Intensive Hemodialysis Associates with Improved Survival Compared with Conventional Hemodialysis
01 Apr 2012-Journal of The American Society of Nephrology (American Society of Nephrology)-Vol. 23, Iss: 4, pp 696-705
TL;DR: There is a strong association between intensive home hemodialysis and improved survival, but whether this relationship is causal remains unknown.
Abstract: Patients undergoing conventional maintenance hemodialysis typically receive three sessions per week, each lasting 2.5–5.5 hours. Recently, the use of more intensive hemodialysis (>5.5 hours, three to seven times per week) has increased, but the effects of these regimens on survival are uncertain. We conducted a retrospective cohort study to examine whether intensive hemodialysis associates with better survival than conventional hemodialysis. We identified 420 patients in the International Quotidian Dialysis Registry who received intensive home hemodialysis in France, the United States, and Canada between January 2000 and August 2010. We matched 338 of these patients to 1388 patients in the Dialysis Outcomes and Practice Patterns Study who received in-center conventional hemodialysis during the same time period by country, ESRD duration, and propensity score. The intensive hemodialysis group received a mean (SD) 4.8 (1.1) sessions per week with a mean treatment time of 7.4 (0.87) hours per session; the conventional group received three sessions per week with a mean treatment time of 3.9 (0.32) hours per session. During 3008 patient-years of follow-up, 45 (13%) of 338 patients receiving intensive hemodialysis died compared with 293 (21%) of 1388 patients receiving conventional hemodialysis (6.1 versus 10.5 deaths per 100 person-years; hazard ratio, 0.55 [95% confidence interval, 0.34–0.87]). The strength and direction of the observed association between intensive hemodialysis and improved survival were consistent across all prespecified subgroups and sensitivity analyses. In conclusion, there is a strong association between intensive home hemodialysis and improved survival, but whether this relationship is causal remains unknown.
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References
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TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Abstract: : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group. This paper discusses the central role of propensity scores and balancing scores in the analysis of observational studies. The propensity score is the (estimated) conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: matched sampling on the univariate propensity score which is equal percent bias reducing under more general conditions than required for discriminant matching, multivariate adjustment by subclassification on balancing scores where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and visual representation of multivariate adjustment by a two-dimensional plot. (Author)
23,744 citations
TL;DR: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.
Abstract: Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
15,454 citations
TL;DR: Methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero are described, thereby allowing one to determined the range of standardized differences that are plausible with the propensity score model having been correctly specified.
Abstract: The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
3,929 citations
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