Standard Errors for Attributable Risk for Simple and Complex Sample Designs
Biostatistics Branch, National Cancer Institute, 6120 Executive Boulevard, Room 8024, Bethesda, Maryland 20892, USA. Biometrics
(Impact Factor: 1.57).
10/2005; 61(3):847-55. DOI: 10.1111/j.1541-0420.2005.00355.x
Adjusted attributable risk (AR) is the proportion of diseased individuals in a population that is due to an exposure. We consider estimates of adjusted AR based on odds ratios from logistic regression to adjust for confounding. Influence function methods used in survey sampling are applied to obtain simple and easily programmable expressions for estimating the variance of AR. These variance estimators can be applied to data from case-control, cross-sectional, and cohort studies with or without frequency or individual matching and for sample designs with subject samples that range from simple random samples to (sample) weighted multistage stratified cluster samples like those used in national household surveys. The variance estimation of AR is illustrated with: (i) a weighted stratified multistage clustered cross-sectional study of childhood asthma from the Third National Health and Examination Survey (NHANES III), and (ii) a frequency-matched case-control study of melanoma skin cancer.
Available from: Antonio Gasparrini
- "Analytical formulae for confidence intervals of attributable risk measures are not easily produced , and this also applies to the extended versions developed here. Although approximated estimators have been proposed [15,16], in this context the most straightforward approach is to rely on interval estimation obtained empirically through Monte Carlo simulations [17,18]. "
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ABSTRACT: Measures of attributable risk are an integral part of epidemiological analyses, particularly when aimed at the planning and evaluation of public health interventions. However, the current definition of such measures does not consider any temporal relationships between exposure and risk. In this contribution, we propose extended definitions of attributable risk within the framework of distributed lag non-linear models, an approach recently proposed for modelling delayed associations in either linear or non-linear exposure-response associations.
We classify versions of attributable number and fraction expressed using either a forward or backward perspective. The former specifies the future burden due to a given exposure, while the latter summarizes the current burden due to the exposure experienced in the past. In addition, we illustrate how the components related to sub-ranges of the exposure can be separated.
We apply these methods for estimating the mortality risk attributable to outdoor temperature in two cities, London and Rome, using time series data for the periods 1993-2006 and 1992-2010, respectively. The analysis provides estimates of the overall mortality burden attributable to temperature, and then computes the components attributable to cold and heat and then mild and extreme temperatures.
These extended definitions of attributable risk account for the additional temporal dimension which characterizes exposure-response associations, providing more appropriate attributable measures in the presence of dependencies characterized by potentially complex temporal patterns.
Available from: Ravi Varadhan
- "The use of sample weights makes the risk estimates of the lexpit model for case–control data; both require the same design considerations for accurate estimation of standard errors of estimates. We therefore use influence-based methods, a common approach for linearized variance estimation of survey statistics , to derive variances for the lexpit model’s risk estimates. In the Additional file 1 we summarize the optimization algorithm and the influence approach for obtaining variance estimates for the lexpit model parameters. "
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ABSTRACT: Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. Yet technical challenges have limited the consideration of additive risk models in case--control studies.
Using a flexible risk regression model that allows additive and multiplicative components to estimate absolute risks and risk differences, we report a new analysis of data from the population-based case--control Environment And Genetics in Lung cancer Etiology study, conducted in Northern Italy between 2002--2005. The analysis provides estimates of the gender-specific absolute risk (cumulative risk) for non-smoking- and smoking-associated lung cancer, adjusted for demographic, occupational, and smoking history variables.
In the multiple-variable lexpit regression, the adjusted 3-year absolute risk of lung cancer in never smokers was 4.6 per 100,000 persons higher in women than men. However, the absolute increase in 3-year risk of lung cancer for every 10 additional pack-years smoked was less for women than men, 13.6 versus 52.9 per 100,000 persons.
In a Northern Italian population, the absolute risk of lung cancer among never smokers is higher in women than men but among smokers is lower in women than men. Lexpit regression is a novel approach to additive-multiplicative risk modeling that can contribute to clearer interpretation of population-based case--control studies.
Available from: Brian Rostron
- "). Our method could be further enhanced through the development of methods for constructing CIs for the estimates, analogous to methods for similar estimates of population-attributable risk (Flegal et al., 2005; Graubard & Fears, 2005). "
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ABSTRACT: Introduction: Smoking is the leading cause of preventable mortality in the United States, but the methods and data used in the Centers
for Disease Control and Prevention’s (CDC) published estimates of adult smoking-attributable mortality have not been substantially
revised since their introduction in the 1980s.
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