Survival analysis with error-prone time-varying covariates: a risk set calibration approach

Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure

Regression calibration as developed by Rosner, Spiegelman, and Willett is used to adjust the bias in effect estimates due to measurement error in continuous exposures. The method involves two models: a measurement error model relating the mismeasured exposure to the true (or gold-standard) exposure and an outcome model relating the mismeasured exposure to the outcome. However, no comprehensive guidance exists for determining which covariates should be included in each model. In this article, we investigate the selection of the minimal and most efficient covariate adjustment sets under a causal inference framework. We show that to address the measurement error, researchers must adjust for, in both measurement error and outcome models, any common causes (1) of true exposure and the outcome and (2) of measurement error and the outcome. We also show that adjusting for so-called prognostic variables that are independent of true exposure and measurement error in the outcome model, may increase efficiency, while adjusting for any covariates that are associated only with true exposure generally results in efficiency loss in realistic settings. We apply the proposed covariate selection approach to the Health Professional Follow-up Study dataset to study the effect of fiber intake on cardiovascular disease. Finally, we extend the originally proposed estimators to a nonparametric setting where effect modification by covariates is allowed.

Asynchronous and error-prone longitudinal data analysis via functional calibration

In many longitudinal settings, time-varying covariates may not be measured at the same time as responses and are often prone to measurement error. Naive last-observation-carried-forward methods incur estimation biases, and existing kernel-based methods suffer from slow convergence rates and large variations. To address these challenges, we propose a new functional calibration approach to efficiently learn longitudinal covariate processes based on sparse functional data with measurement error. Our approach, stemming from functional principal component analysis, calibrates the unobserved synchronized covariate values from the observed asynchronous and error-prone covariate values, and is broadly applicable to asynchronous longitudinal regression with time-invariant or time-varying coefficients. For regression with time-invariant coefficients, our estimator is asymptotically unbiased, root-n consistent, and asymptotically normal; for time-varying coefficient models, our estimator has the optimal varying coefficient model convergence rate with inflated asymptotic variance from the calibration. In both cases, our estimators present asymptotic properties superior to the existing methods. The feasibility and usability of the proposed methods are verified by simulations and an application to the Study of Women's Health Across the Nation, a large-scale multisite longitudinal study on women's health during midlife.

A smoothed corrected score approach for proportional hazards model with misclassified discretized covariates induced by error-contaminated continuous time-dependent exposure

We consider the proportional hazards model in which the covariates include the discretized categories of a continuous time-dependent exposure variable measured with error. Naively ignoring the measurement error in the analysis may cause biased estimation and erroneous inference. Although various approaches have been proposed to deal with measurement error when the hazard depends linearly on the time-dependent variable, it has not yet been investigated how to correct when the hazard depends on the discretized categories of the time-dependent variable. To fill this gap in the literature, we propose a smoothed corrected score approach based on approximation of the discretized categories after smoothing the indicator function. The consistency and asymptotic normality of the proposed estimator are established. The observation times of the time-dependent variable are allowed to be informative. For comparison, we also extend to this setting two approximate approaches, the regression calibration and the risk-set regression calibration. The methods are assessed by simulation studies and by application to data from an HIV clinical trial.

Childhood beverage intake and risk of hypertension and hyperlipidaemia in young adults

An epidemiological analysis assessing beverage consumption and risk factors for cardiovascular disease was conducted. Participants were 9-16 years old at enrolment, completed food frequency questionnaires in 1996-2001 and self-reported outcomes in 2010-2014. Exclusion criteria included missing data on relevant variables and covariates, prevalent disease before 2005, and implausible/extreme weight or energy intake. Intakes of orange juice, apple/other fruit juice, sugar-sweetened beverages and diet soda were related to the risk of incident hypertension or hyperlipidaemia using Cox proportional hazards regression, adjusting for diet, energy intake, age, smoking, physical activity and body mass index. There were 9,043 participants with 618 cases of hypertension and 850 of hyperlipidaemia in 17 years of mean follow-up. Sugar-sweetened beverage intake but not fruit juice nor diet soda was associated with hypertension (hazard ratio (95% confidence interval): 1.16 (1.03, 1.31)) in males. This study can guide beverage consumption as it relates to early predictors of cardiovascular disease.

