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  • 1.
    Wang, Xiaoqin
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science, Mathematics.
    Blom, Johannes
    Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
    Ye, Weimin
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.
    Yin, Li
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.
    Estimating and testing the influence of early diagnosis on cancer survival via point effects of diagnoses and treatments2022In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 31, no 8, p. 1538-1548Article in journal (Refereed)
    Abstract [en]

    A cancer diagnosis is part of a complex stochastic process, which involves patient's characteristics, diagnosing methods, an initial assessment of cancer progression, treatments and a certain outcome of interest. To evaluate the performance of diagnoses, one needs not only a consistent estimation of the causal effect under a specified regime of diagnoses and treatments but also reliable confidence interval, P-value and hypothesis testing of the causal effect. In this article, we identify causal effects under various regimes of diagnoses and treatments by the point effects of diagnoses and treatments and thus are able to estimate and test these causal effects by estimating and testing point effects in the familiar framework of single-point causal inference. Specifically, using data from a Swedish prognosis study of stomach cancer, we estimate and test the causal effects on cancer survival under various regimes of diagnosing and treating hospitals including the optimal regime. We also estimate and test the modification of the causal effect by age. With its simple setting, one can readily extend the example to a large variety of settings in the area of cancer diagnosis: different personal characteristics such as family history, different diagnosing procedures such as multistage screening, and different cancer outcomes such as cancer progression.

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  • 2.
    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.

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