Efficient estimation for left-truncated competing risks regression for case-cohort studies.

The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.

A case-cohort study of per- and polyfluoroalkyl substance concentrations and incident prostate cancer in the cancer prevention Study-II LifeLink cohort study.

INTRODUCTION: Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent, potentially carcinogenic chemicals. Previous studies investigating PFAS exposure and prostate cancer yielded mixed findings. We aimed to investigate associations between PFAS exposure and incident prostate cancer in a large cohort of U.S. men, overall and by selected demographic, lifestyle, and medical-related characteristics. METHODS: We conducted a case-cohort study among Cancer Prevention Study-II LifeLink Cohort participants who, at baseline (1998-2001), had serum specimens collected and no prior cancer diagnosis. The study included all men diagnosed with prostate cancer (n = 1610) during follow-up (baseline-June 30, 2015) and a random sub-cohort of 500 men. PFAS concentrations [perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), and perfluorooctanoic acid (PFOA)] were measured in stored serum specimens. We used multivariable Cox proportional hazards models to estimate associations between PFAS concentrations and prostate cancer, overall and by selected characteristics (grade, stage, family history, age, education, smoking status, and alcohol consumption). RESULTS: Prostate cancer hazards were slightly higher among men with concentrations in the highest (Q4) vs lowest quartile (Q1) for PFHxS [hazard ratio (HR) (95% CI): 1.18 (0.88-1.59)] and PFOS [HR (95% CI): 1.18 (0.89-1.58)], but not for PFNA or PFOA. However, we observed heterogeneous associations by age, family history of prostate cancer (PFHxS), alcohol consumption (PFHxS), and education (PFNA). For example, no meaningful associations were observed among men aged <70 years at serum collection, but among men aged ≥70 years, HRs (95% CIs) comparing Q4 to Q1 were PFHxS 1.54 (1.02-2.31) and PFOS 1.62 (1.08-2.44). No meaningful heterogeneity in associations were observed by tumor grade or stage. CONCLUSIONS: Our findings do not clearly support an association between the PFAS considered and prostate cancer. However, positive associations observed in some subgroups, and consistently positive associations observed for PFHxS warrant further investigation.

Assessing risk model calibration with missing covariates.

When validating a risk model in an independent cohort, some predictors may be missing for some subjects. Missingness can be unplanned or by design, as in case-cohort or nested case-control studies, in which some covariates are measured only in subsampled subjects. Weighting methods and imputation are used to handle missing data. We propose methods to increase the efficiency of weighting to assess calibration of a risk model (i.e. bias in model predictions), which is quantified by the ratio of the number of observed events, $\mathcal{O}$, to expected events, $\mathcal{E}$, computed from the model. We adjust known inverse probability weights by incorporating auxiliary information available for all cohort members. We use survey calibration that requires the weighted sum of the auxiliary statistics in the complete data subset to equal their sum in the full cohort. We show that a pseudo-risk estimate that approximates the actual risk value but uses only variables available for the entire cohort is an excellent auxiliary statistic to estimate $\mathcal{E}$. We derive analytic variance formulas for $\mathcal{O}/\mathcal{E}$ with adjusted weights. In simulations, weight adjustment with pseudo-risk was much more efficient than inverse probability weighting and yielded consistent estimates even when the pseudo-risk was a poor approximation. Multiple imputation was often efficient but yielded biased estimates when the imputation model was misspecified. Using these methods, we assessed calibration of an absolute risk model for second primary thyroid cancer in an independent cohort.

Associations of mitochondrial DNA copy number with incident risks of gastrointestinal cancers: A prospective case-cohort study.

