http://www.epi-perspectives.com The most accessed research articles published by Epidemiologic Perspectives & Innovations 2010-01-20T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ Austin Bradford Hill's landmark 1965 paper contains several important lessons for the current conduct of epidemiology. Unfortunately, it is almost exclusively cited as the source of the "Bradford-Hill criteria" for inferring causation when association is observed, despite Hill's explicit statement that cause-effect decisions cannot be based on a set of rules. Overlooked are Hill's important lessons about how to make decisions based on epidemiologic evidence. He advised epidemiologists to avoid over-emphasizing statistical significance testing, given the observation that systematic error is often greater than random error. His compelling and intuitive examples point out the need to consider costs and benefits when making decisions about health-promoting interventions. These lessons, which offer ways to dramatically increase the contribution of health science to decision making, are as needed today as they were when Hill presented them. http://www.epi-perspectives.com/content/1/1/3 Carl Phillips Karen Goodman Epidemiologic Perspectives & Innovations 2004, 1:3 2004-10-04T00:00:00Z doi:10.1186/1742-5573-1-3 Epidemiologic Perspectives & Innovations 1742-5573 1 3 2004-10-04T00:00:00Z XML Background: The WINPEPI (PEPI-for-Windows) computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. They aim to complement other statistics packages. The programs are free, and can be downloaded from the Internet.ImplementationThere are at present four WINPEPI programs: DESCRIBE, for use in descriptive epidemiology, COMPARE2, for use in comparisons of two independent groups or samples, PAIRSetc, for use in comparisons of paired and other matched observations, and WHATIS, a "ready reckoner" utility program. The programs contain 75 modules, each of which provides a number, sometimes a large number, of statistical procedures. The manuals explain the uses, limitations and applicability of specific procedures, and furnish formulae and references. Conclusions: WINPEPI provides a wide variety of statistical routines commonly used by epidemiologists, and is a handy resource for many procedures that are not very commonly used or easily found. The programs are in general user-friendly, although some users may be confused by the large numbers of options and results provided. The main limitations are the inability to read data files and the fact that only one of the programs presents graphic results. WINPEPI has a considerable potential as a learning and teaching aid. http://www.epi-perspectives.com/content/1/1/6 Joseph Abramson Epidemiologic Perspectives & Innovations 2004, 1:6 2004-12-17T00:00:00Z doi:10.1186/1742-5573-1-6 Epidemiologic Perspectives & Innovations 1742-5573 1 6 2004-12-17T00:00:00Z XML In 1986 the International Journal of Epidemiology published "Identifiability, Exchangeability and Epidemiological Confounding". We review the article from the perspective of a quarter century after it was first drafted and relate it to subsequent developments on confounding, ignorability, and collapsibility. http://www.epi-perspectives.com/content/6/1/4 Sander Greenland James Robins Epidemiologic Perspectives & Innovations 2009, 6:4 2009-09-04T00:00:00Z doi:10.1186/1742-5573-6-4 Epidemiologic Perspectives & Innovations 1742-5573 6 4 2009-09-04T00:00:00Z XML Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes.The authors propose using biomarkers known to be correlated with dietary intake, so as to strengthen analyses of diet-disease hypotheses. The authors consider combining self-reported intakes and biomarker levels using principal components, Howe's method, or a joint statistical test of effects in a bivariate model. They compared the statistical power of these methods with that of conventional univariate analyses of self-reported intake or of biomarker level. They used computer simulation of different disease risk models, with input parameters based on data from the literature on the relationship between lutein intake and age-related macular degeneration.The results showed that if the dietary effect on disease was fully mediated through the biomarker level, then the univariate analysis of the biomarker was the most powerful approach. However, combination methods, particularly principal components and Howe's method, were not greatly inferior in this situation, and were as good as, or better than, univariate biomarker analysis if mediation was only partial or non-existent. In some circumstances sample size requirements were reduced to 20-50% of those required for conventional analyses of self-reported intake.The authors conclude that (i) including biomarker data in addition to the usual dietary data in a cohort could greatly strengthen