Approximate profile likelihood estimation for Cox regression with covariate measurement error

In nutritional epidemiology, measurement error in covariates is a well-known problem since dietary intakes are usually assessed through self-reporting. In this article, we consider an additive error model in which error variables are highly correlated, and propose a new method called approximate profile likelihood estimation (APLE) for covariates measured with error in the Cox regression. Asymptotic normality of this estimator is established under regularity conditions, and simulation studies are conducted to examine the finite sample performance of the proposed estimator empirically. Moreover, the popular correction method called regression calibration is shown to be a special case of APLE. We then apply APLE to deal with measurement error in some nutrients of interest in the EPIC-InterAct Study under a sensitivity analysis framework.

Mecor: An R package for measurement error correction in linear regression models with a continuous outcome

Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated covariate-outcome association. While several texts on measurement error correction methods are available, these methods remain seldomly applied. To improve the use of measurement error correction methodology, we developed mecor, an R package that implements measurement error correction methods for regression models with a continuous outcome. Measurement error correction requires information about the measurement error model and its parameters. This information can be obtained from four types of studies, used to estimate the parameters of the measurement error model: an internal validation study, a replicates study, a calibration study and an external validation study. In the package mecor, regression calibration methods and a maximum likelihood method are implemented to correct for measurement error in a continuous covariate in regression analyses. Additionally, methods of moments methods are implemented to correct for measurement error in the continuous outcome in regression analyses. Variance estimation of the corrected estimators is provided in closed form and using the bootstrap.

A novel calibration framework for survival analysis when a binary covariate is measured at sparse time points

The goals in clinical and cohort studies often include evaluation of the association of a time-dependent binary treatment or exposure with a survival outcome. Recently, several impactful studies targeted the association between initiation of aspirin and survival following colorectal cancer (CRC) diagnosis. The value of this exposure is zero at baseline and may change its value to one at some time point. Estimating this association is complicated by having only intermittent measurements on aspirin-taking. Commonly used methods can lead to substantial bias. We present a class of calibration models for the distribution of the time of status change of the binary covariate. Estimates obtained from these models are then incorporated into the proportional hazard partial likelihood in a natural way. We develop non-parametric, semiparametric, and parametric calibration models, and derive asymptotic theory for the methods that we implement in the aspirin and CRC study. We further develop a risk-set calibration approach that is more useful in settings in which the association between the binary covariate and survival is strong.

Quantitative Characterization of Uncertainty in the Concentration-Response Relationship between Long-Term PM2.5 Exposure and Mortality at Low Concentrations

Extensive epidemiologic evidence supports a linear, no-threshold concentration-response (C-R) relationship between long-term exposure to fine particles (PM2.5) and mortality in the United States. While examinations of the C-R relationship are designed to assess the shape of the C-R curve, they do not provide the information needed to quantitatively characterize uncertainty at specific PM2.5 concentrations, which is often needed in the context of risk assessments and benefits analyses. We developed a novel approach, using information that is typically available in published epidemiologic studies, to quantitatively characterize uncertainty at different concentrations along the PM2.5 concentration distribution. Our approach utilizes the annual mean PM2.5 concentration and corresponding standard deviation from a published epidemiologic study to estimate the standard deviation of hypothetical PM2.5 concentration distributions defined at 0.1 μg/m3 increments. The hypothetical distributions are then used to derive adjusted uncertainty estimates in the reported effect estimate at low concentrations (i.e., concentrations lower than the annual mean observed in the study). We demonstrate the application of this method in six individual epidemiologic studies that examined the relationship between long-term PM2.5 exposure and mortality and were conducted in different geographic locations worldwide and at different PM2.5 concentrations. This new method allows for a more comprehensive quantitative evaluation of uncertainty in the shape of the C-R relationship between long-term PM2.5 exposure and mortality at concentrations below the mean annual concentrations observed in current studies.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Estimation in the Cox survival regression model with covariate measurement error and a changepoint

The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM₁₀ and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.

Best Practices for Conducting and Interpreting Studies to Validate Self-Report Dietary Assessment Methods

Careful consideration of the validity and reliability of methods intended to assess dietary intake is central to the robustness of nutrition research. A dietary assessment method with high validity is capable of providing useful measurement for a given purpose and context. More specifically, a method with high validity is well grounded in theory; its performance is consistent with that theory; and it is precise, dependable, and accurate within specified performance standards. Assessing the extent to which dietary assessment methods possess these characteristics can be difficult due to the complexity of dietary intake, as well as difficulties capturing true intake. We identified challenges and best practices related to the validation of self-report dietary assessment methods. The term validation is used to encompass various dimensions that must be assessed and considered to determine whether a given method is suitable for a specific purpose. Evidence on the varied concepts of validity and reliability should be interpreted in combination to inform judgments about the suitability of a method for a specified purpose. Self-report methods are the focus because they are used in most studies seeking to measure dietary intake. Biomarkers are important reference measures to validate self-report methods and are also discussed. A checklist is proposed to contribute to strengthening the literature on the validation of dietary assessment methods and ultimately, the nutrition literature more broadly.

