The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects

Abstract Background In molecular epidemiology studies biospecimen data are collected, often with the purpose of evaluating the synergistic role between a biomarker and another feature on an outcome. Typically, biomarker data are collected on only a proportion of subjects eligible for study, leading to a missing data problem. Missing data methods, however, are not customarily incorporated into analyses. Instead, complete-case (CC) analyses are performed, which can result in biased and inefficient estimates. Methods Through simulations, we characterized the performance of CC methods when interaction effects are estimated. We also investigated whether standard multiple imputation (MI) could improve estimation over CC methods when the data are not missing at random (NMAR) and auxiliary information may or may not exist. Results CC analyses were shown to result in considerable bias and efficiency loss. While MI reduced bias and increased efficiency over CC methods under specific conditions, it too resulted in biased estimates depending on the strength of the auxiliary data available and the nature of the missingness. In particular, CC performed better than MI when extreme values of the covariate were more likely to be missing, while MI outperformed CC when missingness of the covariate related to both the covariate and outcome. MI always improved performance when strong auxiliary data were available. In a real study, MI estimates of interaction effects were attenuated relative to those from a CC approach. Conclusions Our findings suggest the importance of incorporating missing data methods into the analysis. If the data are MAR, standard MI is a reasonable method. Auxiliary variables may make this assumption more reasonable even if the data are NMAR. Under NMAR we emphasize caution when using standard MI and recommend it over CC only when strong auxiliary data are available. MI, with the missing data mechanism specified, is an alternative when the data are NMAR. In all cases, it is recommended to take advantage of MI's ability to account for the uncertainty of these assumptions

A Bayesian proportional hazards regression model with non-ignorably missing time-varying covariates

Missing covariate data is common in observational studies of time to an event, especially when covariates are repeatedly measured over time. Failure to account for the missing data can lead to bias or loss of efficiency, especially when the data are non-ignorably missing. Previous work has focused on the case of fixed covariates rather than those that are repeatedly measured over the follow-up period, so here we present a selection model that allows for proportional hazards regression with time-varying covariates when some covariates may be non-ignorably missing. We develop a fully Bayesian model and obtain posterior estimates of the parameters via the Gibbs sampler in WinBUGS. We illustrate our model with an analysis of post-diagnosis weight change and survival after breast cancer diagnosis in the Long Island Breast Cancer Study Project (LIBCSP) follow-up study. Our results indicate that post-diagnosis weight gain is associated with lower all-cause and breast cancer specific survival among women diagnosed with new primary breast cancer. Our sensitivity analysis showed only slight differences between models with different assumptions on the missing data mechanism yet the complete case analysis yielded markedly different results

Validation and Calibration of a Model Used to Reconstruct Historical Exposure to Polycyclic Aromatic Hydrocarbons for Use in Epidemiologic Studies

OBJECTIVES: We previously developed a historical reconstruction model to estimate exposure to airborne polycyclic aromatic hydrocarbons (PAHs) from traffic back to 1960 for use in case–control studies of breast cancer risk. Here we report the results of four exercises to validate and calibrate the model. METHODS: Model predictions of benzo[a]pyrene (BaP) concentration in soil and carpet dust were tested against measurements collected at subjects’ homes at interview. In addition, predictions of air intake of BaP were compared with blood PAH–DNA adducts. These same soil, carpet, and blood measurements were used for model optimization. In a separate test of the meteorological dispersion part of the model, predictions of hourly concentrations of carbon monoxide from traffic were compared with data collected at a U.S. Environmental Protection Agency monitoring station. RESULTS: The data for soil, PAH–DNA adducts, and carbon monoxide concentrations were all consistent with model predictions. The carpet dust data were inconsistent, suggesting possible spatial confounding with PAH-containing contamination tracked in from outdoors or unmodeled cooking sources. BaP was found proportional to other PAHs in our soil and dust data, making it reasonable to use BaP historical data as a surrogate for other PAHs. Road intersections contributed 40–80% of both total emissions and average exposures, suggesting that the repertoire of simple markers of exposure, such as traffic counts and/or distance to nearest road, needs to be expanded to include distance to nearest intersection

Imputation method for lifetime exposure assessment in air pollution epidemiologic studies

Background: Environmental epidemiology, when focused on the life course of exposure to a specific pollutant, requires historical exposure estimates that are difficult to obtain for the full time period due to gaps in the historical record, especially in earlier years. We show that these gaps can be filled by applying multiple imputation methods to a formal risk equation that incorporates lifetime exposure. We also address challenges that arise, including choice of imputation method, potential bias in regression coefficients, and uncertainty in age-at-exposure sensitivities. Methods: During time periods when parameters needed in the risk equation are missing for an individual, the parameters are filled by an imputation model using group level information or interpolation. A random component is added to match the variance found in the estimates for study subjects not needing imputation. The process is repeated to obtain multiple data sets, whose regressions against health data can be combined statistically to develop confidence limits using Rubin’s rules to account for the uncertainty introduced by the imputations. To test for possible recall bias between cases and controls, which can occur when historical residence location is obtained by interview, and which can lead to misclassification of imputed exposure by disease status, we introduce an “incompleteness index,” equal to the percentage of dose imputed (PDI) for a subject. “Effective doses” can be computed using different functional dependencies of relative risk on age of exposure, allowing intercomparison of different risk models. To illustrate our approach, we quantify lifetime exposure (dose) from traffic air pollution in an established case–control study on Long Island, New York, where considerable in-migration occurred over a period of many decades. Results: The major result is the described approach to imputation. The illustrative example revealed potential recall bias, suggesting that regressions against health data should be done as a function of PDI to check for consistency of results. The 1% of study subjects who lived for long durations near heavily trafficked intersections, had very high cumulative exposures. Thus, imputation methods must be designed to reproduce non-standard distributions. Conclusions: Our approach meets a number of methodological challenges to extending historical exposure reconstruction over a lifetime and shows promise for environmental epidemiology. Application to assessment of breast cancer risks will be reported in a subsequent manuscript

