arXiv subjects

Xiaoqin Wang

Publications and source records attributed to Xiaoqin Wang.

23 records · Page 2Linked to original sources

arXiv2015

Parametric Sequential Causal Inference in Point Parametrization

Suppose that a sequence of treatments are assigned to influence an outcome of interest that occurs after the last treatment. Between treatments there exist time-dependent covariates that may be posttreatment variables of the earlier treatments and confounders of the subsequent treatments. In this article, we develop a parametric approach to inference of the causal effect of the treatment sequence on the outcome called the sequential causal effect. We construct a point parametrization for the conditional distribution of an outcome given all treatments and time-dependent covariates, in which the point parameters of interest are the point effects of treatments considered as single-point treatments. We (1) identify net effects of treatments by point effects of treatments, (2) express patterns of net effects of treatments by constraints on point effects of treatments, and (3) show that all sequential causal effects are determined by net effects of treatments. Accordingly we (1) estimate net effects of treatments through point effects of treatments by maximum likelihood, (2) improve the estimation by constraints on point effects of treatments and assignment conditions of treatments, and (3) use the estimates of net effects of treatments to obtain those of sequential causal effects. As a result, we obtain unbiased consistent maximum-likelihood estimates of sequential causal effects even for long treatment sequences. For illustration of our method, we study the causal effects of various sequences of recreational drugs on the CD4 count among HIV patients.

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arXiv2015

Measuring and estimating interaction between exposures on dichotomous outcome of a population

In observational studies for the interaction between exposures on dichotomous outcome of a population, one usually uses one parameter of a regression model to describe the interaction, leading to one measure of the interaction. In this article, we use the conditional risk of outcome given exposures and covariates to describe the interaction and obtain five different measures for the interaction in observational studies, i.e. 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. By using only one regression model for the conditional risk of outcome given exposures and covariates, we obtain the maximum-likelihood estimates of all these measures. By generating approximate distributions of the maximum-likelihood estimates of these measures, we obtain interval estimates of these measures. The method is presented by studying the interaction between a therapy and the environment on eradication of Helicobacter pylori among Vietnamese children.

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arXiv2015

Point and interval estimation of exposure effects and interaction between the exposures based on logistic model for observational studies

In observational studies with dichotomous outcome of a population, researchers need to present the effects of exposures and interaction between the exposures jointly in order to learn the relationship between the exposure effects and the interaction. In this article we study point and interval estimation of exposure effects and the interaction based on logistic model, where the exposure effects are measured by risk differences while the interaction is measured by difference between risk differences. Using approximate normal distribution of the maximum-likelihood (ML) estimate of the model parameters, we obtain approximate non-normal distribution of the ML estimate of the exposure effects and the interaction. Using the obtained distribution, we obtain point estimate and confidence region of (exposure effect, interaction) as well as point estimate and confidence interval of the interaction when the ML estimate of an exposure effect falls into specified range. Our maximum-likelihood-based approach provides a simple but reliable method of interval estimation of exposure effects and the interaction.

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arXiv2015

Estimating confidence regions of common measures of (baseline, treatment effect) on dichotomous outcome of a population

In this article we estimate confidence regions of the common measures of (baseline, treatment effect) in observational studies, where the measure of baseline is baseline risk or baseline odds while the measure of treatment effect is odds ratio, risk difference, risk ratio or attributable fraction, and where confounding is controlled in estimation of both baseline and treatment effect. To avoid high complexity of the normal approximation method and the parametric or non-parametric bootstrap method, we obtain confidence regions for measures of (baseline, treatment effect) by generating approximate distributions of the ML estimates of these measures based on one logistic model.

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arXiv2014

Estimating Net Effects of Treatments in Treatment Sequence without the Assumption of Strongly Ignorable Treatment Assignment

In sequential causal inference, one estimates the causal net effect of treatment in treatment sequence on an outcome after last treatment in the presence of time-dependent covariates between treatments, improves the estimation by the untestable assumption of strongly ignorable treatment assignment, and obtains consistent but non-genuine likelihood-based estimate. In this article, we introduce the net effect of treatment as parameter for the conditional distribution of outcome given all treatments and time-dependent covariates and show that it is equal to the causal net effect of treatment under the assumption of strongly ignorable treatment assignment. As a result, we can estimate the net effect of treatment and evaluate its causal interpretation in two separate steps. The first step is fucus of this article while the second step can be accomplished by usual sensitivity analyses. We construct point parametrization for the conditional outcome distribution in which the parameters of interest are the point effects of single-point treatments. With point parametrization and without the untestable assumption, we estimate the net effect of treatment by maximum likelihood, improve the estimation by testable pattern of the net effect of treatment, and obtain unbiased consistent maximum-likelihood estimate for the net effect of treatment with finite-dimensional pattern.

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