There is no impact of exposure measurement error on latency estimation in linear models

Identification of the latency period for the effect of a time-varying exposure is key when assessing many environmental, nutritional, and behavioral risk factors. A pre-specified exposure metric involving an unknown latency parameter is often used in the statistical model for the exposure-disease relationship. Likelihood-based methods have been developed to estimate this latency parameter for generalized linear models but do not exist for scenarios where the exposure is measured with error, as is usually the case. Here, we explore the performance of naive estimators for both the latency parameter and the regression coefficients, which ignore exposure measurement error, assuming a linear measurement error model. We prove that, in many scenarios under this general measurement error setting, the least squares estimator for the latency parameter remains consistent, while the regression coefficient estimates are inconsistent as has previously been found in standard measurement error models where the primary disease model does not involve a latency parameter. Conditions under which this result holds are generalized to a wide class of covariance structures and mean functions. The findings are illustrated in a study of body mass index in relation to physical activity in the Health Professionals Follow-Up Study.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses

PURPOSE OF REVIEW

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data.

RECENT FINDINGS

We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

A simulation study on implementing marginal structural models in an observational study with switching medication based on a biomarker

Assessing treatment effectiveness in longitudinal data can be complex when treatments are not randomly assigned and patients are allowed to switch treatment to other or no treatment, often in a manner that is driven by changes in one or more variables associated with patient or clinical characteristics. There can be confounding of the treatment effect from a time-varying variable, i.e., one which is affected by previous exposure and can in turn also influence subsequent treatment changes. Precision medicine relies on validated biomarkers to better classify patients by their probable response to treatment. However, biomarkers may be a source of time-varying confounding, which are affected by prior treatment in the evaluation and are also subject to measurement errors. The impact of switching medications based on a biomarker has received less attention. We conducted simulation studies to explore biased estimation under various scenarios when marginal structural model estimations are employed. Holding model misspecification issues constant, bias is severe in the presence of multiple switching, along with measurement error and missing data in the covariates.

Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology

BACKGROUND

Several statistical approaches have been proposed to assess and correct for exposure measurement error. We aimed to provide a critical overview of the most common approaches used in nutritional epidemiology.

METHODS

MEDLINE, EMBASE, BIOSIS and CINAHL were searched for reports published in English up to May 2016 in order to ascertain studies that described methods aimed to quantify and/or correct for measurement error for a continuous exposure in nutritional epidemiology using a calibration study.

RESULTS

We identified 126 studies, 43 of which described statistical methods and 83 that applied any of these methods to a real dataset. The statistical approaches in the eligible studies were grouped into: a) approaches to quantify the relationship between different dietary assessment instruments and "true intake", which were mostly based on correlation analysis and the method of triads; b) approaches to adjust point and interval estimates of diet-disease associations for measurement error, mostly based on regression calibration analysis and its extensions. Two approaches (multiple imputation and moment reconstruction) were identified that can deal with differential measurement error.

CONCLUSIONS

For regression calibration, the most common approach to correct for measurement error used in nutritional epidemiology, it is crucial to ensure that its assumptions and requirements are fully met. Analyses that investigate the impact of departures from the classical measurement error model on regression calibration estimates can be helpful to researchers in interpreting their findings. With regard to the possible use of alternative methods when regression calibration is not appropriate, the choice of method should depend on the measurement error model assumed, the availability of suitable calibration study data and the potential for bias due to violation of the classical measurement error model assumptions. On the basis of this review, we provide some practical advice for the use of methods to assess and adjust for measurement error in nutritional epidemiology.

Survival analysis with functions of mismeasured covariate histories: the case of chronic air pollution exposure in relation to mortality in the nurses' health study

Environmental epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories, such as the 12-month moving average, in relation to chronic disease incidence or mortality. The individual exposure measurements that comprise such an exposure history are usually mis-measured, at least moderately, and, often, more substantially. To obtain unbiased estimates of Cox model hazard ratios for these complex mis-measured exposure functions, an extended risk set regression calibration method for Cox models is developed and applied to a study of long-term exposure to the fine particulate matter (PM_2.5) component of air pollution in relation to all-cause mortality in the Nurses' Health Study. Simulation studies under several realistic assumptions about the measurement error model and about the correlation structure of the repeated exposure measurements were conducted to assess the finite sample properties of this new method, and found that the method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage.

