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      Sodium Intake from Various Time Frames and Incident Hypertension Among Chinese Adults :

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          Abstract

          Although it is clear that there are short-term effects of sodium intake on blood pressure, little is known about the most relevant timing of sodium exposure for the onset of hypertension. This question can be addressed only in cohorts with repeated measures of sodium intake.

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          Most cited references15

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          Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements.

          Previous cohort studies of fat intake and risk of coronary heart disease (CHD) have been inconsistent, probably due in part to methodological differences and various limitations, including inadequate dietary assessment and incomplete adjustment for total energy intake. The authors analyzed repeated assessment of diet from the Nurses' Health Study to examine the associations between intakes of four major types of fat (saturated, monounsaturated, polyunsaturated, and trans fats) and risk of CHD during 14 years of follow-up (1980-1994) by using alternative methods for energy adjustment. In particular, the authors compared four risk models for energy adjustment: the standard multivariate model, the energy-partition model, the nutrient residual model, and the multivariate nutrient density model. Within each model, the authors compared four different approaches for analyzing repeated dietary measurements: baseline diet only, the most recent diet, and two different algorithms for calculating cumulative average diets. The substantive results were consistent across all models; that is, higher intakes of saturated and trans fats were associated with increased risk of CHD, while higher intakes of monounsaturated and polyunsaturated fats were associated with reduced risk. When nutrients were considered as continuous variables, the four energy-adjustment methods yielded similar associationS. However, the interpretation of the relative risks differed across models. In addition, within each model, the methods using the cumulative averages in general yielded stronger associations than did those using either only baseline diet or the most recent diet. When the nutrients were categorized according to quintiles, the residual and the nutrient density models, which gave similar results, yielded statistically more significant tests for linear trend than did the standard and the partition models.
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            A structural approach to selection bias.

            The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects ("selection bias") and those resulting from the existence of common causes of exposure and outcome ("confounding"). This classification also leads to a unified approach to adjust for selection bias.
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              Adjusted survival curves with inverse probability weights.

              Kaplan-Meier survival curves and the associated nonparametric log rank test statistic are methods of choice for unadjusted survival analyses, while the semiparametric Cox proportional hazards regression model is used ubiquitously as a method for covariate adjustment. The Cox model extends naturally to include covariates, but there is no generally accepted method to graphically depict adjusted survival curves. The authors describe a method and provide a simple worked example using inverse probability weights (IPW) to create adjusted survival curves. When the weights are non-parametrically estimated, this method is equivalent to direct standardization of the survival curves to the combined study population.
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                Author and article information

                Journal
                Epidemiology
                Epidemiology
                Ovid Technologies (Wolters Kluwer Health)
                1044-3983
                2013
                May 2013
                : 24
                : 3
                : 410-418
                Article
                10.1097/EDE.0b013e318289e047
                3909658
                23466527
                7125b50e-b460-4e46-991a-81a9a69c7365
                © 2013
                History

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