{"title":"Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods.","authors":"Li-Chu Chien","doi":"10.1515/ijb-2022-0123","DOIUrl":"10.1515/ijb-2022-0123","url":null,"abstract":"<p><p>In genome-wide association studies (GWAS), logistic regression is one of the most popular analytics methods for binary traits. Multinomial regression is an extension of binary logistic regression that allows for multiple categories. However, many GWAS methods have been limited application to binary traits. These methods have improperly often been used to account for ordinal traits, which causes inappropriate type I error rates and poor statistical power. Owing to the lack of analysis methods, GWAS of ordinal traits has been known to be problematic and gaining attention. In this paper, we develop a general framework for identifying ordinal traits associated with genetic variants in pedigree-structured samples by collapsing and kernel methods. We use the local odds ratios GEE technology to account for complicated correlation structures between family members and ordered categorical traits. We use the retrospective idea to treat the genetic markers as random variables for calculating genetic correlations among markers. The proposed genetic association method can accommodate ordinal traits and allow for the covariate adjustment. We conduct simulation studies to compare the proposed tests with the existing models for analyzing the ordered categorical data under various configurations. We illustrate application of the proposed tests by simultaneously analyzing a family study and a cross-sectional study from the Genetic Analysis Workshop 19 (GAW19) data.</p>","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":"677-690"},"PeriodicalIF":1.2,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41177324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wentian Li, S. Cetin, A. Ulgen, M. Cetin, Hakan Şıvgın, Yaning Yang
{"title":"Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events","authors":"Wentian Li, S. Cetin, A. Ulgen, M. Cetin, Hakan Şıvgın, Yaning Yang","doi":"10.1101/2021.04.22.21255955","DOIUrl":"https://doi.org/10.1101/2021.04.22.21255955","url":null,"abstract":"Abstract COVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHR d and csHR r ). The eventual mortality/release outcome is also analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations: (1) The magnitude of OR is an upper limit of the csHR d : |log(OR)| ≥ |log(csHR d )|. This relationship between OR and HR might be understood from the definition of the two quantities; (2) csHR d and csHR r point in opposite directions: log(csHR d ) ⋅ log(csHR r ) < 0; This relation is a direct consequence of the nature of the two events; and (3) there is a tendency for a reciprocal relation between csHR d and csHR r : csHR d ∼ 1/csHR r . Though an approximate reciprocal trend between the two hazard ratios is in indication that the same factor causing faster death also lead to slow recovery by a similar mechanism, and vice versa, a quantitative relation between csHR d and csHR r in this context is not obvious. These results may help future analyses of data from COVID-19 or other similar diseases, in particular if the deceased patients are lacking, whereas surviving patients are abundant.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"0 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42193520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christian Palmes, Tobias Bluhmki, Benedikt Funke, E. Bluhmki
{"title":"Asymptotic properties of the two one-sided t-tests – new insights and the Schuirmann-constant","authors":"Christian Palmes, Tobias Bluhmki, Benedikt Funke, E. Bluhmki","doi":"10.1515/IJB-2020-0057","DOIUrl":"https://doi.org/10.1515/IJB-2020-0057","url":null,"abstract":"Abstract The two one-sided t-tests (TOST) method is the most popular statistical equivalence test with many areas of application, i.e., in the pharmaceutical industry. Proper sample size calculation is needed in order to show equivalence with a certain power. Here, the crucial problem of choosing a suitable mean-difference in TOST sample size calculations is addressed. As an alternative concept, it is assumed that the mean-difference follows an a-priori distribution. Special interest is given to the uniform and some centered triangle a-priori distributions. Using a newly developed asymptotical theory a helpful analogy principle is found: every a-priori distribution corresponds to a point mean-difference, which we call its Schuirmann-constant. This constant does not depend on the standard deviation and aims to support the investigator in finding a well-considered mean-difference for proper sample size calculations in complex data situations. In addition to the proposed concept, we demonstrate that well-known sample size approximation formulas in the literature are in fact biased and state their unbiased corrections as well. Moreover, an R package is provided for a right away application of our newly developed concepts.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"18 1","pages":"19 - 38"},"PeriodicalIF":1.2,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/IJB-2020-0057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46667419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimation of semi-Markov multi-state models: a comparison of the sojourn times and transition intensities approaches","authors":"A. Asanjarani, B. Liquet, Y. Nazarathy","doi":"10.1515/IJB-2020-0083","DOIUrl":"https://doi.org/10.1515/IJB-2020-0083","url":null,"abstract":"Abstract Semi-Markov models are widely used for survival analysis and reliability analysis. In general, there are two competing parameterizations and each entails its own interpretation and inference properties. On the one hand, a semi-Markov process can be defined based on the distribution of sojourn times, often via hazard rates, together