Evaluating Public Health Interventions: 4. The Nurses' Health Study and Methods for Eliminating Bias Attributable to Measurement Error and Misclassification

The Nurses' Health Study and many other large longitudinal cohorts around the world use the food frequency questionnaire to assess dietary intake over time, and to relate diet to health. Controversies concerning this questionnaire's ability to adequately measure diet have led to a flurry of methods for evaluating the magnitude of measurement error and misclassification in exposure assessment, and for correcting the point and interval estimates of effect on the basis of these assessment methods for this error. Nurses' Health Study investigators have been in the forefront of these developments and their applications, although hundreds of other investigators have also used them. This commentary provides an overview of the methods and their uses, and concludes with remarks on their potential applications in the evaluation of public health interventions.

Long-Term Exposure to Particulate Matter and Self-Reported Hypertension: A Prospective Analysis in the Nurses' Health Study

BACKGROUND

Studies have suggested associations between elevated blood pressure and short-term air pollution exposures, but the evidence is mixed regarding long-term exposures on incidence of hypertension.

OBJECTIVES

We examined the association of hypertension incidence with long-term residential exposures to ambient particulate matter (PM) and residential distance to roadway.

METHODS

We estimated 24-month and cumulative average exposures to PM10, PM2.5, and PM2.5-10 and residential distance to road for women participating in the prospective nationwide Nurses' Health Study. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for incident hypertension from 1988 to 2008 using Cox proportional hazards models adjusted for potential confounders. We considered effect modification by age, diet, diabetes, obesity, region, and latitude.

RESULTS

Among 74,880 participants, 36,812 incident cases of hypertension were observed during 960,041 person-years. In multivariable models, 10-μg/m3 increases in 24-month average PM10, PM2.5, and PM2.5-10 were associated with small increases in the incidence of hypertension (HR: 1.02, 95% CI: 1.00, 1.04; HR: 1.04, 95% CI: 1.00, 1.07; and HR: 1.03, 95% CI: 1.00, 1.07, respectively). Associations were stronger among women < 65 years of age (HR: 1.04, 95% CI: 1.01, 1.06; HR: 1.07, 95% CI: 1.02, 1.12; and HR: 1.05, 95% CI: 1.01, 1.09, respectively) and the obese (HR: 1.07, 95% CI: 1.04, 1.12; HR: 1.15, 95% CI: 1.07, 1.23; and HR: 1.13, 95% CI: 1.07, 1.19, respectively), with p-values for interaction < 0.05 for all models except age and PM2.5-10. There was no association with roadway proximity.

CONCLUSIONS

Long-term exposure to particulate matter was associated with small increases in risk of incident hypertension, particularly among younger women and the obese.

CITATION

Zhang Z, Laden F, Forman JP, Hart JE. 2016. Long-term exposure to particulate matter and self-reported hypertension: a prospective analysis in the Nurses' Health Study. Environ Health Perspect 124:1414-1420; http://dx.doi.org/10.1289/EHP163.

Origin, Methods, and Evolution of the Three Nurses' Health Studies

We have summarized the evolution of the Nurses' Health Study (NHS), a prospective cohort study of 121 700 married registered nurses launched in 1976; NHS II, which began in 1989 and enrolled 116 430 nurses; and NHS3, which began in 2010 and has ongoing enrollment. Over 40 years, these studies have generated long-term, multidimensional data, including lifestyle- and health-related information across the life course and an extensive repository of various biological specimens. We have described the questionnaire data collection, disease follow-up methods, biorepository resources, and data management and statistical procedures. Through integrative analyses, these studies have sustained a high level of scientific productivity and substantially influenced public health recommendations. We have highlighted recent interdisciplinary research projects and discussed future directions for collaboration and innovation.

Quantifying risk over the life course - latency, age-related susceptibility, and other time-varying exposure metrics

Identification of the latency period and age-related susceptibility, if any, is an important aspect of assessing risks of environmental, nutritional, and occupational exposures. We consider estimation and inference for latency and age-related susceptibility in relative risk and excess risk models. We focus on likelihood-based methods for point and interval estimation of the latency period and age-related windows of susceptibility coupled with several commonly considered exposure metrics. The method is illustrated in a study of the timing of the effects of constituents of air pollution on mortality in the Nurses' Health Study. Copyright © 2016 John Wiley & Sons, Ltd.

