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  • 1.
    Yin, Li
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Wang, Xiaoqin
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för elektroteknik, matematik och naturvetenskap, Matematik.
    Estimating and testing sequential causal effects based on alternative G-formula: an observational study of the influence of early diagnosis on survival of cardia cancer2024Inngår i: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 53, nr 4, s. 1917-1931Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cancer diagnosis is part of a complex stochastic process, in which patients' personal and social characteristics influence the choice of diagnosing methods, diagnosing methods in turn influence the initial assessment of cancer stage, cancer stage in turn influences the choice of treating methods, and treating methods in turn influence cancer outcomes such as cancer survival. To evaluate the performance of diagnoses, one needs to estimate and test the sequential causal effect (SCE) under a specified regime of diagnoses and treatments in such a complex observational study, where the data-generating mechanism is unknown and modeling is needed for statistical inference. In this article, we introduce a method of statistical modeling to estimate and test SCEs under regimes of treatments (diagnoses and treatments in cancer diagnosis) in complex observational studies. By applying the alternative G-formula, we express the SCE in terms of the point effects of treatments in the sequence, so that the modeling can be conducted via the point effects in the framework of single-point causal inference. We illustrate our method by a medical example of cancer diagnosis with data from a Swedish prognosis study of cardia cancer.

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  • 2.
    Yin, Li
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
    Wang, Xiaoqin
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för elektronik, matematik och naturvetenskap, Matematik.
    Estimating confidence regions of common measures of the baseline and treatment effect on dichotomous outcome of a population2017Inngår i: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, nr 4, s. 3034-3049Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this article we estimate confidence regions of the common measures of (baseline, treatment effect) in observational studies, where the measure of a baseline is baseline risk or baseline odds while the measure of a treatment effect is odds ratio, risk difference, risk ratio or attributable fraction, and where confounding is controlled in estimation of both the baseline and treatment effect. We use only one logistic model to generate approximate distributions of the maximum-likelihood estimates of these measures and thus obtain the maximum-likelihood-based confidence regions for these measures. The method is presented via a real medical example.

  • 3.
    Yin, Li
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Wang, Xiaoqin
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för elektronik, matematik och naturvetenskap, Matematik.
    Ye, Weimin
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Maximum-likelihood estimation and presentation for the interaction between treatments in observational studies with a dichotomous outcome2017Inngår i: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, nr 9, s. 7138-7153Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In observational studies for the interaction between treatments, one needs to estimate and present both the treatment effects and the interaction to learn the significance of the interaction to the treatment effects. In this article, we estimate the treatment effects and the interaction jointly by using only one logistic model and based on maximum-likelihood. We present the interaction by (1) point estimate and confidence interval of the interaction, (2) point estimate and confidence region of (treatment effect, interaction) and (3) point estimate and confidence interval of the interaction when the maximum-likelihood estimate of one treatment effect falls into specified range.

  • 4.
    Zhang, Yongjin
    et al.
    College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China; School of Mathematics and Physics, Anhui University of Technology, Maanshan, China.
    Sun, Youchao
    College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
    Zhao, Ming
    Högskolan i Gävle, Akademin för teknik och miljö, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, Industriell ekonomi. Högskolan i Gävle, Centrum för logistik och innovativ produktion.
    A combinatorial estimation approach for storage reliability with initial failures based on periodic testing data2017Inngår i: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, nr 4, s. 3319-3340Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. Unlike the operational reliability, storage reliability for certain types of products may not be always 100% at the beginning of storage since there are existing possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new combinatorial approach, the non-parametric measure for the estimates of the number of failed products and the current reliability at each testing time in storage, and the parametric measure for the estimates of the initial reliability and the failure rate based on the exponential reliability function, is proposed for estimating and predicting the storage reliability with possible initial failures. The proposed method has taken into consideration that, the initial failure and the reliability testing data before and during the storage process, are available for providing more accurate estimates of both initial failure probability and the probability of storage failures. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is provided in this paper. Finally, numerical examples are given to illustrate the method. Furthermore, a detailed comparison between the proposed method and the traditional method, for examining the rationality of assessment and prediction on the storage reliability, is presented. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality.

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