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  • 1. Rubin, Donald B.
    et al.
    Wang, Xiaoqin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Yin, Li
    Zell, Elizabth
    Bayesian casual inference: Appraches to Estimating the Effect of Treating Hospital Type on Cancer Survival in Sweden Using Principal Stratification2010In: The Oxford handbook of applied bayesian analysis, Oxford University Press , 2010, p. 679-708Chapter in book (Refereed)
  • 2.
    Wang, Xiaoqin
    University of Gävle, Department of Mathematics, Natural and Computer Sciences, Ämnesavdelningen för matematik och statistik.
    Analyticity theorems for parameter-dependent currents1991In: Mathematica Scandinavica, ISSN 0025-5521, E-ISSN 1903-1807, Vol. 69, no 2, p. 179-198Article in journal (Refereed)
    Abstract [en]

    Plurisubharmonic functions of two groups of complex variables (x1,...,x(n)) and (a1,...,a(m)) are considered; their partial functions are defined by f(a)(x) = f(x, a). We discuss analyticity theorems for the level sets associated to Lelong numbers of the parameter-dependent currents dd(c)f(a).

  • 3.
    Wang, Xiaoqin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    On Partial Lelong Numbers1997Report (Other academic)
  • 4.
    Wang, Xiaoqin
    University of Gävle, Department of Mathematics, Natural and Computer Sciences, Ämnesavdelningen för matematik och statistik.
    The finite part of singular integrals in several complex variables1993In: Transactions of the American Mathematical Society, ISSN 0002-9947, E-ISSN 1088-6850, Vol. 337, no 2, p. 771-793Article in journal (Refereed)
    Abstract [en]

    A divergent integral can sometimes be handled by assigning to it as its value the finite part in the sense of Hadamard. This is done by expanding the integral over the complement of a symmetric neighborhood of a singularity in powers of the radius, and throwing away the negative powers. In this paper the finite part of a singular integral of Cauchy type is defined, and this is then used to describe the boundary behavior of derivatives of a Cauchy-type integral. The finite part of a singular integral of Bochner-Martinelli type is studied, and an extension of the Plemelj jump formulas is shown to hold.

  • 5.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Jin, Yin
    Department of Sports Medicine, Chengdu Sport University, Chengdu, China.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
    Measuring and estimating treatment effect on dichotomous outcome of a population2016In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 25, no 5, p. 1779-1790Article in journal (Refereed)
    Abstract [en]

    In different studies for treatment effect on dichotomous outcome of a certain population, one uses different regression models, leading to different measures of the treatment effect. In observational studies, the common measures of the treatment effect are the conditional risk ratio based on a log-linear model and the conditional odds ratio based on a logistic model; in randomized trials, the common measures are the marginal risk difference based on a linear model, the marginal risk ratio based on a log-linear model, and the marginal odds ratio based on a logistic model. In this paper we express these measures in terms of the risk of a dichotomous outcome conditional on covariates and treatment, where the risk is described by a regression model. Therefore these measures do not explicitly depend on the regression model. As a result, we are able to use one regression model in one study to estimate all these measures by their maximum likelihood estimates. We show that these measures have causal interpretations and reflect different aspects of the same underlying treatment effect under the assumption of no unmeasured confounding covariate given observed covariates. We construct approximate distributions of the maximum likelihood estimates of these measures and then by using the approximate distributions we get confidence intervals for these measures. As an illustration, we estimate these measures for the effect of a triple therapy on eradication of Helicobacter pylori among Vietnamese children and are able to compare the treatment effect in this study with those in other studies.

  • 6.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Ye, Weimin
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
    Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies2017In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 44, no 14, p. 2483-2498Article in journal (Refereed)
    Abstract [en]

    In observational studies for the interaction between exposures on a dichotomous outcome of a certain population, usually one parameter of a regression model is used to describe the interaction, leading to one measure of the interaction. In this article we use the conditional risk of an outcome given exposures and covariates to describe the interaction and obtain five different measures of the interaction, that is, difference between the marginal risk differences, ratio of the marginal risk ratios, ratio of the marginal odds ratios, ratio of the conditional risk ratios, and ratio of the conditional odds ratios. These measures reflect different aspects of the interaction. By using only one regression model for the conditional risk, we obtain the maximum-likelihood (ML)-based point and interval estimates of these measures, which are most efficient due to the nature of ML. We use the ML estimates of the model parameters to obtain the ML estimates of these measures. We use the approximate normal distribution of the ML estimates of the model parameters to obtain approximate non-normal distributions of the ML estimates of these measures and then confidence intervals of these measures. The method can be easily implemented and is presented via a medical example.

