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It is shown that variance estimates for regression coefficients in exposure-stratified case-cohort studies (Borgan et al 9 Lifetime Data Anal., 6,2000,39-58) can easily be obtained from influence terms routinely calculated in the standard software for Cox regression. By allowing for post-stratification on outcome we also place the estimators proposed by Chen (J. R. Statist. Soc. Ser. B, 63, 2001,791-809) for a general class of cohort sampling designs within the Borgan et al.'s framework, facilitating simple variance estimation for these designs. Finally, the Chen approach is extended to accommodate stratified designs with surrogate variables available for all cohort members, such as stratified case-cohort and counter-matching designs.

Background: The logistic regression analysis proposed by Schouten et al. (Stat Med. 1993;12:1733–1745) has been a standard method in current statistical analysis of case-cohort studies, and it enables effective estimation of risk ratio from selected subsamples, with adjustment of potential confounding factors. Schouten et al. (1993) also proposed the standard error estimate of the risk ratio estimator can be calculated by the robust variance estimator, and this method has been widely adopted.Methods and Results: We show that the robust variance estimator does not account for the duplications of case and subcohort samples and generally has certain bias, i.e., inaccurate confidence intervals and P-values are possibly obtained. To address the invalid statistical inference problem, we provide an alternative bootstrap-based valid variance estimator. Through simulation studies, the bootstrap method consistently provided more precise confidence intervals compared with those provided by the robust variance method, while retaining adequate coverage probabilities.Conclusion: The robust variance estimator has certain bias, and inadequate conclusions might be deduced from the resultant statistical analyses. The proposed bootstrap variance estimator can provide more accurate and precise interval estimates. The bootstrap method would be an alternative effective approach in practice to provide accurate evidence.

The nested case-control and case-cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full-cohort studies for nested case-control and case-cohort studies. We consider data missing by design and data missing by chance. MI analyses that make use of full-cohort data and MI analyses based on substudy data only are described, alongside an intermediate approach in which the imputation uses full-cohort data but the analysis uses only the substudy. We describe adaptations to two imputation methods: the approximate method (MI-approx) of White and Royston (2009) and the \"substantive model compatible\" (MI-SMC) method of Bartlett et al. (2015). We also apply the \"MI matched set\" approach of Seaman and Keogh (2015) to nested case-control studies, which does not require any full-cohort information. The methods are investigated using simulation studies and all perform well when their assumptions hold. Substantial gains in efficiency can be made by imputing data missing by design using the full-cohort approach or by imputing data missing by chance in analyses using the substudy only. The intermediate approach brings greater gains in efficiency relative to the substudy approach and is more robust to imputation model misspecification than the full-cohort approach. The methods are illustrated using the ARIC Study cohort. Supplementary Materials provide R and Stata code.

Aims/hypothesis Intake of dietary fibre has been associated with a reduced risk of type 2 diabetes, but few European studies have been published on this. We evaluated the association between intake of dietary fibre and type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study and in a meta-analysis of prospective studies. Methods During 10.8 years of follow-up, 11,559 participants with type 2 diabetes were identified and a subcohort of 15,258 participants was selected for the case-cohort study. Country-specific HRs were estimated using Prentice-weighted Cox proportional hazards models and were pooled using a random effects meta-analysis. Eighteen other cohort studies were identified for the meta-analysis. Results In the EPIC-InterAct Study, dietary fibre intake was associated with a lower risk of diabetes (HR Q4 vs Q1 0.82; 95% CI 0.69, 0.97) after adjustment for lifestyle and dietary factors. Similar inverse associations were observed for the intake of cereal fibre and vegetable fibre, but not fruit fibre. The associations were attenuated and no longer statistically significant after adjustment for BMI. In the meta-analysis (19 cohorts), the summary RRs per 10 g/day increase in intake were 0.91 (95% CI 0.87, 0.96) for total fibre, 0.75 (95% CI 0.65, 0.86) for cereal fibre, 0.95 (95% CI 0.87, 1.03) for fruit fibre and 0.93 (95% CI 0.82, 1.05) for vegetable fibre. Conclusions/interpretation The overall evidence indicates that the intake of total and cereal fibre is inversely related to the risk of type 2 diabetes. The results of the EPIC-InterAct Study suggest that the association may be partially explained by body weight.