Epidemiological investigations implied that mitochondrial DNA copy number (mtDNAcn) variations could trigger predisposition to multiple cancers, but evidence regarding gastrointestinal cancers (GICs) was still uncertain. We conducted a case-cohort study within the prospective Dongfeng-Tongji cohort, including incident cases of colorectal cancer (CRC, n = 278), gastric cancer (GC, n = 138), and esophageal cancer (EC, n = 72) as well as a random subcohort (n = 1173), who were followed up from baseline to the end of 2018. We determined baseline blood mtDNAcn and associations of mtDNAcn with the GICs risks were estimated by using weighted Cox proportional hazards models. Significant U-shaped associations were observed between mtDNAcn and GICs risks. Compared to subjects within the second quartile (Q2) mtDNAcn subgroup, those within the 1st (Q1), 3rd (Q3), and 4th (Q4) quartile subgroups showed increased risks of CRC (hazard ratio [HR] [95% confidence interval, CI] = 2.27 [1.47-3.52], 1.65 [1.04-2.62], and 2.81 [1.85-4.28], respectively) and total GICs (HR [95%CI] = 1.84 [1.30-2.60], 1.47 [1.03-2.10], and 2.51 [1.82-3.47], respectively], and those within Q4 subgroup presented elevated GC and EC risks (HR [95% CI] = 2.16 [1.31-3.54] and 2.38 [1.13-5.02], respectively). Similar associations of mtDNAcn with CRC and total GICs risks remained in stratified analyzes by age, gender, smoking, and drinking status. This prospective case-cohort study showed U-shaped associations between mtDNAcn and GICs risks, but further research works are needed to uncover underlying biological mechanisms.

Polygenic liability, stressful life events and risk for secondary-treated depression in early life: a nationwide register-based case-cohort study.

BACKGROUND: In this study, we examined the relationship between polygenic liability for depression and number of stressful life events (SLEs) as risk factors for early-onset depression treated in inpatient, outpatient or emergency room settings at psychiatric hospitals in Denmark. METHODS: Data were drawn from the iPSYCH2012 case-cohort sample, a population-based sample of individuals born in Denmark between 1981 and 2005. The sample included 18 532 individuals who were diagnosed with depression by a psychiatrist by age 31 years, and a comparison group of 20 184 individuals. Information on SLEs was obtained from nationwide registers and operationalized as a time-varying count variable. Hazard ratios and cumulative incidence rates were estimated using Cox regressions. RESULTS: Risk for depression increased by 35% with each standard deviation increase in polygenic liability (p < 0.0001), and 36% (p < 0.0001) with each additional SLE. There was a small interaction between polygenic liability and SLEs (ß = -0.04, p = 0.0009). The probability of being diagnosed with depression in a hospital-based setting between ages 15 and 31 years ranged from 1.5% among males in the lowest quartile of polygenic liability with 0 events by age 15, to 18.8% among females in the highest quartile of polygenic liability with 4+ events by age 15. CONCLUSIONS: These findings suggest that although there is minimal interaction between polygenic liability and SLEs as risk factors for hospital-treated depression, combining information on these two important risk factors could potentially be useful for identifying high-risk individuals.

Additive subdistribution hazards regression for competing risks data in case-cohort studies.

In survival data analysis, a competing risk is an event whose occurrence precludes or alters the chance of the occurrence of the primary event of interest. In large cohort studies with long-term follow-up, there are often competing risks. Further, if the event of interest is rare in such large studies, the case-cohort study design is widely used to reduce the cost and achieve the same efficiency as a cohort study. The conventional additive hazards modeling for competing risks data in case-cohort studies involves the cause-specific hazard function, under which direct assessment of covariate effects on the cumulative incidence function, or the subdistribution, is not possible. In this paper, we consider an additive hazard model for the subdistribution of a competing risk in case-cohort studies. We propose estimating equations based on inverse probability weighting methods for the estimation of the model parameters. Consistency and asymptotic normality of the proposed estimators are established. The performance of the proposed methods in finite samples is examined through simulation studies and the proposed approach is applied to a case-cohort dataset from the Sister Study.

On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint.

BACKGROUND: Case-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis when there is also unintended missingness. Multiple imputation (MI) has become a default standard for handling unintended missingness and is typically used in combination with IPW to handle the intended missingness due to the case-control sampling. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of an MI-only approach has been investigated in the context of a case-cohort study with a time-to-event outcome, it is unclear how this approach performs with a binary outcome. METHODS: We conducted a simulation study to assess and compare the performance of approaches using only MI, only IPW, and a combination of MI and IPW, for handling intended and unintended missingness in the case-cohort setting. We also applied the approaches to a case study. RESULTS: Our results show that the combined approach is approximately unbiased for estimation of the exposure effect when the sample size is large, and was the least biased with small sample sizes, while MI-only and IPW-only exhibited larger biases in both sample size settings. CONCLUSIONS: These findings suggest that a combined MI/IPW approach should be preferred to handle intended and unintended missing data in case-cohort studies with binary outcomes.

