Article
Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models.
Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
American Journal of Epidemiology
(Impact Factor: 4.98).
11/2003;
158(7):68794.
Source: PubMed

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 Cited In (128)

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ABSTRACT: Background: The parametric gformula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric gformula can be used to adjust for timevarying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied. Methods: We provide a simple introduction to the parametric gformula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to timevarying confounding. Results: Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the gformula. Conclusions: The gformula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric gformula that is sufficiently general to allow easy adaptation to many settings of public health relevance.Epidemiology (Cambridge, Mass.) 08/2014; 25(6). DOI:10.1097/EDE.0000000000000160 · 6.18 Impact Factor 
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ABSTRACT: Longitudinal observational data are required to assess the association between exposure to βinterferon medications and disease progression among relapsingremitting multiple sclerosis (MS) patients in the "realworld" clinical practice setting. Marginal structural Cox models (MSCMs) can provide distinct advantages over traditional approaches by allowing adjustment for timevarying confounders such as MS relapses, as well as baseline characteristics, through the use of inverse probability weighting. We assessed the suitability of MSCMs to analyze data from a large cohort of 1,697 relapsingremitting MS patients in British Columbia, Canada (19952008). In the context of this observational study, which spanned more than a decade and involved patients with a chronic yet fluctuating disease, the recently proposed "normalized stabilized" weights were found to be the most appropriate choice of weights. Using this model, no association between βinterferon exposure and the hazard of disability progression was found (hazard ratio = 1.36, 95% confidence interval: 0.95, 1.94). For sensitivity analyses, truncated normalized unstabilized weights were used in additional MSCMs and to construct inverse probability weightadjusted survival curves; the findings did not change. Additionally, qualitatively similar conclusions from approximation approaches to the weighted Cox model (i.e., MSCM) extend confidence in the findings.American Journal of Epidemiology 06/2014; 180(2). DOI:10.1093/aje/kwu125 · 4.98 Impact Factor 
Article: Regression methods in biostatistics. Linear, logistic, survival, and repeated measures models
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ABSTRACT: This book is, as the title indicates, about regression methods, with examples and terminology from the biostatistics field. It should, however, also be useful for practitioners from other disciplines where regression methods can be applied. A prerequisite for the book is a first course in statistics, including simple linear regression, oneway ANOVA, and the chisquare test for independence in contingency tables. After a motivating introductory example in Chapter 1, the authors continue in Chapter 2 with a presentation of descriptive and graphical methods for different kinds of variables, e.g., the histogram, the boxplot, and the qqplot, along with ordinary numerical summary statistics for numerical variables, tables for categorical variables, and scatterplots, correlation coefficients, and crosstabulation for the relation between two variables. In Chapter 3, “Basic Statistical Methods”, the above mentioned prerequisites are reviewed, i.e., the ttest, analysis of variance, correlation, simple linear regression, methods for binary outcomes, survival analysis, and bootstrap confidence intervals. In Chapter 4, multipredictor methods are introduced in the context of the basic linear regression model. This chapter also covers the general topics confounding, mediation, interaction, and model checking. Chapter 5 deals with predictor selection, Chapter 6 with binary outcomes and logistic regression, and Chapter 7 with survival analysis. In Chapter 8, dependence is introduced in the form of repeated measurements, and its various forms are discussed in sections about hierarcical data, longitudinal data, generalized estimating equations, and random effects models. Chapter 9 returns to independence and introduces the generalized linear models. Finally, Chapter 10 introduces briefly the concept of analysis of complex surveys. The book concludes with a summary in Chapter 11, where the authors try to give practical advises about which of the methods covered in this book should be used in a particular situation, and also under what circumstances none is suitable. Most chapters end with a Problems section, and a section of further notes and references, making the book suitable as a text for a course on regression methods for Ph. D. students in medicine, but also in other appplied fields, e.g., in the social sciences. Many of the analyses in the book are illustrated with output from the statistical package Stata.Reviewer: Göran Broström (Umea)
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