Neutralizing antibody titers postdose 3 correlate with efficacy of the CYD-TDV dengue vaccine to prevent symptomatic, virologically confirmed dengue. In 2 Phase 3 trials, high titers associated with high vaccine efficacy for all serotypes, baseline serostatus groups, and age groups. Abstract Background In the CYD14 and CYD15 Phase 3 trials of the CYD-TDV dengue vaccine, estimated vaccine efficacy (VE) against symptomatic, virologically confirmed dengue (VCD) occurring between months 13 and 25 was 56.5% and 60.8%, respectively. Methods Neutralizing antibody titers to the 4 dengue serotypes in the CYD-TDV vaccine insert were measured at month 13 in a randomly sampled immunogenicity subcohort and in all VCD cases through month 25 (2848 vaccine, 1574 placebo) and studied for their association with VCD and with the level of VE to prevent VCD. Results For each trial and serotype, vaccinees with higher month 13 titer to the serotype had significantly lower risk of VCD with that serotype (hazard ratios, 0.19–0.43 per 10-fold increase). Moreover, for each trial, vaccinees with higher month 13 average titer to the 4 serotypes had significantly higher VE against VCD of any serotype (P < .001). Conclusions Neutralizing antibody titers postdose 3 correlate with CYD-TDV VE to prevent dengue. High titers associate with high VE for all serotypes, baseline serostatus groups, age groups, and both trials. However, lowest titers do not fully correspond to zero VE, indicating that other factors influence VE.

Biased sampling occurs frequently in economics, epidemiology, and medical studies either by design or due to data collecting mechanism. Failing to take into account the sampling bias usually leads to incorrect inference. We propose a unified estimation procedure and a computationally fast resampling method to make statistical inference for quantile regression with survival data under general biased sampling schemes, including but not limited to the length-biased sampling, the case-cohort design, and variants thereof. We establish the uniform consistency and weak convergence of the proposed estimator as a process of the quantile level. We also investigate more efficient estimation using the generalized method of moments and derive the asymptotic normality. We further propose a new resampling method for inference, which differs from alternative procedures in that it does not require to repeatedly solve estimating equations. It is proved that the resampling method consistently estimates the asymptotic covariance matrix. The unified framework proposed in this article provides researchers and practitioners a convenient tool for analyzing data collected from various designs. Simulation studies and applications to real datasets are presented for illustration. Supplementary materials for this article are available online.

This longitudinal register study aimed to investigate the association between gambling disorder (GD) and work disability and to map work disability in subgroups of individuals with GD, three years before and three years after diagnosis. We included individuals aged 19-62 with GD between 2005 and 2018 ( = 2830; 71.1% men, mean age: 35.1) and a matched comparison cohort ( = 28 300). Work disability was operationalized as the aggregated net days of sickness absence and disability pension. Generalized estimating equation models were used to calculate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for the risk of long-term work disability (>90 days of work disability/year). Secondly, we conducted Group-based Trajectory Models on days of work disability. Individuals with GD showed a four-year increased risk of long-term work disability compared to the matched cohort, peaking at the time of diagnosis (AOR = 1.89; CI 1.67-2.13). Four trajectory groups of work disability days were identified: (60.3%, 5.6-11.2 days), (11.4%, 11.8-152.5 days), (11.1%, 65.1-110 days), and (17.1%, 264-331 days). Individuals who were females, older, with prior psychiatric diagnosis, and had been dispensed a psychotropic medication, particularly antidepressants, were more likely to be assigned to groups other than the . Individuals with GD have an increased risk of work disability which may add financial and social pressure and is an additional incentive for earlier detection and prevention of GD.

Case-cohort design has been advocated in many epidemiologic studies when studying rare diseases or events. In this design, with a rare event, all the events are selected for risk-factor assessment. When the event is not rare, it is desirable to consider a generalized case-cohort design, where only a fraction of events are sampled. We provide a valid test statistic to compare hazards functions between two samples for this generalized design and give a method for calculating power. Our result generalizes the result in Cai and Zeng (2004, Biometrics60, 1015-1024), and it shows numerically that efficiency loss due to sampling only part of the events is very low under nonrare-events situation.

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 the risk ratio and risk difference 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 by 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.Conclusions: 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.

Aims/hypothesis Consumption of sugar-sweetened beverages has been shown, largely in American populations, to increase type 2 diabetes incidence. We aimed to evaluate the association of consumption of sweet beverages (juices and nectars, sugar-sweetened soft drinks and artificially sweetened soft drinks) with type 2 diabetes incidence in European adults. Methods We established a case–cohort study including 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,154 participants selected from eight European cohorts participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. After exclusions, the final sample size included 11,684 incident cases and a subcohort of 15,374 participants. Cox proportional hazards regression models (modified for the case–cohort design) and random-effects meta-analyses were used to estimate the association between sweet beverage consumption (obtained from validated dietary questionnaires) and type 2 diabetes incidence. Results In adjusted models, one 336 g (12 oz) daily increment in sugar-sweetened and artificially sweetened soft drink consumption was associated with HRs for type 2 diabetes of 1.22 (95% CI 1.09, 1.38) and 1.52 (95% CI 1.26, 1.83), respectively. After further adjustment for energy intake and BMI, the association of sugar-sweetened soft drinks with type 2 diabetes persisted (HR 1.18, 95% CI 1.06, 1.32), but the association of artificially sweetened soft drinks became statistically not significant (HR 1.11, 95% CI 0.95, 1.31). Juice and nectar consumption was not associated with type 2 diabetes incidence. Conclusions/interpretation This study corroborates the association between increased incidence of type 2 diabetes and high consumption of sugar-sweetened soft drinks in European adults.