Long-term status and change of body fat distribution, and risk of colorectal cancer: a prospective cohort study

BACKGROUND

Although obesity has been linked to an increased risk of colorectal cancer (CRC), the risk associated with long-term status or change of body fat distribution has not been fully elucidated.

METHODS

Using repeated anthropometric assessments in the Nurses' Health Study and Health Professionals Follow-up Study, we prospectively investigated cumulative average waist circumference, hip circumference and waist-to-hip ratio, as well as their 10-year changes over adulthood, in relation to CRC risk over 23-24 years of follow-up. Cox proportional hazards models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI).

RESULTS

High waist circumference, hip circumference and waist-to-hip ratio were all associated with a higher CRC risk in men, even after adjusting for body mass index. The association was attenuated to null in women after adjusting for body mass index. Ten-year gain of waist circumference was positively associated with CRC risk in men (P for trend = 0.03), but not in women (P for trend = 0.34).Compared with men maintaining their waist circumference, those gaining waist circumference by ≥ 10 cm were at a higher risk of CRC, with a multivariable-adjusted HR of 1.59 (95% CI, 1.01-2.49). This association appeared to be independent of weight change.

CONCLUSIONS

Abdominal adiposity, independent of overall obesity, is associated with an increased CRC risk in men but not in women. Our findings also provide the first prospective evidence that waist circumference gain during adulthood may be associated with higher CRC risk in men, thus highlighting the importance of maintaining a healthy waist for CRC prevention.

The association of long-term exposure to PM2.5 on all-cause mortality in the Nurses' Health Study and the impact of measurement-error correction

BACKGROUND

Long-term exposure to particulate matter less than 2.5 μm in diameter (PM2.5) has been consistently associated with risk of all-cause mortality. The methods used to assess exposure, such as area averages, nearest monitor values, land use regressions, and spatio-temporal models in these studies are subject to measurement error. However, to date, no study has attempted to incorporate adjustment for measurement error into a long-term study of the effects of air pollution on mortality.

METHODS

We followed 108,767 members of the Nurses' Health Study (NHS) 2000-2006 and identified all deaths. Biennial mailed questionnaires provided a detailed residential address history and updated information on potential confounders. Time-varying average PM2.5 in the previous 12-months was assigned based on residential address and was predicted from either spatio-temporal prediction models or as concentrations measured at the nearest USEPA monitor. Information on the relationships of personal exposure to PM2.5 of ambient origin with spatio-temporal predicted and nearest monitor PM2.5 was available from five previous validation studies. Time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95 percent confidence intervals (95%CI) for each 10 μg/m(3) increase in PM2.5. Risk-set regression calibration was used to adjust estimates for measurement error.

RESULTS

Increasing exposure to PM2.5 was associated with an increased risk of mortality, and results were similar regardless of the method chosen for exposure assessment. Specifically, the multivariable adjusted HRs for each 10 μg/m(3) increase in 12-month average PM2.5 from spatio-temporal prediction models were 1.13 (95%CI:1.05, 1.22) and 1.12 (95%CI:1.05, 1.21) for concentrations at the nearest EPA monitoring location. Adjustment for measurement error increased the magnitude of the HRs 4-10% and led to wider CIs (HR = 1.18; 95%CI: 1.02, 1.36 for each 10 μg/m(3) increase in PM2.5 from the spatio-temporal models and HR = 1.22; 95%CI: 1.02, 1.45 from the nearest monitor estimates).

CONCLUSIONS

These findings support the large body of literature on the adverse effects of PM2.5, and suggest that adjustment for measurement error be considered in future studies where possible.

Marine ω-3 polyunsaturated fatty acids and risk of colorectal cancer according to microsatellite instability

BACKGROUND

Chronic inflammation is involved in the development of colorectal cancer (CRC) and microsatellite instability (MSI), a distinct phenotype of CRC. Experimental evidence indicates an anti-inflammatory and antineoplastic effect of marine ω-3 polyunsaturated fatty acids (PUFAs). However, epidemiologic data remain inconclusive.

METHODS

We investigated whether the association between marine ω-3 PUFAs and CRC varies by MSI-defined subtypes of tumors in the Nurses' Health Study and Health Professionals Follow-up Study. We documented and classified 1125 CRC cases into either MSI-high tumors, in which 30% or more of the 10 microsatellite markers demonstrated instability, or microsatellite-stable (MSS) tumors. Cox proportional hazards model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of MSS tumors and MSI-high tumors in relation to marine ω-3 PUFA intake. All statistical tests were two-sided.

