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Missing data in covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often applied to regression analysis with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood‐based estimators, but in general are more robust against model misspecification. In this article, we propose a robust best linear weighted estimator for Cox regression with missing covariates. Our proposed estimator is the projection of the simple inverse probability weighted estimator onto the orthogonal complement of the score space based on a working regression model of the observed data. The efficiency gain is from the use of the association between the survival outcome variable and the available covariates, which is the working regression model. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a colorectal cancer study to assess the association of the microsatellite instability status with colorectal cancer‐specific mortality. [ABSTRACT FROM AUTHOR]

BackgroundAlthough epidemiologic studies have shown associations between sedentary behavior and mortality, few have focused on older women with adequate minority representation and few have controlled for both physical activity and functional status.PurposeThe objective of this study was to determine the relationship between sedentary time and total; cardiovascular disease (CVD); coronary heart disease (CHD); and cancer mortality in a prospective, multiethnic cohort of postmenopausal women.MethodsThe study population included 92,234 women aged 50-79 years at baseline (1993-1998) who participated in the Women's Health Initiative Observational Study through September 2010. Self-reported sedentary time was assessed by questionnaire and examined in 4 categories (≤4, >4-8, ≥8-11, >11 hours). Mortality risks were examined using Cox proportional hazard models adjusting for confounders. Models were also stratified by age, race/ethnicity, body mass index, physical activity, physical function, and chronic disease to examine possible effect modification. Analyses were conducted in 2012-2013.ResultsThe mean follow-up period was 12 years. Compared with women who reported the least sedentary time, women reporting the highest sedentary time had increased risk of all-cause mortality in the multivariate model (HR=1.12, 95% CI=1.05, 1.21). Results comparing the highest versus lowest categories for CVD, CHD, and cancer mortality were as follows: HR=1.13, 95% CI=0.99, 1.29; HR=1.27, 95% CI=1.04, 1.55; and HR=1.21, 95% CI=1.07, 1.37, respectively. For all mortality outcomes, there were significant linear tests for trend.ConclusionsThere was a linear relationship between greater amounts of sedentary time and mortality risk after controlling for multiple potential confounders.

This article discusses the cutaneous manifestation of dermatomyositis (DM) and its potential to be misdiagnosed as an allergic rash, which can delay the diagnosis of malignancy. DM is a rare inflammatory myopathy characterized by unique skin rash patterns and muscle inflammation. The incidence of DM is relatively low, and it is often associated with comorbid cancers. The article presents three cases of cancer-associated DM that were initially misdiagnosed as other dermatopathies. The authors emphasize the importance of recognizing specific DM criteria, such as Heliotrope rash and Gottron's papules, to prompt further investigation for cancer. They also highlight the challenges in diagnosing DM, especially in cases with minimal muscle involvement, and the need for comprehensive evaluations to identify cancer-associated myositis. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]