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The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case-control studies, accommodating scenarios with additional matching and non-proportionality. We also consider the case where data for different causes are obtained from different studies conducted in the same cohort. It is demonstrated that while only modest gains in efficiency are possible in full cohort analyses, substantial gains may be attained in nested case-control analyses for failure types that are relatively rare. Extensive simulation studies are conducted and real data analyses are provided using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) study.

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Objective: To examine the potential difference in survival and risk of death between asymptomatic and symptomatic SARS-CoV-2 patients, controlled by age and gender for all the attendance in hospitals of Khyber Pakhtunkhwa (KP), Pakistan. Methods: In this retrospective study, the medical records of 6273 SARS-CoV-2 patients admitted to almost all hospitals in Khyber Pakhtunkhwa during the first wave of the coronavirus outbreak from March to June 2020 were analysed. The effects of gender, age, and being symptomatic on the survival of SARS-CoV-2 patients were assessed using cure-survival models as opposed to the conventional Cox proportional hazards model. Results: The prevalence of initially symptomatic patients was 55.8%, and the overall mortality rate was 11.8%. The fitted cure-survival models suggest that age affects the probability of death (incidence) but not the short-term survival time of patients (latency); symptomatic patients have a higher risk of death than their asymptomatic counterparts, but the survival time of symptomatic patients is longer on average; gender has no significant effect on the probability of death and survival time. Conclusion: The available data and statistical results suggest that asymptomatic and young patients are generally less susceptible to initial infection with SARS-CoV-2 and therefore have a lower risk of death. Our regression models show that uncured asymptomatic patients generally have poorer short-term survival than their uncured symptomatic counterparts. The association between gender and survival outcome was not significant.

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BACKGROUND: American Indian/Alaska Natives (AI/ANs) disproportionately suffer from diabetes compared to non-Hispanic whites (NHW). In 2013, 69% of end-stage kidney disease (ESKD) in AI/ANs was caused by diabetes (ESKD-D) but accounts for only 44% of ESKD diagnoses in the overall USA population. Moreover, the diagnosis of diabetes and ESKD-D may be significantly related to social determinants of health. The purpose of this study was to conduct a survival analysis of AI/ANs and NHWs diagnosed with ESKD-D nationally and by Indian Health Service region and correlate the survival analysis to the Area Deprivation Index® (ADI®). METHODS: This manuscript reports a retrospective cohort analysis of 2021 United States Renal Data System data. Eligible patient records were AI/AN and NHWs with diabetes as the primary cause of ESKD and started dialysis on January 1, 2014, or later. RESULTS: A total of 81,862 patient records were included in this analysis, of which 1798 (2.2%) were AI/AN. AI/ANs survive longer, with an 18.4% decrease in risk of death compared to NHW. However, AI/ANs are diagnosed with ESKD-D and start dialysis earlier than NHWs. ADI® variables became significant as ADI® ratings increased, meaning persons with greater social disadvantage had worse survival outcomes. CONCLUSIONS: The findings reveal that AI/ANs have better survival outcomes than NWH, explained in part by initiating dialysis earlier than NHW. Additional research is needed to explore factors (e.g., social determinants; cultural; physiologic) that contribute to earlier diagnosis of ESKD-D in AI/ANs and the impact of prolonged dialysis on quality of life of those with ESKD-D.

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During patient follow-up in a randomized trial, some deaths may occur. Where death (or noncardiovascular death) is not part of an outcome of interest it is termed a competing risk. Conventional analyses (eg, Cox proportional hazards model) handle death similarly to other censored follow-up. Patients still alive are unrealistically assumed to be representative of those who died. The Fine and Gray model has been used to handle competing risks, but is often used inappropriately and can be misleading. We propose an alternative multiple imputation approach that plausibly accounts for the fact that patients who die tend also to be at high risk for the (unobserved) outcome of interest. This provides a logical framework for exploring the impact of a competing risk, recognizing that there is no unique solution. We illustrate these issues in 3 cardiovascular trials and in simulation studies. We conclude with practical recommendations for handling competing risks in future trials.