RESULTS

Marine ω-3 PUFA intake was not associated with overall incidence of CRC. However, a statistically significant difference was detected by MSI status (P heterogeneity = .02): High marine ω-3 PUFA intake was associated with a lower risk of MSI-high tumors (comparing ≥0.30g/d with <0.10g/d: multivariable HR = 0.54, 95% CI = 0.35 to 0.83, P linearity = .03) but not MSS tumors (HR = 0.97, 95% CI = 0.78 to 1.20, P linearity = .28). This differential association appeared to be independent of CpG island methylator phenotype and BRAF mutation status.

CONCLUSIONS

High marine ω-3 PUFA intake is associated with lower risk of MSI-high CRC but not MSS tumors, suggesting a potential role of ω-3 PUFAs in protection against CRC through DNA mismatch repair. Further research is needed to confirm our findings and elucidate potential underlying mechanisms.

Proportional Hazards Model with Covariate Measurement Error and Instrumental Variables

In biomedical studies, covariates with measurement error may occur in survival data. Existing approaches mostly require certain replications on the error-contaminated covariates, which may not be available in the data. In this paper, we develop a simple nonparametric correction approach for estimation of the regression parameters in the proportional hazards model using a subset of the sample where instrumental variables are observed. The instrumental variables are related to the covariates through a general nonparametric model, and no distributional assumptions are placed on the error and the underlying true covariates. We further propose a novel generalized methods of moments nonparametric correction estimator to improve the efficiency over the simple correction approach. The efficiency gain can be substantial when the calibration subsample is small compared to the whole sample. The estimators are shown to be consistent and asymptotically normal. Performance of the estimators is evaluated via simulation studies and by an application to data from an HIV clinical trial. Estimation of the baseline hazard function is not addressed.

Covariate measurement error correction methods in mediation analysis with failure time data

Mediation analysis is important for understanding the mechanisms whereby one variable causes changes in another. Measurement error could obscure the ability of the potential mediator to explain such changes. This article focuses on developing correction methods for measurement error in the mediator with failure time outcomes. We consider a broad definition of measurement error, including technical error, and error associated with temporal variation. The underlying model with the "true" mediator is assumed to be of the Cox proportional hazards model form. The induced hazard ratio for the observed mediator no longer has a simple form independent of the baseline hazard function, due to the conditioning event. We propose a mean-variance regression calibration approach and a follow-up time regression calibration approach, to approximate the partial likelihood for the induced hazard function. Both methods demonstrate value in assessing mediation effects in simulation studies. These methods are generalized to multiple biomarkers and to both case-cohort and nested case-control sampling designs. We apply these correction methods to the Women's Health Initiative hormone therapy trials to understand the mediation effect of several serum sex hormone measures on the relationship between postmenopausal hormone therapy and breast cancer risk.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake

We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.

Adjusting for measurement error in baseline prognostic biomarkers included in a time-to-event analysis: a joint modelling approach

Background

Methodological development of joint models of longitudinal and survival data has been rapid in recent years; however, their full potential in applied settings are yet to be fully explored. We describe a novel use of a specific association structure, linking the two component models through the subject specific intercept, and thus extend joint models to account for measurement error in a biomarker, even when only the baseline value of the biomarker is of interest. This is a common occurrence in registry data sources, where often repeated measurements exist but are simply ignored.

Methods

The proposed specification is evaluated through simulation and applied to data from the General Practice Research Database, investigating the association between baseline Systolic Blood Pressure (SBP) and the time-to-stroke in a cohort of obese patients with type 2 diabetes mellitus.

Results

By directly modelling the longitudinal component we reduce bias in the hazard ratio for the effect of baseline SBP on the time-to-stroke, showing the large potential to improve on previous prognostic models which use only observed baseline biomarker values.

Conclusions

The joint modelling of longitudinal and survival data is a valid approach to account for measurement error in the analysis of a repeatedly measured biomarker and a time-to-event. User friendly Stata software is provided.

Physical activity and risk of inflammatory bowel disease: prospective study from the Nurses' Health Study cohorts

OBJECTIVE

To examine the association between physical activity and risk of ulcerative colitis and Crohn's disease.

DESIGN

Prospective cohort study.

SETTING

Nurses' Health Study and Nurses' Health Study II.

PARTICIPANTS

194,711 women enrolled in the Nurses' Health Study and Nurses' Health Study II who provided data on physical activity and other risk factors every two to four years since 1984 in the Nurses' Health Study and 1989 in the Nurses' Health Study II and followed up through 2010.

MAIN OUTCOME MEASURE

Incident ulcerative colitis and Crohn's disease.