Risk Ratio and Risk Difference Estimation in Case-cohort Studies.

BACKGROUND: In case-cohort studies with binary outcomes, ordinary logistic regression analyses have been widely used because of their computational simplicity. However, the resultant odds ratio estimates cannot be interpreted as relative risk measures unless the event rate is low. The risk ratio and risk difference are more favorable outcome measures that are directly interpreted as effect measures without the rare disease assumption. METHODS: We provide pseudo-Poisson and pseudo-normal linear regression methods for estimating risk ratios and risk differences in analyses of case-cohort studies. These multivariate regression models are fitted by weighting the inverses of sampling probabilities. Also, the precisions of the risk ratio and risk difference estimators can be improved using auxiliary variable information, specifically by adapting the calibrated or estimated weights, which are readily measured on all samples from the whole cohort. Finally, we provide computational code in R (R Foundation for Statistical Computing, Vienna, Austria) that can easily perform these methods. RESULTS: Through numerical analyses of artificially simulated data and the National Wilms Tumor Study data, accurate risk ratio and risk difference estimates were obtained using the pseudo-Poisson and pseudo-normal linear regression methods. Also, using the auxiliary variable information from the whole cohort, precisions of these estimators were markedly improved. CONCLUSION: The ordinary logistic regression analyses may provide uninterpretable effect measure estimates, and the risk ratio and risk difference estimation methods are effective alternative approaches for case-cohort studies. These methods are especially recommended under situations in which the event rate is not low.

Genetic and psychosocial influence on the association between early childhood infections and later psychiatric disorders.

OBJECTIVE: To evaluate the influence of extensive genetic and psychosocial confounding on the association between early childhood infection and five major psychiatric disorders METHODS: A case-cohort study including participants from the Danish iPSYCH2012 sample, a case-cohort sample where all cases born between May 1, 1981, and December 31, 2005, diagnosed with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar affective disorder (BIP), Major Depressive Disorder (MDD) or schizophrenia (SCZ), were identified and pooled with a representative sample (subcohort) of the Danish population. We used Cox proportional hazards regression customized to the case-cohort setup to calculate hazard ratios of outcome with 95% confidence intervals (CIs), following exposure to early childhood infection before the age of 5 years for ADHD and ASD, and before the age of 10 years for BIP, MDD, and SCZ. To evaluate psychosocial confounding we included sex, calendar period, sibling infections, urbanicity, parental socio-economic status, parental mental health information, and polygenic risk scores for all five disorders, as covariates. To estimate how liability for psychiatric disorders measured through the PRS influenced the risk of early childhood infection, we calculated odds ratios (ORs) with 95% CIs, using logistic regression RESULTS: Early childhood infection was associated with ADHD, ASD, MDD, and SCZ with number of childhood infections increasing the hazard. The HR was still significant in the model with full adjustments after 1 infection for ADHD (HR 1.29, 95% CI: 1.19-1.41), ASD (HR 1.28, 95% CI: 1.18-1.40), MDD (HR 1.23, 95% CI: 1.14-1.33), and SCZ (HR 1.21, 95% CI: 1.07-1.36), but not for BIP (HR1.17, 95% CI: 0.96-1.42). Probands exposed to sibling infections, but not own infection had an absolute risk of ADHD, BIP, MDD, and SCZ that closely approached the absolute risk for individuals exposed to own infections. We found evidence of gene-environment correlation with higher PRS of MDD and to some extent SCZ increasing the risk of infections and higher PRS of BIP associated with significantly decreased risk CONCLUSION: Early childhood infection is significantly associated with ADHD, ASD, MDD, and SCZ and not explained by genetic or psychosocial confounding. Although we found evidence of gene-environment correlation, it had minor impact on the results.

Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome.