  • 7.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Yin, Jin
    Department of Sport Medicine, Chengdu Sport University, Chengdu, China.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
    Point and interval estimations of marginal risk difference by logistic model2015In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 17, p. 3703-3722Article in journal (Refereed)
    Abstract [en]

    We use logistic model to get point and interval estimates of the marginal risk difference in observational studies and randomized trials with dichotomous outcome. We prove that the maximum likelihood estimate of the marginal risk difference is unbiased for finite sample and highly robust to the effects of dispersing covariates. We use approximate normal distribution of the maximum likelihood estimates of the logistic model parameters to get approximate distribution of the maximum likelihood estimate of the marginal risk difference and then the interval estimate of the marginal risk difference. We illustrate application of the method by a real medical example. 

  • 8.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute.
    Identification of Confounding versus Dispersing Covariates by Confounding Influence2013In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 42, no 24, p. 4540-4556Article in journal (Refereed)
  • 9.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
    Identifying and estimating net effects of treatments in sequential casual inference2015In: Electronic Journal of Statistics, ISSN 1935-7524, E-ISSN 1935-7524, Vol. 9, p. 1608-1643Article in journal (Refereed)
    Abstract [en]

    Suppose that a sequence of treatments are assigned to influence an outcome of interest that occurs after the last treatment. Between treatments, there are time-dependent covariates that may be post-treatment variables of the earlier treatments and confounders of the subsequent treatments. In this article, we study identification and estimation of the net effect of each treatment in the treatment sequence. We construct a point parametrization for the joint distribution of treatments, time-dependent covariates and the outcome, in which the point parameters of interest are the point effects of treatments considered as single-point treatments. We identify net effects of treatments by their expressions in terms of point effects of treatments and express patterns of net effects of treatments by constraints on point effects of treatments. We estimate net effects of treatments through their point effects under the constraint by maximum likelihood and reduce the number of point parameters in the estimation by the treatment assignment condition. As a result, we obtain an unbiased consistent maximum-likelihood estimate for the net effect of treatment even in a long treatment sequence. We also show by simulation that the interval estimation of the net effect of treatment achieves the nominal coverage probability.

  • 10.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
    Inference  of Net Effects of Treatments via Pseudo net effects of Treatments in Sequence Causal Inference2014In: Statistica sinica, ISSN 1017-0405, E-ISSN 1996-8507Article in journal (Refereed)
  • 11.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science, Mathematics.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    New g-formula for the sequential causal effect and blip effect of treatment in sequential causal inference2019In: Annals of Statistics, ISSN 0090-5364, E-ISSN 2168-8966Article in journal (Refereed)
    Abstract [en]

    In sequential causal inference, two types of causal effects are of practical interest, namely, the causal effect of the treatment regime (called the sequential causal effect) and the blip effect of treatmenton on the potential outcome after the last treatment. The well-known G-formula expresses these causal effects in terms of the standard paramaters. In this article, we obtain a new G-formula that expresses these causal effects in terms of the point observable effects of treatments similar to treatment in the framework of single-point causal inference. Based on the new G-formula, we estimate these causal effects by maximum likelihood via point observable effects with methods extended from single-point causal inference. We are able to increase precision of the estimation without introducing biases by an unsaturated model imposing constraints on the point observable effects. We are also able to reduce the number of point observable effects in the estimation by treatment assignment conditions.