RESULTS

During 3,421,972 person years of follow-up, we documented 284 cases of Crohn's disease and 363 cases of ulcerative colitis. The risk of Crohn's disease was inversely associated with physical activity (P for trend 0.02). Compared with women in the lowest fifth of physical activity, the multivariate adjusted hazard ratio of Crohn's disease among women in the highest fifth of physical activity was 0.64 (95% confidence interval 0.44 to 0.94). Active women with at least 27 metabolic equivalent task (MET) hours per week of physical activity had a 44% reduction (hazard ratio 0.56, 95% confidence interval 0.37 to 0.84) in risk of developing Crohn's disease compared with sedentary women with <3 MET h/wk. Physical activity was not associated with risk of ulcerative colitis (P for trend 0.46). The absolute risk of ulcerative colitis and Crohn's disease among women in the highest fifth of physical activity was 8 and 6 events per 100,000 person years compared with 11 and 16 events per 100,000 person years among women in the lowest fifth of physical activity, respectively. Age, smoking, body mass index, and cohort did not significantly modify the association between physical activity and risk of ulcerative colitis or Crohn's disease (all P for interaction >0.35).

CONCLUSION

In two large prospective cohorts of US women, physical activity was inversely associated with risk of Crohn's disease but not of ulcerative colitis.

Predicted plasma 25-hydroxyvitamin D and risk of renal cell cancer

BACKGROUND

Although the kidney is a primary organ for vitamin D metabolism, the association between vitamin D and renal cell cancer (RCC) remains unclear.

METHODS

We prospectively evaluated the association between predicted plasma 25-hydroxyvitamin D [25(OH)D] and RCC risk among 72,051 women and 46,380 men in the period from 1986 to 2008. Predicted plasma 25(OH)D scores were computed using validated regression models that included major determinants of vitamin D status (race, ultraviolet B flux, physical activity, body mass index, estimated vitamin D intake, alcohol consumption, and postmenopausal hormone use in women). Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. All statistical tests were two-sided.

RESULTS

During 22 years of follow-up, we documented 201 cases of incident RCC in women and 207 cases in men. The multivariable hazard ratios between extreme quintiles of predicted 25(OH)D score were 0.50 (95% CI = 0.32 to 0.80) in women, 0.59 (95% CI = 0.37 to 0.94) in men, and 0.54 (95% CI = 0.39 to 0.75; P trend < .001) in the pooled cohorts. An increment of 10 ng/mL in predicted 25(OH)D score was associated with a 44% lower incidence of RCC (pooled HR = 0.56, 95% CI = 0.42 to 0.74). We found no statistically significant association between vitamin D intake estimated from food-frequency questionnaires and RCC incidence.

CONCLUSION

Higher predicted plasma 25(OH)D levels were associated with a statistically significantly lower risk of RCC in men and women. Our findings need to be confirmed by other prospective studies using valid markers of long-term vitamin D status.

Bias Correction Methods for Misclassified Covariates in the Cox Model: comparison offive correction methods by simulation and data analysis

Measurement error/misclassification is commonplace in research when variable(s) can notbe measured accurately. A number of statistical methods have been developed to tackle this problemin a variety of settings and contexts. However, relatively few methods are available to handlemisclassified categorical exposure variable(s) in the Cox proportional hazards regression model. Inthis paper, we aim to review and compare different methods to handle this problem - naïvemethods, regression calibration, pooled estimation, multiple imputation, corrected score estimation,and MC-SIMEX - by simulation. These methods are also applied to a life course study with recalleddata and historical records. In practice, the issue of measurement error/misclassification should beaccounted for in design and analysis, whenever possible. Also, in the analysis, it could be moreideal to implement more than one correction method for estimation and inference, with properunderstanding of underlying assumptions.

Measurement Error Case Series Models with Application to Infection-Cardiovascular Risk in OlderPatients on Dialysis

Infection and cardiovascular disease are leading causes of hospitalization and death in older patients on dialysis. Our recent work found an increase in the relative incidence of cardiovascular outcomes during the ~ 30 days after infection-related hospitalizations using the case series model, which adjusts for measured and unmeasured baseline confounders. However, a major challenge in modeling/assessing the infection-cardiovascular risk hypothesis is that the exact time of infection, or more generally "exposure," onsets cannot be ascertained based on hospitalization data. Only imprecise markers of the timing of infection onsets are available. Although there is a large literature on measurement error in the predictors in regression modeling, to date there is no work on measurement error on the timing of a time-varying exposure to our knowledge. Thus, we propose a new method, the measurement error case series (MECS) models, to account for measurement error in time-varying exposure onsets. We characterized the general nature of bias resulting from estimation that ignores measurement error and proposed a bias-corrected estimation for the MECS models. We examined in detail the accuracy of the proposed method to estimate the relative incidence. Hospitalization data from United States Renal Data System, which captures nearly all (> 99%) patients with end-stage renal disease in the U.S. over time, is used to illustrate the proposed method. The results suggest that the estimate of the cardiovascular incidence following the 30 days after infections, a period where acute effects of infection on vascular endothelium may be most pronounced, is substantially attenuated in the presence of infection onset measurement error.