BACKGROUND: In case-cohort studies a random subcohort is selected from the inception cohort and acts as the sample of controls for several outcome investigations. Analysis is conducted using only the cases and the subcohort, with inverse probability weighting (IPW) used to account for the unequal sampling probabilities resulting from the study design. Like all epidemiological studies, case-cohort studies are susceptible to missing data. Multiple imputation (MI) has become increasingly popular for addressing missing data in epidemiological studies. It is currently unclear how best to incorporate the weights from a case-cohort analysis in MI procedures used to address missing covariate data. METHOD: A simulation study was conducted with missingness in two covariates, motivated by a case study within the Barwon Infant Study. MI methods considered were: using the outcome, a proxy for weights in the simple case-cohort design considered, as a predictor in the imputation model, with and without exposure and covariate interactions; imputing separately within each weight category; and using a weighted imputation model. These methods were compared to a complete case analysis (CCA) within the context of a standard IPW analysis model estimating either the risk or odds ratio. The strength of associations, missing data mechanism, proportion of observations with incomplete covariate data, and subcohort selection probability varied across the simulation scenarios. Methods were also applied to the case study. RESULTS: There was similar performance in terms of relative bias and precision with all MI methods across the scenarios considered, with expected improvements compared with the CCA. Slight underestimation of the standard error was seen throughout but the nominal level of coverage (95%) was generally achieved. All MI methods showed a similar increase in precision as the subcohort selection probability increased, irrespective of the scenario. A similar pattern of results was seen in the case study. CONCLUSIONS: How weights were incorporated into the imputation model had minimal effect on the performance of MI; this may be due to case-cohort studies only having two weight categories. In this context, inclusion of the outcome in the imputation model was sufficient to account for the unequal sampling probabilities in the analysis model.

The blood microbiome and its association to cardiovascular disease mortality: case-cohort study.

BACKGROUND: Little is known about the association between bacterial DNA in human blood and the risk of cardiovascular disease (CVD) mortality. METHODS: A case-cohort study was performed based on a 9 ½ year follow-up of the Oslo II study from 2000. Eligible for this analysis were men born in 1923 and from 1926 to 1932. The cases were men (n = 227) who had died from CVD, and the controls were randomly selected participants from the same cohort (n = 178). Analysis of the bacterial microbiome was performed on stored frozen blood samples for both cases and controls. Association analyses for CVD mortality were performed by Cox proportional hazard regression adapted to the case-cohort design. We used the Bonferroni correction due to the many bacterial genera that were identified. RESULTS: Bacterial DNA was identified in 372 (82%) of the blood samples and included 78 bacterial genera from six phyla. Three genera were significantly associated with CVD mortality. The genera Kocuria (adjusted hazard ratio (HR) 8.50, 95% confidence interval (CI) (4.05, 17.84)) and Enhydrobacter (HR 3.30 (2.01, 5.57)) indicate an association with CVD mortality with increasing levels. The genera Paracoccus (HR 0.29 (0.15, 0.57)) was inversely related. Significant predictors of CVD mortality were: the feeling of bad health; and the consumption of more than three cups of coffee per day. The following registered factors were borderline significant, namely: a history of heart failure; increased systolic blood pressure; and currently taking antihypertensive drugs now, versus previously. CONCLUSIONS: The increasing levels of two bacterial genera Kocuria (skin and oral) and Enhydrobacter (skin) and low levels of Paracoccus (soil) were associated with CVD mortality independent of known risk factors for CVD.

Estimating the hazard rate difference from case-cohort studies.

The case-cohort design, among many two-phase sampling designs, substantially reduces the cost of an epidemiological study by selecting more informative participants within the full cohort for expensive variable measurements. Despite their benefits, additive hazards models, which estimate hazard differences, have rarely been used for the analysis of case-cohort studies due to the lack of software and application examples. In this paper, we describe a newly developed estimation method that fits the additive hazards models to general two-phase sampling studies along with the R package addhazard that implements it. It allows for missing covariates among cases, cohort stratification, robust variances, and the incorporation of auxiliary information from the full cohort to enhance inference precision. We demonstrate the use of this tool to estimate the association of the risk of coronary heart disease (CHD) with biomarkers high-sensitivity C-reactive protein (hs-CRP) and Lipoprotein-associated phospholipase A2 (Lp-PLA2) by analyzing the Atherosclerosis Risk in Communities Study, which adopted a two-phase sampling design for studying these two biomarkers. We show that the use of auxiliary variables from the full cohort based on calibration techniques improves the precision of the hazard difference being estimated. We observe a synergistic effect of the two biomarkers among participants with lower LDL cholesterol (LDL-C): the CHD hazard rate attributable to the combined action of high hs-CRP and high Lp-PLA2 exceeded the sum of the CHD hazard rate attributable to each one independently by 11.58 (95% CI 2.16-21.01) cases per 1000 person-years. With higher LDL-C, we observe the CHD hazard rate attributable to the combined action of high hs-CRP and medium Lp-PLA2 was less than the sum of their individual effects by 13.42 (95% CI 2.44-24.40) cases per 1000 person-years. This demonstration serves the dual purposes of illustrating analysis techniques and providing insights about the utility of hs-CRP and Lp-PLA2 for identifying the high-risk population of CHD that the traditional risk factors such as the LDL-C may miss. Epidemiologists are encouraged to use this new tool to analyze other case-cohort studies and incorporate auxiliary variables embedded in the full cohort in their analysis.