the investigation of diet-disease relationships, and (ii) when the extent of mediation through the biomarker is unknown, use of principal components or Howe's method appears a good strategy. http://www.epi-perspectives.com/content/7/1/2 Laurence Freedman Victor Kipnis Arthur Schatzkin Natasa Tasevska Nancy Potischman Epidemiologic Perspectives & Innovations 2010, 7:2 2010-01-20T00:00:00Z doi:10.1186/1742-5573-7-2 Epidemiologic Perspectives & Innovations 1742-5573 7 2 2010-01-20T00:00:00Z PDF Interaction measured on the additive scale has been argued to be better correlated with biologic interaction than when measured on the multiplicative scale. Measures of interaction on the additive scale have been developed using risk ratios. However, in studies that use odds ratios as the sole measure of effect, the calculation of these measures of additive interaction is usually performed by directly substituting odds ratios for risk ratios. Yet assessing additive interaction based on replacing risk ratios by odds ratios in formulas that were derived using the former may be erroneous. In this paper, we evaluate the extent to which three measures of additive interaction – the interaction contrast ratio (ICR), the attributable proportion due to interaction (AP), and the synergy index (S), estimated using odds ratios versus using risk ratios differ as the incidence of the outcome of interest increases in the source population and/or as the magnitude of interaction increases. Our analysis shows that the difference between the two depends on the measure of interaction used, the type of interaction present, and the baseline incidence of the outcome. Substituting odds ratios for risk ratios, when calculating measures of additive interaction, may result in misleading conclusions. Of the three measures, AP appears to be the most robust to this direct substitution. Formulas that use stratum specific odds and odds ratios to accurately calculate measures of additive interaction are presented. http://www.epi-perspectives.com/content/3/1/5 Linda Kalilani Julius Atashili Epidemiologic Perspectives & Innovations 2006, 3:5 2006-04-18T00:00:00Z doi:10.1186/1742-5573-3-5 Epidemiologic Perspectives & Innovations 1742-5573 3 5 2006-04-18T00:00:00Z XML Background: In epidemiologic studies, cancer stage is an important predictor of outcomes. However, cancer stage is typically unavailable in medical insurance claims datasets, thus limiting the usefulness of such data for epidemiologic studies. Therefore, we sought to develop an algorithm to predict cancer stage based on covariates available from claims-based data. Methods: We identified a cohort of 77,306 women age ≥ 66 years with stage I-IV breast cancer, using the Surveillence Epidemiology and End Results (SEER)-Medicare database. We formulated an algorithm to predict cancer stage using covariates (demographic, tumor, and treatment characteristics) obtained from claims. Logistic regression models derived prediction equations in a training set, and equations' test characteristics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value [NPV]) were calculated in a validation set. Results: Of the entire sample of women diagnosed with invasive breast cancer, 51% had stage I; 26% stage II; 11% stage III; and 4% stage IV disease. The equation predicting stage IV disease achieved sensitivity of 81%, specificity 89%, positive predictive value (PPV) 24%, and negative predictive value (NPV) 99%, while the equation distinguishing stage I/II from stage III disease achieved sensitivity 83%, specificity 78%, PPV 98%, and NPV 31%. Combined, the equations most accurately identified early stage disease and ascertained a sample in which 98% of patients were stage I or II. Conclusions: A claims-based algorithm was utilized to predict breast cancer stage, and was particularly successful when used to identify early stage disease. These prediction equations may be applied in future studies of breast cancer patients, substantially improving the utility of claims-based studies in this group. This method may similarly be employed to develop algorithms permitting claims-based epidemiologic studies of patients with other cancers. http://www.epi-perspectives.com/content/7/1/1 Grace Smith Ya-Chen Shih Sharon Giordano Benjamin Smith Thomas Buchholz Epidemiologic Perspectives & Innovations 2010, 7:1 2010-01-15T00:00:00Z doi:10.1186/1742-5573-7-1 Epidemiologic Perspectives & Innovations 1742-5573 7 1 2010-01-15T00:00:00Z XML When carefully planned and analysed, the case-cohort design is a powerful choice for follow-up studies with multiple event types of interest. While the literature is rich with analysis methods for case-cohort data, little is written about the designing of a case-cohort study. Our experiences in designing, coordinating and analysing the MORGAM case-cohort study are potentially useful