Correcting for exposure misclassification using survival analysis with a time-varying exposure

PURPOSE

Survival analysis is increasingly being used in perinatal epidemiology to assess time-varying risk factors for various pregnancy outcomes. Here we show how quantitative correction for exposure misclassification can be applied to a Cox regression model with a time-varying dichotomous exposure.

METHODS

We evaluated influenza vaccination during pregnancy in relation to preterm birth among 2267 non-malformed infants whose mothers were interviewed as part of the Slone Birth Defects Study during 2006 through 2011. The hazard of preterm birth was modeled using a time-varying exposure Cox regression model with gestational age as the time-scale. The effect of exposure misclassification was then modeled using a probabilistic bias analysis that incorporated vaccination date assignment. The parameters for the bias analysis were derived from both internal and external validation data.

RESULTS

Correction for misclassification of prenatal influenza vaccination resulted in an adjusted hazard ratio (AHR) slightly higher and less precise than the conventional analysis: Bias-corrected AHR 1.04 (95% simulation interval, 0.70-1.52); conventional AHR, 1.00 (95% confidence interval, 0.71-1.41).

CONCLUSIONS

Probabilistic bias analysis allows epidemiologists to assess quantitatively the possible confounder-adjusted effect of misclassification of a time-varying exposure, in contrast with a speculative approach to understanding information bias.

Hazard ratio estimation for biomarker-calibrated dietary exposures

Uncertainty concerning the measurement error properties of self-reported diet has important implications for the reliability of nutritional epidemiology reports. Biomarkers based on the urinary recovery of expended nutrients can provide an objective measure of short-term nutrient consumption for certain nutrients and, when applied to a subset of a study cohort, can be used to calibrate corresponding self-report nutrient consumption assessments. A nonstandard measurement error model that makes provision for systematic error and subject-specific error, along with the usual independent random error, is needed for the self-report data. Three estimation procedures for hazard ratio (Cox model) parameters are extended for application to this more complex measurement error structure. These procedures are risk set regression calibration, conditional score, and nonparametric corrected score. An estimator for the cumulative baseline hazard function is also provided. The performance of each method is assessed in a simulation study. The methods are then applied to an example from the Women's Health Initiative Dietary Modification Trial.

Low-carbohydrate diet scores and risk of type 2 diabetes in men

BACKGROUND

Fat and protein sources may influence whether low-carbohydrate diets are associated with type 2 diabetes (T2D).

OBJECTIVE

The objective was to compare the associations of 3 low-carbohydrate diet scores with incident T2D.

DESIGN

A prospective cohort study was conducted in participants from the Health Professionals Follow-Up Study who were free of T2D, cardiovascular disease, or cancer at baseline (n = 40,475) for up to 20 y. Cumulative averages of 3 low-carbohydrate diet scores (high total protein and fat, high animal protein and fat, and high vegetable protein and fat) were calculated every 4 y from food-frequency questionnaires and were associated with incident T2D by using Cox models.

RESULTS

We documented 2689 cases of T2D during follow-up. After adjustments for age, smoking, physical activity, coffee intake, alcohol intake, family history of T2D, total energy intake, and body mass index, the score for high animal protein and fat was associated with an increased risk of T2D [top compared with bottom quintile; hazard ratio (HR): 1.37; 95% CI: 1.20, 1.58; P for trend < 0.01]. Adjustment for red and processed meat attenuated this association (HR: 1.11; 95% CI: 0.95, 1.30; P for trend = 0.20). A high score for vegetable protein and fat was not significantly associated with the risk of T2D overall but was inversely associated with T2D in men aged <65 y (HR: 0.78; 95% CI: 0.66, 0.92; P for trend = 0.01, P for interaction = 0.01).

CONCLUSIONS

A score representing a low-carbohydrate diet high in animal protein and fat was positively associated with the risk of T2D in men. Low-carbohydrate diets should obtain protein and fat from foods other than red and processed meat.