Urinary cadmium and stroke - a case-cohort study in Danish never-smokers.

BACKGROUND: and Purpose: Cadmium has been associated with risk of cardiovascular events, including stroke. Human cadmium exposure occurs primarily through diet and tobacco smoke. Recent cohort studies have found an association with stroke, but residual confounding from smoking, could not be ruled out. We therefore conducted a case-cohort study to evaluate whether cadmium is associated with stroke in never-smokers. METHODS: The Danish Diet Cancer and Health cohort consists of Danes 50-64 years old, recruited in 1993-1997. From never-smoking cohort members without previous cancer or stroke we sampled a sub-cohort of 1200 persons. We also identified all (n = 534) cases in the cohort with a validated stroke diagnosis between baseline and 2009. We quantified cadmium and creatinine concentrations from baseline urine samples and used cadmium per creatinine as our main exposure metric. We used Cox proportional hazards models to estimate hazard ratios (HRs) with age as time scale and adjusting for BMI, education and urinary cotinine with and without stratification by sex. RESULTS: The median urinary cadmium concentration was 0.21 µg cadmium/g creatinine in cases and 0.19 µg/g in the sub-cohort. The majority (83%) of stroke cases were diagnosed with ischemic stroke. The HR for stroke in the highest quartile of exposure (median 0.44 µg/g creatinine) was 1.11 (95% CI: 0.79-1.54) compared with the lowest quartile (median 0.10 µg/g creatinine). The HR per inter quartile range (IQR, 0.19 µg/g creatinine) was 1.02 (95% CI: 0.92-1.12). Among men, the HR per IQR higher levels of cadmium (0.16 µg/g creatinine) was 1.18 (95% CI: 0.92-1.52), and 1.00 (95% CI: 0.89-1.12) among women. Adjusting for creatinine or using osmolality instead of creatinine standardization generally attenuated observed relationships. CONCLUSIONS: Our results do not support that low levels of cadmium exposure among never-smokers are strongly associated with risk of stroke, although results varied somewhat by sex and method of accounting for urinary dilution.

Biomarker-defined pathways for incident type 2 diabetes and coronary heart disease-a comparison in the MONICA/KORA study.

BACKGROUND: Biomarkers may contribute to our understanding of the pathophysiology of various diseases. Type 2 diabetes (T2D) and coronary heart disease (CHD) share many clinical and lifestyle risk factors and several biomarkers are associated with both diseases. The current analysis aims to assess the relevance of biomarkers combined to pathway groups for the development of T2D and CHD in the same cohort. METHODS: Forty-seven serum biomarkers were measured in the MONICA/KORA case-cohort study using clinical chemistry assays and ultrasensitive molecular counting technology. The T2D (CHD) analyses included 689 (568) incident cases and 1850 (2004) non-cases from three population-based surveys. At baseline, the study participants were 35-74 years old. The median follow-up was 14 years. We computed Cox regression models for each biomarker, adjusted for age, sex, and survey. Additionally, we assigned the biomarkers to 19 etiological pathways based on information from literature. One age-, sex-, and survey-controlled average variable was built for each pathway. We used the R2PM coefficient of determination to assess the explained disease risk. RESULTS: The associations of many biomarkers, such as several cytokines or the iron marker soluble transferrin receptor (sTfR), were similar in strength for T2D and CHD, but we also observed important differences. Lipoprotein (a) (Lp(a)) and N-terminal pro B-type natriuretic peptide (NT-proBNP) even demonstrated opposite effect directions. All pathway variables together explained 49% of the T2D risk and 21% of the CHD risk. The insulin-like growth factor binding protein 2 (IGFBP-2, IGF/IGFBP system pathway) best explained the T2D risk (about 9% explained risk, independent of all other pathway variables). For CHD, the myocardial-injury- and lipid-related-pathways were most important and both explained about 4% of the CHD risk. CONCLUSIONS: The biomarker-derived pathway variables explained a higher proportion of the T2D risk compared to CHD. The ranking of the pathways differed between the two diseases, with the IGF/IGFBP-system-pathway being most strongly associated with T2D and the myocardial-injury- and lipid-related-pathways with CHD. Our results help to better understand the pathophysiology of the two diseases, with the ultimate goal of pointing out targets for lifestyle intervention and drug development to ideally prevent both T2D and CHD development.