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Background: Certain viral infections have been linked to the development of neurodegenerative diseases. This study aimed to investigate the association between cytomegalovirus (CMV) infection and five neurodegenerative diseases, spinal muscular atrophy (SMA) and related syndromes, Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis (MS), and disorders of the autonomic nervous system (DANS). Methods: This prospective cohort included white British individuals who underwent CMV testing in the UK Biobank from January 1, 2006 to December 31, 2021. A Cox proportional hazard model was utilized to estimate the future risk of developing five neurodegenerative diseases in individuals with or without CMV infection, adjusted for batch effect, age, sex, and Townsend deprivation index in Model 1, and additionally for type 2 diabetes, cancer, osteoporosis, vitamin D, monocyte count and leukocyte count in Model 2. Bidirectional Mendelian randomization was employed to validate the potential causal relationship between CMV infection and PD. Findings: A total of 8346 individuals, consisting of 4620 females (55.4%) and 3726 males (44.6%) who were white British at an average age of 56.74 (8.11), were included in this study. The results showed that CMV infection did not affect the risk of developing AD (model 1: HR [95% CI] = 1.01 [0.57, 1.81], P = 0.965; model 2: HR = 1.00 [0.56, 1.79], P = 0.999), SMA and related syndromes (model 1: HR = 3.57 [0.64, 19.80], P = 0.146; model 2: HR = 3.52 [0.63, 19.61], P = 0.152), MS (model 1: HR = 1.16 [0.45, 2.97], P = 0.756; model 2: HR = 1.16 [0.45, 2.97], P = 0.761) and DANS (model 1: HR = 0.65 [0.16, 2.66], P = 0.552; model 2: HR = 0.65 [0.16, 2.64], P = 0.543). Interestingly, it was found that participants who were CMV seronegative had a higher risk of developing PD compared to those who were seropositive (model 1: HR = 2.37 [1.25, 4.51], P = 0.009; model 2: HR = 2.39 [1.25, 4.54], P = 0.008) after excluding deceased individuals. This association was notably stronger in males (model 1: HR = 3.16 [1.42, 7.07], P = 0.005; model 2: HR = 3.41 [1.50, 7.71], P = 0.003), but no significant difference was observed in the female subgroup (model 1: HR = 1.28 [0.40, 4.07], P = 0.679; model 2: HR = 1.27 [0.40, 4.06], P = 0.684). However, a bidirectional Mendelian randomization analysis did not find a genetic association between CMV infection and PD. Interpretation: The study found that males who did not have a CMV infection were at a higher risk of developing PD. The findings provided a new viewpoint on the risk factors for PD and may potentially influence public health approaches for the disease. Funding: National Natural Science Foundation of China (81873776), Natural Science Foundation of Guangdong Province, China (2021A1515011681, 2023A1515010495).

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Evidence linking greenness to all-site and site-specific cancers remains limited, and the complex role of air pollution in this pathway is unclear. We aimed to fill these gaps by using a large cohort in southern China. A total of 654,115 individuals were recruited from 2009 to 2015 and followed-up until December 2020. We calculated the normalized difference vegetation index (NDVI) in a 500-m buffer around the participants' residences to represent the greenness exposure. Cox proportional-hazards models were used to evaluate the impact of greenness on the risk of all-site and site-specific cancer mortality. Additionally, we assessed both the mediation and interaction roles of air pollution (i.e., PM2.5, PM10, and NO2) in the greenness-cancer association through a causal mediation analysis using a four-way decomposition method. Among the 577,643 participants, 10,088 cancer deaths were recorded. We found a 10% (95% CI: 5-16%) reduction in all-site cancer mortality when the NDVI increased from the lowest to the highest quartile. When stratified by cancer type, our estimates suggested 18% (95% CI: 8-27%) and 51% (95% CI: 16-71%) reductions in mortality due to respiratory system cancer and brain and nervous system cancer, respectively. For the above protective effect, a large proportion could be explained by the mediation (all-site cancer: 1.0-27.7%; respiratory system cancer: 1.2-32.3%; brain and nervous system cancer: 3.6-109.1%) and negative interaction (all-site cancer: 2.1-25.7%; respiratory system cancer: 2.0-25.7%; brain and nervous system cancer: not significant) effects of air pollution. We found that particulate matter (i.e., PM2.5 and PM10) had a stronger causal mediation effect (25.0-109.1%) than NO2 (1.0-3.6%), while NO2 had a stronger interaction effect (25.7%) than particulate matter (2.0-2.8%). In summary, greenness was significantly beneficial in reducing the mortality of all-site, respiratory system, and brain and nervous system cancer in southern China, with the impact being modulated and mediated by air pollution.