Cardiovascular Disease Mortality Among Breast Cancer Survivors

Cardiovascular disease (CVD) is of increasing concern among breast cancer survivors. However the burden of this comorbidity in this group relative to the general population, and its temporal pattern, remains unknown

Exposure to fogger trucks and breast cancer incidence in the Long Island Breast Cancer Study Project: a case-control study

Background: Few studies have supported an association between breast cancer and DDT, usually assessed with biomarkers that cannot discern timing of exposure, or differentiate between the accumulation of chronic low-dose versus acute high-dose exposures in the past. Previous studies suggest that an association may be evident only among women exposed to DDT during biologically susceptible windows, or among those diagnosed with estrogen receptor/progesterone receptor-positive (ER+PR+) breast cancer subtypes. Self-reported acute exposure to a fogger truck, which sprayed DDT prior to 1972, was hypothesized to increase the risk of breast cancer, particularly among women exposed at a young age or diagnosed with ER+PR+ breast cancer. Methods: We examined these possibilities in the Long Island Breast Cancer Study Project (LIBCSP) (1,508 cases, 1,556 controls), which included exposure assessment by structured questionnaire and serum samples collected between 1996–1998, using adjusted logistic and polytomous regression to estimate ORs and 95% CIs. Results: Women with ER+PR+ breast cancer had a 44% increased odds of ever seeing a pre-1972 fogger truck compared to other subtypes (OR = 1.44; 95% CI 1.08-1.93). However, there was little variation in the observed increase in breast cancer risk when considering all women who reported seeing a pre-1972 fogger truck at their residence (OR = 1.16; 95% CI 0.98, 1.37), or during hypothesized susceptible windows. Self-reported acute exposure was not correlated with serum concentrations, a biomarker of long-term exposure. Conclusions: These findings support the hypothesis that seeing a fogger truck, a proxy measure for acute DDT exposure, may be associated with ER+PR+ tumors, the most commonly diagnosed breast cancer subtype among American women

Particulate air pollution and susceptibility to the development of pulmonary tuberculosis disease in North Carolina: an ecological study

Although Mycobacterium tuberculosis is the causative agent of pulmonary tuberculosis (PTB), environmental factors may influence disease progression. Ecologic studies conducted in countries outside the USA with high levels of air pollution and PTB have suggested a link between active disease and ambient air pollution. The present investigation is the first to examine the ambient air pollution/PTB association in a country, where air pollution levels are comparatively lower. We used Poisson regression models to examine the association of outdoor air pollutants, PM10 and PM2.5 with rates of PTB in North Carolina residents during 1993–2007. Results suggest a potential association between long-term exposure to particulate matter (PM) and PTB disease. In view of the high levels of air pollution and high rates of PTB worldwide, a potential association between ambient air pollution and tuberculosis warrants further study

Genetic polymorphisms of phase I metabolizing enzyme genes, their interaction with lifetime grilled and smoked meat intake, and breast cancer incidence

To examine associations between 22 CYP single nucleotide polymorphisms (SNPs) and breast cancer incidence and their interactions with grilled–smoked meat intake, a source of polycyclic aromatic hydrocarbons

Types of Fish Consumed and Fish Preparation Methods in Relation to Pancreatic Cancer Incidence: The VITAL Cohort Study

The associations of types of fish and fish preparation methods with pancreatic cancer risk remain unknown. The authors conducted a prospective cohort study in western Washington State among 66,616 adults, aged 50–76 years, who participated in the VITamins And Lifestyle cohort study. Diet was assessed by a food frequency questionnaire. Pancreatic cancer cases were identified by linkage to the Surveillance, Epidemiology, and End Results cancer registry. During an average follow-up of 6.8 years, 151 participants developed pancreatic cancer (adenocarcinoma). Long-chain (n-3) polyunsaturated fatty acids (LC-PUFAs) and nonfried fish intake were inversely associated with pancreatic cancer incidence. When the highest and lowest tertiles of exposure were compared, the multivariable-adjusted hazard ratio of pancreatic cancer was 0.62 (95% confidence interval: 0.40, 0.98) (Ptrend = 0.08) for LC-PUFAs and 0.55 (95% confidence interval: 0.34, 0.88) (Ptrend = 0.045) for nonfried fish. Docosahexaenoic acid showed a greater inverse association with pancreatic cancer than eicosapentaenoic acid. No statistically significant associations were observed with fried fish and shellfish consumption. The potential health impact of fish consumption may depend on the types of fish consumed and fish preparation methods. LC-PUFAs, particularly docosahexaenoic acid, and nonfried fish, but not shellfish or fried fish, may be beneficial in the primary prevention of pancreatic cancer

Dietary flavonoid intake and Barrett's esophagus in western Washington State

Flavonoids, concentrated in fruits and vegetables, demonstrate in experimental studies chemopreventive properties in relation to Barrett's esophagus (BE), a precursor lesion for esophageal adenocarcinoma. One case-control investigation reported an inverse association between isoflavone intake and odds of BE, yet no epidemiologic study has considered other flavonoid classes, which are more commonly consumed by Americans