Biostatistics and Epidemiology最新文献

Adjusting for bias due to measurement error in functional quantile regression models with error-prone functional and scalar covariates. 在容易出错的函数协变量和标量协变量的功能分位数回归模型中调整由于测量误差引起的偏差。

Biostatistics and Epidemiology Pub Date : 2024-01-01 Epub Date: 2024-10-02 DOI: 10.1080/24709360.2024.2405439

Xiwei Chen, Heyang Ji, Yuanyuan Luan, Roger S Zoh, Lan Xue, Sneha Jadhav, Carmen D Tekwe

{"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":"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.","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}

引用次数: 0

The analysis of Salmonella’s ability to survive in different external environments 沙门氏菌在不同外部环境下的生存能力分析

Biostatistics and Epidemiology Pub Date : 2023-01-02 DOI: 10.1080/24709360.2023.2265277

Wesam R. Kadhum, Lyudmila Sviridova, Dmitry Snegirev

{"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}

引用次数: 0

Notice of duplicate publication: public transportation network scan for rapid surveillance 复发通知:公交网络扫描快速监控

Biostatistics and Epidemiology Pub Date : 2023-01-02 DOI: 10.1080/24709360.2023.2275481

引用次数: 0

Global Odds Model with Proportional Odds and Trend Odds Applied to Gross and Microscopic Brain Infarcts. 具有比例赔率和趋势赔率的全局赔率模型应用于大体和微观脑梗塞。

Biostatistics and Epidemiology Pub Date : 2023-01-01 Epub Date: 2018-07-26 DOI: 10.1080/24709360.2018.1500089

Ana W Capuano, Robert Wilson, Julie A Schneider, Sue E Leurgans, David A Bennett

{"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":"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.","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}

引用次数: 0

Flexible and robust procedure for subgroup inference 子群推理的灵活鲁棒方法

Biostatistics and Epidemiology Pub Date : 2022-07-03 DOI: 10.1080/24709360.2022.2127650

Ao Yuan, Anqi Yin, M. Tan

{"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}

引用次数: 1

A marginal structural model for estimation of the effect of HIV positivity awareness on risky sexual behavior 估计HIV阳性意识对危险性行为影响的边际结构模型

Biostatistics and Epidemiology Pub Date : 2022-07-03 DOI: 10.1080/24709360.2023.2171537

H. Twabi, Samuel O. M. Manda, D. Small, H. Kohler

引用次数: 0

The “exposure-based cross-sectional” study design: a novel observational study design applicable to rare exposures “基于暴露的横断面”研究设计：一种适用于罕见暴露的新型观察性研究设计

Biostatistics and Epidemiology Pub Date : 2022-07-03 DOI: 10.1080/24709360.2022.2095244

J. Poorolajal

{"title":"The “exposure-based cross-sectional” study design: a novel observational study design applicable to rare exposures","authors":"J. Poorolajal","doi":"10.1080/24709360.2022.2095244","DOIUrl":"https://doi.org/10.1080/24709360.2022.2095244","url":null,"abstract":"Current epidemiological studies are either inefficient or very expensive and time-consuming when the exposure of interest is very rare. The ‘exposure-based cross-sectional’ study is a new design that can overcome this problem. The ‘exposure-based cross-sectional’ study starts with exposed and unexposed groups. Then, these two groups are compared to determine what proportion of each group have the disease and what proportion do not. It is as if we were conducting a reversed case–control study in which the positions of the disease and exposures are altered. Dissimilar to retrospective cohort studies, the ‘exposure-based cross-sectional’ study does not depend on the basic existing records. This study measures the disease ‘prevalence’ rather than the disease ‘incidence’. The ‘exposure-based cross-sectional’ study design was examined in several real-life epidemiological studies with binary and continuous outcomes. The ‘exposure-based cross-sectional’ study is an efficient, inexpensive, expeditious, and easy to conduct study design for rare exposures. It can be performed for both binary and continuous outcomes.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"285 - 291"},"PeriodicalIF":0.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47766890","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}

引用次数: 0

Propensity score-based adjustment for covariate effects on classification accuracy of bio-marker using ROC curve 利用ROC曲线对生物标志物分类准确性的协变量影响进行基于倾向性评分的调整

Biostatistics and Epidemiology Pub Date : 2022-07-03 DOI: 10.1080/24709360.2022.2131994