Adipose tissue content of alpha-linolenic acid and development of peripheral artery disease: a Danish case-cohort study.

PURPOSE: The aim of this study was to investigate the association between adipose tissue content of the plant-derived n-3 fatty acid, alpha-linolenic acid, and the rate of incident peripheral artery disease (PAD). METHODS: We conducted a case-cohort study nested within the Danish Diet, Cancer and Health cohort (n = 57,053), which was established between 1993 and 1997. Potential PAD cases were identified using linkage with The Danish National Patient Register and all potential cases were validated. Adipose tissue samples from the buttock were collected at baseline and fatty acid composition was determined in cases and in a random sample (n = 3500) from the cohort by gas chromatography. Statistical analyses were performed using weighted Cox regression allowing for different baseline hazards among sexes. RESULTS: During a median of 13.5 years of follow-up, we identified 863 PAD cases with complete information. The median adipose tissue content of ALA in the sub-cohort (n = 3197) was 0.84% (interquartile range 0.73-0.94%) of total fatty acids. In multivariate analyses including adjustment for established risk factors, we observed a U-shaped association between ALA in adipose tissue and rate of PAD, but the association was not statistically significant (P = 0.131). Similar pattern of associations were observed between ALA content in adipose tissue and the rate of PAD among men and women. CONCLUSIONS: We found indications of a U-shaped association between adipose tissue content of ALA and the rate of PAD, but the association was not statistically significant.

Benzene exposure-response and risk of lymphoid neoplasms in Chinese workers: A multicenter case-cohort study.

BACKGROUND: While international agreement supports a causal relationship of benzene exposure with acute myeloid leukemia, there is debate about benzene and lymphoid neoplasm risks. METHODS: In a case-cohort study with follow-up of 110 631 Chinese workers during 1972-1999, we evaluated benzene exposure-response for non-Hodgkin lymphoma (NHL), lymphoid leukemias (LL), acute lymphocytic leukemia (ALL), and total lymphoid neoplasms (LN). We estimated benzene exposures using state-of-the-art hierarchical modeling of occupational factors calibrated with historical routine measurements and evaluated cumulative exposure-response using Cox regression. RESULTS: NHL and other specified LN were increased in exposed vs unexposed workers. However, there was no evidence of exposure-response for NHL or other specified LN. Based on a linear exposure-response, relative risks at 100 parts per million-years (RR at 100 ppm-years) for cumulative benzene exposure using a 2-year lag (exposure at least 2 years before the time at risk) were 1.05 for NHL (95 percent confidence interval (CI) = 0.97, 1.27; 32 cases), 1.11 for LL (95% CI < 0, 1.66; 12 cases), 1.21 for ALL (95% CI < 0, 3.53; 10 cases), and 1.02 for total LN (95% CI < 0, 1.16; 49 cases). No statistically significant exposure-response trends were apparent for these LN for 2 to <10-year or ≥10-year lags. NHL risks were not significantly modified by sex, age, or year at first exposure, attained age, or time since exposure. CONCLUSION: Given the study strengths and limitations, we found little evidence of exposure-response for benzene and NHL, LL, ALL, or total LN, although NHL and other specified LN were increased in exposed vs unexposed individuals.

Cumulative risk regression in case-cohort studies using pseudo-observations.

Case-cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case-cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case-cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case-cohort survival data. We adjust for the case-cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.