  • 12.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
    Point and interval estimation of baseline risk and treatment effect based on logistic model for observational studies2015In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 57, no 3, p. 441-452Article in journal (Refereed)
    Abstract [en]

    In observational studies with dichotomous outcome of a population, researchers usually report treatment effect alone, although both baseline risk and treatment effect are needed to evaluate the significance of the treatment effect to the population. In this article, we study point and interval estimates including confidence region of baseline risk and treatment effect based on logistic model, where baseline risk is the risk of outcome of the population under control treatment while treatment effect is measured by the risk difference between outcomes of the population under active versus control treatments. Using approximate normal distribution of the maximum-likelihood (ML) estimate of the model parameters, we obtain an approximate joint distribution of the ML estimate of the baseline risk and the treatment effect. Using the approximate joint distribution, we obtain point estimate and confidence region of the baseline risk and the treatment effect as well as point estimate and confidence interval of the treatment effect when the ML estimate of the baseline risk falls into specified range. These interval estimates reflect nonnormality of the joint distribution of the ML estimate of the baseline risk and the treatment effect. The method can be easily implemented by using any software that generates normal distribution. The method can also be used to obtain point and interval estimates of baseline risk and any other measure of treatment effect such as risk ratio and the number needed to treat. The method can also be extended from logistic model to other models such as log-linear model.

  • 13. Yin, Li
    et al.
    Sundberg, Rolf
    Wang, Xiaoqin
    University of Gävle, Department of Mathematics, Natural and Computer Sciences, Ämnesavdelningen för matematik och statistik.
    Rubin, Donald
    Control of confounding through secondary samples2006In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 25, no 22, p. 3814-3825Article in journal (Refereed)
    Abstract [en]

    The control of confounding is essential in many statistical problems, especially in those that attempt to estimate exposure effects. In some cases, in addition to the 'primary' sample, there is another 'secondary' sample which, though having no direct information about the exposure effect, contains information about the confounding factors. The purpose of this article is to study the influence of the secondary sample on likelihood inference for the exposure effect. In particular, we investigate the interplay between the efficiency improvement and the possible bias introduced by the secondary sample as a function of the degree of confounding in the primary sample and the sizes of the primary and secondary samples. In the case of weak confounding, the secondary sample can only little improve estimation of the exposure effect, whereas with strong confounding the secondary sample can be much more useful. On the other hand, it will be more important to consider possible biasing effects in the latter case. For illustration, we use a formal example of a generalized linear model and a real example with sparse data from a case-control study of the association between gastric cancer and HM-CAP/Band 120. Copyright (c) 2006 John Wiley & Sons, Ltd.

  • 14.
    Yin, Li
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
    Wang, Xiaoqin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Estimating confidence regions of common measures of the baseline and treatment effect on dichotomous outcome of a population2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 4, p. 3034-3049Article in journal (Refereed)
    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.

  • 15.
    Yin, Li
    et al.
    Karolinska Institute, Department of Epidemiology and Biostatistics.
    Wang, Xiaoqin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Measuring and estimating treatment effect on count outcome in randomized trial and observational studies2015In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 5, p. 1080-1095Article in journal (Refereed)
    Abstract [en]

    When estimating treatment effect on count outcome of given population, one uses different models in different studies, resulting in non-comparable measures of treatment effect. Here we show that the marginal rate differences in these studies are comparable measures of treatment effect. We estimate the marginal rate differences by log-linear models and show that their finite-sample maximum-likelihood estimates are unbiased and highly robust with respect to effects of dispersing covariates on outcome. We get approximate finite-sample distributions of these estimates by using the asymptotic normal distribution of estimates of the log-linear model parameters. This method can be easily applied to practice.

  • 16.
    Yin, Li
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
    Wang, Xiaoqin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Parametric Inference of Sequential Causal Effects via Point Effects of Treatments2014In: Electronic Journal of Statistics, ISSN 1935-7524, E-ISSN 1935-7524Article in journal (Refereed)
  • 17.
    Yin, Li
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Wang, Xiaoqin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    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 outcome2017In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 9, p. 7138-7153Article in journal (Refereed)
    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.

  • 18.
    Yin, Li
    et al.
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
    Ye, Weimin
    Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
    Wang, Xiaoqin
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
    Point and interval estimation of exposure effects and interaction between exposures based on logistic model for observational studies2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141Article in journal (Refereed)
1 - 18 of 18
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