Biostatistics and Epidemiology最新文献_第2页

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

Modeling time taken to HIV testing and uptake of test results: application of extended PWP model HIV测试的建模时间和测试结果的吸收:扩展PWP模型的应用

Biostatistics and Epidemiology Pub Date : 2022-01-02 DOI: 10.1080/24709360.2021.2017637

M. Suranga, S. Samita

{"title":"Modeling time taken to HIV testing and uptake of test results: application of extended PWP model","authors":"M. Suranga, S. Samita","doi":"10.1080/24709360.2021.2017637","DOIUrl":"https://doi.org/10.1080/24709360.2021.2017637","url":null,"abstract":"Improving HIV testing among the most at risk populations (MARP) is one of the first steps to achieve the Sustainable Development Goal target of ending AIDS by 2030. Factors affecting time taken to HIV testing and subsequent clinic visits to uptake the test result are important inputs for development of HIV prevention programmes. This study aims to develop multivariate statistical models to describe HIV testing behavior of MARP. HIV testing data of 5667 Female Sex Workers registered with the National HIV Prevention Programme in 10 districts of Sri Lanka during 2016 and 2017 were modelled using univariate and multivariate survival analysis techniques. Results showed that the Prentice, Williams & Peterson gap time model (PWPGTM), and all univariate Cox Proportional Hazard Models together generated consistent results. However, higher number of effects of the factors and interaction effects were detected in the PWPGTM compared to other models. Further, PWPGTM generated more precise estimates with lower standard errors. In all the models, most of the factors were identified as time dependent covariates. Study concludes that the extended PWPGTM is the more appropriate technique to model time taken to HIV testing and subsequent clinic visit to uptake of test results among MARP.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"97 - 112"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48753315","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

Estimation of highly heterogeneous multinomial probabilities observed at the beginning of COVID-19 新冠肺炎开始时观察到的高度异质多项式概率的估计

Biostatistics and Epidemiology Pub Date : 2022-01-02 DOI: 10.1080/24709360.2022.2064693

T. Ogura, T. Yanagimoto

引用次数: 0

A comparison of single imputation and multiple imputation methods for missing data in different oncogene expression profiles 不同癌基因表达谱中缺失数据的单一插补和多重插补方法的比较

Biostatistics and Epidemiology Pub Date : 2022-01-02 DOI: 10.1080/24709360.2021.2023805

W. Ye, Ling Zhang, Wenqing Zhang, Xiaojiao Wu, Dong Yi, Yazhou Wu

{"title":"A comparison of single imputation and multiple imputation methods for missing data in different oncogene expression profiles","authors":"W. Ye, Ling Zhang, Wenqing Zhang, Xiaojiao Wu, Dong Yi, Yazhou Wu","doi":"10.1080/24709360.2021.2023805","DOIUrl":"https://doi.org/10.1080/24709360.2021.2023805","url":null,"abstract":"To evaluate the effects of multiple-imputation (MI) method for missing data in gene expression profiles with different datasets and percentages of missing values compared with 3 single-imputation (SI) methods. Based on 3 gene expression profiles datasets from human colon cancer, non-small cell lung cancer, and lymph cancer, different deletion rates and different imputation numbers of MI were compared. The imputation and clustering effects of different methods were evaluated using the NRMSE and the gene clustering accuracy (F value). The NRMSE of the 4 methods gradually increased as the percentage of missing values in the 3 datasets increased, whereas the F value gradually decreased. In all datasets with different percentage of missing values settings, the NRMSEs of MI was consistently lower than those of the 3 SI methods, whereas the F value of MI was highest. The NRMSEs of MI gradually decreased as the number of imputations increased and increased as the variability in the original datasets increased, and the datasets imputed by MI showed the best clustering results. The results showed that the application of MI develops and enriches imputation-model approaches and provides a solid foundation for subsequent establishment of imputation strategies for gene expression profiles with missing data.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"113 - 127"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46745736","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

Bayesian inference of the basic reproduction number for a SIR epidemic model SIR流行病模型基本繁殖数的贝叶斯推断

Biostatistics and Epidemiology Pub Date : 2022-01-02 DOI: 10.1080/24709360.2022.2031721

Abdelaziz Qaffou, Hamid El Maroufy, Mokhtar Zbair

{"title":"Bayesian inference of the basic reproduction number for a SIR epidemic model","authors":"Abdelaziz Qaffou, Hamid El Maroufy, Mokhtar Zbair","doi":"10.1080/24709360.2022.2031721","DOIUrl":"https://doi.org/10.1080/24709360.2022.2031721","url":null,"abstract":"This paper is concerned with the Bayesian estimation for the basic reproduction number , defined as the expected number of new infectious from one infected individual in a fully susceptible population through the entire duration of the infectious period. This parameter is of great importance within epidemic modeling because no epidemic can occur if and an epidemic occurs if . Estimation of , or equivalent parameters in more complex models, can usually be achieved via Markov chain Monte Carlo (MCMC) methods. We will adopt the Bayesian method proposed by Eraker [MCMC analysis of diffusion models with application to finance. J Bus Econ Statist. 2001;19(2):177–191] in the context of financial models. The method consists of augmenting the low-frequency observations by the insertion of a finite number of latent data between two consecutive observations. We develop MCMC methods for inference to explore a posterior distribution of and of missing data. We illustrate the performance of the estimators on both synthetic data and real epidemic from the SIR (Susceptible-Infective-Removed) epidemic model and compare the results with the maximum likelihood (ML) method.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"128 - 143"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44710149","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

Abstract PO-166: Geographic distribution of the cervical cancer incidence in the northeast region of the state of São Paulo, Brazil 摘要PO-166:巴西<s:1>圣保罗州东北部地区宫颈癌发病率的地理分布

Biostatistics and Epidemiology Pub Date : 2022-01-01 DOI: 10.1158/1538-7755.disp21-po-166

A. Ribeiro, A. M. Costa, J. Fregnani

引用次数: 0

Abstract PO-165: Prevalence of metabolic syndrome among cancer survivors: An NHANES study PO-165:癌症幸存者中代谢综合征的患病率:一项NHANES研究

Biostatistics and Epidemiology Pub Date : 2022-01-01 DOI: 10.1158/1538-7755.disp21-po-165

A. Ezeani, T. Agurs-Collins

引用次数: 0