with transition probabilities of an embedded Markov chain. On the other hand, intensity transition functions may be used, often referred to as the hazard rates of the semi-Markov process. We summarize and contrast these two parameterizations both from a probabilistic and an inference perspective, and we highlight relationships between the two approaches. In general, the intensity transition based approach allows the likelihood to be split into likelihoods of two-state models having fewer parameters, allowing efficient computation and usage of many survival analysis tools. Nevertheless, in certain cases the sojourn time based approach is natural and has been exploited extensively in applications. In contrasting the two approaches and contemporary relevant R packages used for inference, we use two real datasets highlighting the probabilistic and inference properties of each approach. This analysis is accompanied by an R vignette.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"18 1","pages":"243 - 262"},"PeriodicalIF":1.2,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/IJB-2020-0083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43491644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation","authors":"Waleed Almutiry, R. Deardon","doi":"10.1515/ijb-2017-0092","DOIUrl":"https://doi.org/10.1515/ijb-2017-0092","url":null,"abstract":"Abstract Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2019-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42487422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marta Tallarita, M. De Iorio, A. Guglielmi, J. Malone‐Lee
{"title":"Bayesian Autoregressive Frailty Models for Inference in Recurrent Events","authors":"Marta Tallarita, M. De Iorio, A. Guglielmi, J. Malone‐Lee","doi":"10.1515/ijb-2018-0088","DOIUrl":"https://doi.org/10.1515/ijb-2018-0088","url":null,"abstract":"Abstract We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the frailty parameters influencing the response at different times. The order of the autoregression may be assumed unknown and is an object of inference. We consider two alternative approaches to perform model selection under this scenario. Covariates may be easily included in the regression framework and censoring and missing data are easily accounted for. As the proposed methodologies lie within the class of Dirichlet process mixtures, posterior inference can be performed through efficient MCMC algorithms. We illustrate the approach through simulations and medical applications involving recurrent hospitalizations of cancer patients and successive urinary tract infections.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2019-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2018-0088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47218529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Chambaz, Alan Hubbard, Alexander R. Luedtke, M. J. Laan
{"title":"Biostatistics in Africa 2019: A Special Issue of The International Journal of Biostatistics","authors":"A. Chambaz, Alan Hubbard, Alexander R. Luedtke, M. J. Laan","doi":"10.1515/ijb-2019-0125","DOIUrl":"https://doi.org/10.1515/ijb-2019-0125","url":null,"abstract":"","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47478519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging","authors":"Mame Diarra Fall","doi":"10.1515/ijb-2017-0099","DOIUrl":"https://doi.org/10.1515/ijb-2017-0099","url":null,"abstract":"Abstract Biostatistic applications often require to collect and analyze a massive amount of data. Hence, it has become necessary to consider new statistical paradigms that perform well in characterizing complex data. Nonparametric Bayesian methods provide a widely used framework that offers the key advantages of a fully model-based probabilistic framework, while being highly flexible and adaptable. The goal of this paper is to provide a motivation of Bayesian nonparametrics (BNP) through a particular biomedical application, namely Positron Emission Tomography (PET) imaging reconstruction.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42462530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Valérie Garès, C. Dimeglio, G. Guernec, Romain Fantin, B. Lepage, M. Kosorok, N. Savy
{"title":"On the Use of Optimal Transportation Theory to Recode Variables and Application to Database Merging","authors":"Valérie Garès, C. Dimeglio, G. Guernec, Romain Fantin, B. Lepage, M. Kosorok, N. Savy","doi":"10.1515/ijb-2018-0106","DOIUrl":"https://doi.org/10.1515/ijb-2018-0106","url":null,"abstract":"Abstract Merging databases is a strategy of paramount interest especially in medical research. A common problem in this context comes from a variable which is not coded on the same scale in both databases we aim to merge. This paper considers the problem of finding a relevant way to recode the variable in order to merge these two databases. To address this issue, an algorithm, based on optimal transportation theory, is proposed. Optimal transportation theory gives us an application to map the measure associated with the variable in database A to the measure associated with the same variable in database B. To do so, a cost function has to be introduced and an allocation rule has to be defined. Such a function and such a rule is proposed involving the information contained in the covariates. In this paper, the method is compared to multiple imputation by chained equations and a statistical learning method and has demonstrated a better average accuracy in many situations. Applications on both simulated and real datasets show that the efficiency of the proposed merging algorithm depends on how the covariates are linked with the variable of interest.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2019-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2018-0106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42506480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}