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Wang, Xiaoqin, Docent
Publications (10 of 18) Show all publications
Wang, X. & Yin, L. (2019). New g-formula for the sequential causal effect and blip effect of treatment in sequential causal inference. Annals of Statistics
Open this publication in new window or tab >>New g-formula for the sequential causal effect and blip effect of treatment in sequential causal inference
2019 (English)In: Annals of Statistics, ISSN 0090-5364, E-ISSN 2168-8966Article in journal (Refereed) Accepted
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.

Keywords
blip effect, curse of dimensionality, new G-formula, null paradox, point observable effect, sequential causal effect
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-29358 (URN)
Available from: 2019-03-07 Created: 2019-03-07 Last updated: 2019-03-07Bibliographically approved
Yin, L. & Wang, X. (2017). Estimating confidence regions of common measures of the baseline and treatment effect on dichotomous outcome of a population. Communications in statistics. Simulation and computation, 46(4), 3034-3049
Open this publication in new window or tab >>Estimating confidence regions of common measures of the baseline and treatment effect on dichotomous outcome of a population
2017 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 4, p. 3034-3049Article in journal (Refereed) Published
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.

Keywords
Baseline measure, effect measure, confidence region, logistic model
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-20369 (URN)10.1080/03610918.2015.1073301 (DOI)000400186200035 ()2-s2.0-85006269459 (Scopus ID)
Available from: 2015-10-02 Created: 2015-10-02 Last updated: 2018-03-13Bibliographically approved
Yin, L., Wang, X. & Ye, W. (2017). Maximum-likelihood estimation and presentation for the interaction between treatments in observational studies with a dichotomous outcome. Communications in statistics. Simulation and computation, 46(9), 7138-7153
Open this publication in new window or tab >>Maximum-likelihood estimation and presentation for the interaction between treatments in observational studies with a dichotomous outcome
2017 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 46, no 9, p. 7138-7153Article in journal (Refereed) Published
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.

Keywords
treatment effect; interaction between treatments; point estimate; interval estimate; logistic model
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-22744 (URN)10.1080/03610918.2016.1230213 (DOI)000418384300030 ()2-s2.0-85018852517 (Scopus ID)
Available from: 2016-11-11 Created: 2016-11-11 Last updated: 2018-03-13Bibliographically approved
Wang, X., Ye, W. & Yin, L. (2017). Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies. Journal of Applied Statistics, 44(14), 2483-2498
Open this publication in new window or tab >>Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies
2017 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 44, no 14, p. 2483-2498Article in journal (Refereed) Published
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.

Keywords
Interaction, interaction measure, point estimate, interval estimate, regression model
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-18760 (URN)10.1080/02664763.2016.1257587 (DOI)000410837000002 ()2-s2.0-84996537841 (Scopus ID)
Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2018-03-13Bibliographically approved
Wang, X., Jin, Y. & Yin, L. (2016). Measuring and estimating treatment effect on dichotomous outcome of a population. Statistical Methods in Medical Research, 25(5), 1779-1790
Open this publication in new window or tab >>Measuring and estimating treatment effect on dichotomous outcome of a population
2016 (English)In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 25, no 5, p. 1779-1790Article in journal (Refereed) Published
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.

Keywords
Collapsibility of treatment effect, Treatment effect measure, Maximum likelihood estimate, Regression model
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-14100 (URN)10.1177/0962280213502146 (DOI)000385555400003 ()24004484 (PubMedID)2-s2.0-84989904002 (Scopus ID)
Available from: 2013-04-10 Created: 2013-04-10 Last updated: 2018-12-03Bibliographically approved
Yin, L., Ye, W. & Wang, X. (2016). Point and interval estimation of exposure effects and interaction between exposures based on logistic model for observational studies. Communications in statistics. Simulation and computation
Open this publication in new window or tab >>Point and interval estimation of exposure effects and interaction between exposures based on logistic model for observational studies
2016 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141Article in journal (Refereed) Submitted
National Category
Natural Sciences
Identifiers
urn:nbn:se:hig:diva-18756 (URN)
Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2018-03-13Bibliographically approved
Wang, X. & Yin, L. (2015). Identifying and estimating net effects of treatments in sequential casual inference. Electronic Journal of Statistics, 9, 1608-1643
Open this publication in new window or tab >>Identifying and estimating net effects of treatments in sequential casual inference
2015 (English)In: Electronic Journal of Statistics, ISSN 1935-7524, E-ISSN 1935-7524, Vol. 9, p. 1608-1643Article in journal (Refereed) Published
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.

Keywords
Net effect of treatment, pattern of net effects of treatments, point effect of treatment, constraint on point effects of treatments, treatment assignment condition, sequential causal inference
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-18759 (URN)10.1214/15-EJS1046 (DOI)000366268800057 ()2-s2.0-84982672504 (Scopus ID)
External cooperation:
Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2018-03-13Bibliographically approved
Yin, L. & Wang, X. (2015). Measuring and estimating treatment effect on count outcome in randomized trial and observational studies. Communications in Statistics - Theory and Methods, 44(5), 1080-1095
Open this publication in new window or tab >>Measuring and estimating treatment effect on count outcome in randomized trial and observational studies
2015 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 5, p. 1080-1095Article in journal (Refereed) Published
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.

Keywords
Treatment effect measure, Marginal rate difference, Finite-sample estimate
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-14076 (URN)10.1080/03610926.2013.776686 (DOI)000351220400015 ()2-s2.0-84924976048 (Scopus ID)
Available from: 2013-04-10 Created: 2013-04-09 Last updated: 2018-03-13Bibliographically approved
Wang, X. & Yin, L. (2015). Point and interval estimation of baseline risk and treatment effect based on logistic model for observational studies. Biometrical Journal, 57(3), 441-452
Open this publication in new window or tab >>Point and interval estimation of baseline risk and treatment effect based on logistic model for observational studies
2015 (English)In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 57, no 3, p. 441-452Article in journal (Refereed) Published
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.

Keywords
Confounding covariate, Point estimate, Interval estimate, Marginal risk difference
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-14102 (URN)10.1002/bimj.201400019 (DOI)000355610400006 ()25810034 (PubMedID)2-s2.0-84928011260 (Scopus ID)
Available from: 2013-04-10 Created: 2013-04-10 Last updated: 2018-03-13Bibliographically approved
Wang, X., Yin, J. & Yin, L. (2015). Point and interval estimations of marginal risk difference by logistic model. Communications in Statistics - Theory and Methods, 44(17), 3703-3722
Open this publication in new window or tab >>Point and interval estimations of marginal risk difference by logistic model
2015 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 17, p. 3703-3722Article in journal (Refereed) Published
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. 

Keywords
Confounding covariate; Interval estimate; Logistic model; Marginal risk difference; Point estimate
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hig:diva-14103 (URN)10.1080/03610926.2013.851229 (DOI)000361623400011 ()2-s2.0-84942041723 (Scopus ID)
Available from: 2013-04-10 Created: 2013-04-10 Last updated: 2018-03-13Bibliographically approved
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