Muntaha Mushfiquee, M. S. Rahman

{"title":"Propensity score-based adjustment for covariate effects on classification accuracy of bio-marker using ROC curve","authors":"Muntaha Mushfiquee, M. S. Rahman","doi":"10.1080/24709360.2022.2131994","DOIUrl":"https://doi.org/10.1080/24709360.2022.2131994","url":null,"abstract":"The potential performance of bio-marker in classifying diseased from healthy population may be affected by baseline covariates (X) that are associated with both the bio-marker (Y) and the disease status (D). Some existing approaches can be able to adjust for the effect of a single covariate at a time. However, several potential covariates can be available in practice for which simultaneous adjustment in the ROC curve is essential. This study proposed a propensity score (PS) based adjustment for the effects of several covariates in the ROC curve. The PS is first derived from a linear transformation of several covariates and the PS-adjusted (and PS-specific) ROC curve was then estimated using the existing non-parametric induced ROC regression framework. The method is illustrated for both continuous and binary bio-markers. The simulation study suggests that the PS-based adjustment performed well by providing a consistent estimate of the true ROC curve and showing robustness to the mis-specification of the propensity score model as well as to a non-linear function of covariates. Further, an application of the method is provided to evaluate the effectiveness of the body-mass-index in classifying patients with hypertension or diabetes after adjusting for the potential covariates such as age, sex, education, socio-economic status.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"292 - 313"},"PeriodicalIF":0.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49257477","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}

引用次数: 0

Doubly weighted estimating equations and weighted multiple imputation for causal inference with an incomplete subgroup variable 具有不完全子群变量的因果推理的双加权估计方程和加权多重插补

Biostatistics and Epidemiology Pub Date : 2022-05-16 DOI: 10.1080/24709360.2022.2069457

M. Cuerden, L. Diao, C. Cotton, R. Cook

{"title":"Doubly weighted estimating equations and weighted multiple imputation for causal inference with an incomplete subgroup variable","authors":"M. Cuerden, L. Diao, C. Cotton, R. Cook","doi":"10.1080/24709360.2022.2069457","DOIUrl":"https://doi.org/10.1080/24709360.2022.2069457","url":null,"abstract":"Health research often aims to investigate whether the effect of an exposure variable is common across different subgroups of individuals, but sometimes the variable defining subgroups is not recorded in all individuals. We propose and evaluate two methods for estimation of the marginal causal effect of an exposure variable within subgroups in the observational setting where the subgroup variable is incompletely observed. The first approach involves doubly weighted estimating functions with one weight based on a propensity score for exposure and a second weight addressing the selection bias when analyses are restricted to individuals with complete data. The second approach uses the inverse probability of exposure weights in conjunction with multiple imputation for the incomplete subgroup variable. The resulting estimators are consistent when the auxiliary models are correctly specified; we assess the finite sample performance via simulation. An illustrative analysis is provided involving patients with psoriatic arthritis treated with biologic therapy where interest lies in the effect of therapy according to the presence or absence of the human leukocyte antigen marker HLA-B27 which is incompletely observed.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"266 - 284"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42621156","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}

引用次数: 0

Robust quasi-oracle semiparametric estimation of average causal effects 平均因果效应的鲁棒准oracle半参数估计

Biostatistics and Epidemiology Pub Date : 2022-01-02 DOI: 10.1080/24709360.2022.2031808

Peng Wu, Xingwei Tong, Yi Wang, Jiajuan Liang, Xiao‐Hua Zhou

{"title":"Robust quasi-oracle semiparametric estimation of average causal effects","authors":"Peng Wu, Xingwei Tong, Yi Wang, Jiajuan Liang, Xiao‐Hua Zhou","doi":"10.1080/24709360.2022.2031808","DOIUrl":"https://doi.org/10.1080/24709360.2022.2031808","url":null,"abstract":"Causal effects estimation is one of the central problems in real clinical data analysis. Outcome regression and inverse probability weighting are two basic strategies to estimate causal effects in observational studies. The former suffers the problem of implicitly making extrapolation and the latter encounters the problem of volatility in the presence of extreme weights (some propensity score values are close to 0 or 1), which sometimes occurs in clinical data. In this work, we propose two asymptotically equivalent semiparametric estimators of average causal effects based on propensity score. The proposed approaches apply machine learning techniques to estimate propensity score and can circumvent the problem of model extrapolation. It is easy to implement and robust to extreme weights. The proposed estimators are shown to be consistent and asymptotically normal, and the asymptotic variances can also be estimated. In addition, the proposed estimators enjoy the property of quasi-oracle: the resulting estimators of average causal effects based on estimated propensity score are asymptotically indistinguishable from the estimators with true propensity score. Simulation studies and empirical applications further demonstrate the advantages of the proposed methods compared with competing ones.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"144 - 163"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41525238","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}

引用次数: 0