Epidemiologic Perspectives & Innovations - Latest Articles

Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies?

Laurence Freedman — 2010-01-20T00:00:00Z

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.

A method to predict breast cancer stage using Medicare claims

Grace Smith — 2010-01-15T00:00:00Z

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.

Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients

Lawrence McCandless — 2009-09-10T00:00:00Z

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.

Identifiability, exchangeability and confounding revisited

Sander Greenland — 2009-09-04T00:00:00Z

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.

Update: Greenland and Robins (1986). Identifiability, exchangeability and epidemiological confounding.

George Maldonado — 2009-09-04T00:00:00Z

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.

The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"

Andrew Ward — 2009-06-17T00:00:00Z

As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice of statistical methods as well as on the warrant attributed to the causal claims based on the use of such methods. For example, much of the data used by people interested in making causal claims come from non-experimental, observational studies in which random allocations to treatment and control groups are not present. Thus, one of the most important problems in the social and health sciences concerns making justified causal inferences using non-experimental, observational data. In this paper, I examine one method of justifying such inferences that is especially widespread in epidemiology and the health sciences generally – the use of causal criteria. I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation. As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims.

Trend tests for the evaluation of exposure-response relationships in epidemiological exposure studies

Ludwig Hothorn — 2009-03-06T00:00:00Z

One possibility for the statistical evaluation of trends in epidemiological exposure studies is the use of a trend test for data organized in a 2 × k contingency table. Commonly, the exposure data are naturally grouped or continuous exposure data are appropriately categorized. The trend test should be sensitive to any shape of the exposure-response relationship. Commonly, a global trend test only determines whether there is a trend or not. Once a trend is seen it is important to identify the likely shape of the exposure-response relationship. This paper introduces a best contrast approach and an alternative approach based on order-restricted information criteria for the model selection of a particular exposure-response relationship. For the simple change point alternative H1 : π1 = ...= πq <πq+1 = ... = πk an appropriate approach for the identification of a global trend as well as for the most likely shape of that exposure-response relationship is characterized by simulation and demonstrated for real data examples. Power and simultaneous confidence intervals can be estimated as well. If the conditions are fulfilled to transform the exposure-response data into a 2 × k table, a simple approach for identification of a global trend and its elementary shape is available for epidemiologists.

Using the National Health Interview Survey to understand and address the impact of tobacco in the United States: past perspectives and future considerations

Cathy Backinger — 2008-12-04T00:00:00Z

ObjectiveThe National Health Interview Survey (NHIS) is a continuous, nationwide, household interview survey of the civilian noninstitutionalized population of the United States. This annual survey is conducted by the National Center for Health Statistics, part of the Centers for Disease Control and Prevention. Since 1965, the survey and its supplements have provided data on issues related to the use of cigarettes and other tobacco products. This paper describes the survey, provides an overview of peer-reviewed and government-issued research that uses tobacco-related data from the NHIS, and suggests additional areas for exploration and directions for future research.Data sourcesWe performed literature searches using the PubMed database, selecting articles from 1966 to 2008. Study selection. Inclusion criteria were relevancy to tobacco research and primary use of NHIS data; 117 articles met these criteria. Data extraction and synthesis. Tobacco-related data from the NHIS have been used to analyze smoking prevalence and trends; attitudes, knowledge, and beliefs; initiation; cessation and advice to quit; health care practices; health consequences; secondhand smoke exposure; and use of smokeless tobacco. To date, use of these data has had broad application; however, great potential still exists for additional use. Conclusion: NHIS data provide information that can be useful to both practitioners and researchers. It is important to explore new and creative ways to best use these data and to address the full range of salient tobacco-related topics. Doing so will better inform future tobacco control research and programs.

Methods for stratification of person-time and events - a prerequisite for Poisson regression and SIR estimation

Klaus Rostgaard — 2008-11-14T00:00:00Z

IntroductionMany epidemiological methods for analysing follow-up studies require the calculation of rates based on accumulating person-time and events, stratified by various factors. Managing this stratification and accumulation is often the most difficult aspect of this type of analysis.TutorialWe provide a tutorial on accumulating person-time and events, stratified by various factors i.e. creating event-time tables. We show how to efficiently generate event-time tables for many different outcomes simultaneously. We also provide a new vocabulary to characterise and differentiate time-varying factors. The tutorial is focused on using a SAS macro to perform most of the common tasks in the creation of event-time tables. All the most common types of time-varying covariates can be generated and categorised by the macro. It can also provide output suitable for other types of survival analysis (e.g. Cox regression). The aim of our methodology is to support the creation of bug-free, readable, efficient, capable and easily modified programs for making event-time tables. We briefly compare analyses based on event-time tables with Cox regression and nested case-control studies for the analysis of follow-up data. Conclusion: Anyone working with time-varying covariates, particularly from large detailed person-time data sets, would gain from having these methods in their programming toolkit.

Case-case analysis of enteric diseases with routine surveillance data: Potential use and example results

Nick Wilson — 2008-10-31T00:00:00Z

Background: Case-control studies and outbreak investigations are the major epidemiological tools for providing detailed information on enteric disease sources and risk factors, but these investigations can be constrained by cost and logistics. Methods: We explored the advantages and disadvantages of comparing risk factors for enteric diseases using the case-case method. The main issues are illustrated with an analysis of routine notification data on enteric diseases for 2006 collected by New Zealand's national surveillance system. Results: Our analyses of aggregated New Zealand surveillance data found that the associations (crude odds ratios) for risk factors of enteric disease were fairly consistent with findings from local case-control studies and outbreak investigations, adding support for the use of the case-case analytical approach. Despite various inherent limitations, such an approach has the potential to contribute to the monitoring of risk factor trends for enteric diseases. Nevertheless, using the case-case method for analysis of routine surveillance data may need to be accompanied by: (i) reduction of potential selection and information biases by improving the quality of the surveillance data; and (ii) reduction of confounding by conducting more sophisticated analyses based on individual-level data. Conclusion: Case-case analyses of enteric diseases using routine surveillance data might be a useful low-cost means to study trends in enteric disease sources and inform control measures. If used, it should probably supplement rather than replace outbreak investigations and case-control studies. Furthermore, it could be enhanced by utilising high quality individual-level data provided by nationally-representative sentinel sites for enteric disease surveillance.