (NCI). He helped to establish the Health Policy Statistics Section (HPSS) of the American Statistical Association (ASA), co-chaired the First International Conference on Health Policy Research, now called the International Conference on Health Policy Statistics (ICHPS), and founded the Journal of Health Services and Outcomes Research. He chaired the Committee on Applied and Theoretical Statistics (CATS) of the National Academy of Science and was a member of the Committee on National Statistics and the Committee on Reproducibility and Replicability in Science. He cochaired the Committee on the Needs of the Forensic Sciences Community and served on the Board of Mathematical Sciences and Applications and several Institute of Medicine Committees. Dr. Gatsonis is an elected fellow of the ASA and AcademyHealth. He received a Long-Term Excellence Award from the HPSS of the ASA in 2015, the Marvin Zelen Leadership Award in Statistical Science from the Harvard T.H. Chan School of Public Health in 2018, and was named Mosteller Statistician of the Year by the Boston Chapter of the ASA in 2019. This interview was conducted virtually over 3 sessions on June 26, July 1 and July 10, 2024. The conversation spans beyond what we eventually included here.</p>","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13663830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196934","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":"Finite Markov chains with absorbing states and mis-specified random effects: application to cognitive data.","authors":"Pei Wang, Changrui Liu, Jiyeon Park, Suzanne L Tyas, Richard J Kryscio","doi":"10.1080/24709360.2025.2451519","DOIUrl":"10.1080/24709360.2025.2451519","url":null,"abstract":"<p><p>Finite Markov chains with absorbing states are valuable tools for analyzing longitudinal data with categorical responses. However, defining the one-step transition probabilities in terms of fixed and random effects presents challenges due to the large number of unknown parameters involved. To address this, we employ a marginal model to estimate the fixed effects across various choices of the distribution governing the random effects. Subsequently, we utilize an <i>h</i>-likelihood method to estimate the random effects based on these fixed effect estimates. The estimation approach is applied to analyze longitudinal cognitive data from the Nun Study. Our findings highlight that the fixed effects remain relatively robust across a wide range of assumptions. However, the analysis of random effects utilizing tools such as AIC, Q-Q plots, and gradient plots appears to be sensitive to mis-specifications in the distribution of the random effects. Our proposed approach allows researchers to verify the assumptions of random effects and provides more accurate estimation of these effects. Additionally, the precisely estimated random effects enable researchers to identify individuals at high risk for absorbing states (e.g., incurable diseases) and to determine the progression rates for certain diseases.</p>","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087578","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":"Mean Cost and Cost-Effectiveness Ratios with Censored Data: a Tutorial and SAS^® Macros.","authors":"Eduard Poltavskiy, Dingning Liu, Shuai Chen, Heejung Bang, Hongwei Zhao","doi":"10.1080/24709360.2025.2565537","DOIUrl":"10.1080/24709360.2025.2565537","url":null,"abstract":"<p><p>Censoring is an unignorable issue when analyzing survival data and/or medical cost data. Medical costs may be viewed as a type of survival data-in that they accrue over time until an endpoint such as death-or a 'mark' variable. Since Lin et al. (1997) and Mushlin et al. (1998) published landmark papers on this topic, censored cost data have been extensively studied. In this tutorial, we explain how to estimate mean cost and cost-effectiveness ratios, along with three examples under two different data scenarios: when only total cost data (so one observation per person) or longitudinal data (or cost history) are available. We also provide an updated literature review. SAS codes in the supplement could be useful to practitioners and data analysts.</p>","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12674633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679016","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":"On the impact of cancer in the Kashmir valley: analyzing incidence patterns and burden","authors":"F. A. Zargar, Showkat A. Zargar, Mudasir Ashraf Shah, Sudip Malik","doi":"10.1080/24709360.2024.2443714","DOIUrl":"https://doi.org/10.1080/24709360.2024.2443714","url":null,"abstract":"","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147894710","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":"Adjusting for bias due to measurement error in functional quantile regression models with error-prone functional and scalar covariates.","authors":"Xiwei Chen, Heyang Ji, Yuanyuan Luan, Roger S Zoh, Lan Xue, Sneha Jadhav, Carmen D Tekwe","doi":"10.1080/24709360.2024.2405439","DOIUrl":"10.1080/24709360.2024.2405439","url":null,"abstract":"<p><p>Wearable devices enable the continuous monitoring of physical activity (PA) but generate complex functional data with poorly characterized errors. Most work on functional data views the data as smooth, latent curves obtained at discrete time intervals with some random noise with mean zero and constant variance. Viewing this noise as homoscedastic and independent ignores potential serial correlations. Our preliminary studies indicate that failing to account for these serial correlations can bias estimations. In dietary assessments, epidemiologists often use self-reported measures based on food frequency questionnaires that are prone to recall bias. With the increased availability of complex, high-dimensional functional, and scalar biomedical data potentially prone to measurement errors, it is necessary to adjust for biases induced by these errors to permit accurate analyses in various regression settings. However, there has been limited work to address measurement errors in functional and scalar covariates in the context of quantile regression. Therefore, we developed new statistical methods based on simulation extrapolation (SIMEX) and mixed effects regression with repeated measures to correct for measurement error biases in this context. We conducted simulation studies to establish the finite sample properties of our new methods. The methods are illustrated through application to a real data set.