for other studies with similar characteristics. The motivation for using the case-cohort design in the MORGAM genetic study is discussed and issues relevant to its planning and analysis are studied. We propose solutions for appending the earlier case-cohort selection after an extension of the follow-up period and for achieving maximum overlap between earlier designs and the case-cohort design. Approaches for statistical analysis are studied in a simulation example based on the MORGAM data. http://www.epi-perspectives.com/content/4/1/15 Sangita Kulathinal Juha Karvanen Olli Saarela Kari Kuulasmaa for the MORGAM Project Epidemiologic Perspectives & Innovations 2007, 4:15 2007-12-04T00:00:00Z doi:10.1186/1742-5573-4-15 Epidemiologic Perspectives & Innovations 1742-5573 4 15 2007-12-04T00:00:00Z XML We are pleased to publish an update to "Identifiabiliity, exchangeability and epidemiological confounding" (IEEC) by Sander Greenland and James Robins, originally published in 1986 in the International Journal of Epidemiology. This is the first in a series of updates to classic epidemiologic-methods papers that EP&I has commissioned. http://www.epi-perspectives.com/content/6/1/3 George Maldonado Epidemiologic Perspectives & Innovations 2009, 6:3 2009-09-04T00:00:00Z doi:10.1186/1742-5573-6-3 Epidemiologic Perspectives & Innovations 1742-5573 6 3 2009-09-04T00:00:00Z XML Regression adjustment for the propensity score is a statistical method that reduces confounding from measured variables in observational data. A Bayesian propensity score analysis extends this idea by using simultaneous estimation of the propensity scores and the treatment effect. In this article, we conduct an empirical investigation of the performance of Bayesian propensity scores in the context of an observational study of the effectiveness of beta-blocker therapy in heart failure patients. We study the balancing properties of the estimated propensity scores. Traditional Frequentist propensity scores focus attention on balancing covariates that are strongly associated with treatment. In contrast, we demonstrate that Bayesian propensity scores can be used to balance the association between covariates and the outcome. This balancing property has the effect of reducing confounding bias because it reduces the degree to which covariates are outcome risk factors. http://www.epi-perspectives.com/content/6/1/5 Lawrence McCandless Paul Gustafson Peter Austin Adrian Levy Epidemiologic Perspectives & Innovations 2009, 6:5 2009-09-10T00:00:00Z doi:10.1186/1742-5573-6-5 Epidemiologic Perspectives & Innovations 1742-5573 6 5 2009-09-10T00:00:00Z XML ObjectiveTo examine the generalizability of two National Institutes of Health (NIH)-funded double-blind randomized placebo-controlled clinical trials in patients with chronic Lyme disease and to determine whether selection factors resulted in the unfavorable outcomes.DesignEpidemiologic review of the generalizability of two trials conducted by Klempner et al. This paper considers whether the study group was representative of the general chronic Lyme disease population. Results: In their article in The New England Journal of Medicine, Klempner et al. failed to discuss the limitations of their clinical trials. This epidemiologic review argues that their results are not generalizable to the overall Lyme disease population. The treatment failure reported by the authors may be the result of enrolling patients who remained ill after an average of 4.7 years and an average of 3 previous courses of treatment. The poor outcome cited in these trials may be explained by having selected patients who had undergone delayed treatment or multiple treatments unsuccessfully. These selection factors were not addressed by the studies' authors, nor have they been discussed by reviewers. The trials have been over-interpreted by the NIH and widely publicized in a press release. The results have been extrapolated to other groups of Lyme disease patients by commentators, by a case discussant in an influential medical journal, and by health insurance companies to deny antibiotic treatment. Conclusion: The Klempner et al. trials are assumed to be internally valid based on a Randomized Control Trial (RCT) design. However, this review argues that the trials have limited generalizability beyond the select group of patients with characteristics like those in the trial. Applying the findings to target populations with characteristics that differ from those included in these trials is inappropriate and may limit options for chronic Lyme disease patients who might benefit from antibiotic treatment. http://www.epi-perspectives.com/content/3/1/12 Daniel Cameron Epidemiologic Perspectives & Innovations 2006, 3:12 2006-10-17T00:00:00Z doi:10.1186/1742-5573-3-12 Epidemiologic Perspectives & Innovations 1742-5573 3 12 2006-10-17T00:00:00Z XML