Indirect Effects of Pneumococcal Childhood Vaccination in Individuals Treated With Immunosuppressive Drugs in Ambulatory Care: A Case-cohort Study.

BACKGROUND: The extent to which iatrogenically-immunosuppressed individuals benefit from indirect effects of childhood vaccination with pneumococcal conjugate vaccines (PCVs) is unknown. We determined how the sequential introduction of PCV7 (2006) and PCV13 (2011) in the Norwegian childhood vaccination program has affected the epidemiology of invasive pneumococcal disease (IPD) in individuals treated with immunosuppressants in ambulatory care. METHODS: We conducted a case-cohort study comprising 7926 IPD cases reported to the Norwegian Surveillance System for Communicable Diseases in 2005-2014 and 249998 individuals randomly selected from the National Registry in 2012. We defined immunosuppressive treatment groups based on dispensed prescriptions retrieved from the Norwegian Prescription Database. Incidences and age-adjusted relative risks (RR) were estimated. RESULTS: IPD incidences decreased in all groups. The PCV13 incidence decreased by 5-12% across groups. The non-PCV13 incidence increased by 4-10%, mostly in individuals on chemotherapy (overlapping 95% confidence intervals). In the PCV13 era, the RR for IPD was highest (significant) and the percentage of cases caused by the polysaccharide vaccine PPV23 serotypes lowest (numerical) in individuals on chemotherapy (RR = 20.4, PPV23 = 52%), followed by individuals on corticosteroids (RR = 6.2, PPV23 = 64%), other immunosuppressants (RR = 5.6, PPV23 = 68%), and no immunosuppressants (RR = 1 [reference], PPV23 = 74%). CONCLUSIONS: IPD incidences declined after PCV introduction in both immunocompetent and iatrogenically-immunosuppressed individuals, underscoring the benefit of childhood vaccination for the entire population. Still, individuals treated with immunosuppressants in ambulatory care are at increased risk of IPD caused by a more diverse group of serotypes.

Racial disparities in end-stage renal disease in a high-risk population: the Southern Community Cohort Study.

INTRODUCTION: The Southern Community Cohort Study is a prospective study of low socioeconomic status (SES) blacks and whites from the southeastern US, where the burden of end-stage renal disease (ESRD) and its risk factors are high. We tested whether the 2.4-fold elevated risk of ESRD we previously observed in blacks compared to whites was explained by differences in baseline kidney function. METHODS: We conducted a case-cohort study of incident ESRD cases (n = 737) with stored blood and a probability sampled subcohort (n = 4238) and calculated estimated glomerular filtration rate (eGFR) from serum creatinine. 86% of participants were enrolled from community health centers in medically underserved areas and 14% from the general population in 12 states in the southeastern United States. Incident ESRD after entry into the cohort was ascertained by linkage of the cohort with the US Renal Data System (USRDS). RESULTS: Median (25th, 75th percentile) eGFR at baseline was 63.3 (36.0, 98.2) ml/min/1.73m2 for ESRD cases and 103.2 (86.0, 117.9) for subcohort. Black ESRD cases had higher median (25th, 75th) eGFR [63.3 (35.9, 95.9)] compared to whites [59.1 (39.4, 99.2)]. In multivariable Cox models accounting for sampling weights, baseline eGFR was a strong predictor of ESRD risk, and an interaction with race was detected (P = 0.029). The higher ESRD risk among blacks relative to whites persisted (hazard ratio: 2.58; 95% confidence interval: 1.65, 4.03) after adjustment for eGFR. CONCLUSION: In this predominantly lower SES cohort, the racial disparity in ESRD risk is not explained by differences in baseline kidney function.

Analysis of multiple survival events in generalized case-cohort designs.

Generalized case-cohort design has been proposed to assess the effects of exposures on survival outcomes when measuring exposures is expensive and events are not rare in the cohort. In such design, expensive exposure information is collected from both a (stratified) randomly selected subcohort and a subset of individuals with events. In this article, we consider extension of such design to study multiple types of survival events by selecting a proportion of cases for each type of event. We propose a general weighting scheme to analyze data. Furthermore, we examine the optimal choice of weights and show that this optimal weighting yields much improved efficiency gain both asymptotically and in simulation studies. Finally, we apply our proposed methods to data from the Atherosclerosis Risk in Communities study.