</p>","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11737618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013134","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":"The analysis of Salmonella’s ability to survive in different external environments","authors":"Wesam R. Kadhum, Lyudmila Sviridova, Dmitry Snegirev","doi":"10.1080/24709360.2023.2265277","DOIUrl":"https://doi.org/10.1080/24709360.2023.2265277","url":null,"abstract":"AbstractThe work aims to analyze the survival of the Salmonella pathogen in various objects of the outdoor environment (water, soil). Survival rates for Salmonella isolated in agar-agar from aqueous media (distilled water, tap water, well water, seawater) and soil were investigated. Every seven days, samples were subjected to bacteriological analysis, where they were streaked onto nutrient agar medium at a temperature of 36°C to determine the presence of viable Salmonella. In cases where Salmonella was not detected, microscopic examination was conducted to ascertain the presence of dead bacteria. Seasonal aspects of calf morbidity due to salmonellosis were examined. Salmonella survival in distilled water was maximal and exceeded four months; in well water, it survived two months (p ≤ 0.05 with distilled water); the survival rate in tap and sea water was one month (p ≤ 0.01). Salmonella was viable for more than eight months in artificially contaminated chernozem, five months in grey forest soil (p ≤ 0.05), and for at least three months in the soil at 0°C Salmonella (p ≤ 0.01). Salmonellosis is more common in 4–35% of calves 1–3 months of age. Salmonella can live outdoors, remaining viable and virulent in soil and water for 5–8 months.KEYWORDS: Salmonellaeexternal environmentaquatic environmentsoilsurvivalvirulence Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData will be available on request.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135798448","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":"Notice of duplicate publication: public transportation network scan for rapid surveillance","authors":"","doi":"10.1080/24709360.2023.2275481","DOIUrl":"https://doi.org/10.1080/24709360.2023.2275481","url":null,"abstract":"","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799650","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":"Global Odds Model with Proportional Odds and Trend Odds Applied to Gross and Microscopic Brain Infarcts.","authors":"Ana W Capuano, Robert Wilson, Julie A Schneider, Sue E Leurgans, David A Bennett","doi":"10.1080/24709360.2018.1500089","DOIUrl":"10.1080/24709360.2018.1500089","url":null,"abstract":"<p><p>Medical and epidemiological researchers commonly study ordinal measures of symptoms or pathology. Some of these studies involve two correlated ordinal measures. There is often an interest in including both measures in the modeling. It is common to see analyses that consider one of the measures as a predictor in the model for the other measure as outcome. There are, however, issues with these analyses including biased estimate of the probabilities and a decreased power due to multicollinearity (since they share some predictors). These issues create a necessity to examine both variables as simultaneous outcomes, by assessing the marginal probabilities for each outcome (i.e. using a proportional odds model) and the association between the two outcomes (i.e. using a constant global odds model). In this work we extend this model using a parsimonious option when the constraints imposed by assumptions of proportional marginal odds and constant global odds do not hold. We compare approaches by using simulations and by analyzing data on brain infarcts in older adults. Age at death is a marginal predictor of gross infarcts and also a marginal predictor of microscopic infarcts, but does not modify the association between gross and microscopic infarcts.</p>","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41111952","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 marginal structural model for estimation of the effect of HIV positivity awareness on risky sexual behavior","authors":"H. Twabi, Samuel O. M. Manda, D. Small, H. Kohler","doi":"10.1080/24709360.2023.2171537","DOIUrl":"https://doi.org/10.1080/24709360.2023.2171537","url":null,"abstract":"In this paper, a Marginal Structural Model (MSM) with inverse probability of treatment weights was used to estimate the causal effect of HIV positivity awareness on condom use and multiple sexual partners using data from the Malawi Longitudinal Study of Families and Health (MLSFH). Cumulative awareness of HIV positivity was measured as the number of times an individual was aware of their positive HIV status. Awareness of HIV positivity was associated with increased condom use (OR=2.22, 95%: (1.79, 2.75)). Only among women was it associated with multiple sexual partners (OR=1.76, 95%: (1.36, 2.28)). The use of MSM (over standard regression models for repeated measures) should be encouraged as it is more suited for assessing the cumulative treatment effects while controlling for time-varying confounders in longitudinal studies. There is a need to up-scale interventions that promote HIV testing, awareness of HIV status, and prevention of HIV transmission.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"185 - 202"},"PeriodicalIF":0.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49406406","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":"Flexible and robust procedure for subgroup inference","authors":"Ao Yuan, Anqi Yin, M. Tan","doi":"10.1080/24709360.2022.2127650","DOIUrl":"https://doi.org/10.1080/24709360.2022.2127650","url":null,"abstract":"In subgroup analysis of clinical trials and precision medicine, it is important to assess the causal effect of a new treatment against an existing one and classify the new treatment favorable subgroup if it exists. As the original randomization does not apply to comparisons between subgroups, for unbiased estimate the causal inference method will be used, in particular the doubly robust procedure, in which a propensity score model and a regression model need to be specified. As long as one of the models is correctly specified, the causal effect will be estimated unbiased. However, it is known that any subjectively specified model more or less deviates from the true one, and so the doubly robust procedure may still not be robust. To overcome this issue, we apply a recently proposed method to allow the identification of subgroups and causal inference in subgroups. The model is a semiparametric robust and flexible procedure, in which both the propensity score model and the regression model are semiparametric, with monotone constraint on the nonparametric parts. Simulation studies are conducted to evaluate the performance of the proposed method and compare some existing methods. Then the method is applied to analyze a real clinical trial data.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"314 - 328"},"PeriodicalIF":0.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43377669","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}