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BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

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The study aimed to assess the impact of changes in blood pressure on cardiovascular events in the Chinese population. It enrolled 33 179 Chinese participants aged ≥35 years (57.1% women) without CVD at baseline. BP status was defined according to the 2017 ACC/AHA hypertension guidelines. The type of BP change was defined as change in BP status from baseline to the end of follow-up, included stable BP <130/80, <130/80 to ≥130/80, ≥130/80 to <130/80 mm Hg, persistent BP ≥130/80 mm Hg. The hazard ratio (HR) of incident CVD by change in BP category was estimated using Cox proportional hazards and Fine-Gray models. During median follow-up of 3.17 years, 2023 CVD events occurred. Compared with BP <120/80, 120-129/<80 mm Hg at baseline (HR = 1.66, 95% CI: 1.09-2.53), 130-139/80-89 mm Hg (HR = 1.35, 95% CI: 0.94-1.95), and ≥140/90 mm Hg (HR = 2.46, 95% CI: 1.78-3.40) were risk factors for CVD. Compared with the group with stable BP <130/80 mm Hg, the risk of CVD was 1.88 (95% CI: 1.40-2.53) in the group with persistent BP ≥130/80 mm Hg and 1.40 (95% CI: 1.01-1.94) in the group of BP decreased to <130/80 mm Hg. These results showed that BP 120-129/<80, 130-139/80-89, and ≥140/90 mm Hg were associated with a high risk of CVD. Over time, persistent BP ≥130/80 mm Hg increased the risk of CVD, but a return to <130/80 mm Hg from hypertension decreased the risk of CVD.

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OBJECTIVE: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, this assumption is rarely true for most diseases, as the underlying factors have complex, non-linear, and time-varying relationships. This concern is especially relevant for pregnancy, where the risk for pregnancy-related complications, such as preeclampsia, varies across gestation. Recently, deep learning survival models have shown promise in addressing the limitations of classical models, as the novel models allow for non-proportional risk handling, capturing nonlinear relationships, and navigating complex temporal dynamics. METHODS: We present a methodology to model the temporal risk of preeclampsia during pregnancy and investigate the associated clinical risk factors. We utilized a retrospective dataset including 66,425 pregnant individuals who delivered in two tertiary care centers from 2015 to 2023. We modeled the preeclampsia risk by modifying DeepHit, a deep survival model, which leverages neural network architecture to capture time-varying relationships between covariates in pregnancy. We applied time series k-means clustering to DeepHit's normalized output and investigated interpretability using Shapley values. RESULTS: We demonstrate that DeepHit can effectively handle high-dimensional data and evolving risk hazards over time with performance similar to the Cox Proportional Hazards model, achieving an area under the curve (AUC) of 0.78 for both models. The deep survival model outperformed traditional methodology by identifying time-varied risk trajectories for preeclampsia, providing insights for early and individualized intervention. K-means clustering resulted in patients delineating into low-risk, early-onset, and late-onset preeclampsia groups-notably, each of those has distinct risk factors. CONCLUSION: This work demonstrates a novel application of deep survival analysis in time-varying prediction of preeclampsia risk. Our results highlight the advantage of deep survival models compared to Cox Proportional Hazards models in providing personalized risk trajectory and demonstrating the potential of deep survival models to generate interpretable and meaningful clinical applications in medicine.

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Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

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PURPOSE: Several predictors have been identified for mental sickness absence, but those for recurrences are not well-understood. This study assesses recurrence rates for long-term mental sickness absence (LTMSA) within subgroups of common mental disorders (CMDs) and identifies predictors of recurrent LTMSA. METHODS: This historical prospective cohort study used routinely collected data from 16,310 employees obtained from a nationally operating Dutch occupational health service (ArboNed). Total follow-up duration was 23,334 person-years. Overall recurrence rates were assessed using Kaplan-Meier estimators. Recurrence rates within subgroups of CMDs were calculated using person-years. Univariable and multivariable Cox proportional hazards models were used to identify predictors. RESULTS: 15.6% of employees experienced a recurrent LTMSA episode within three years after fully returning to work after a previous LTMSA episode. Highest recurrence rates for LTMSA were observed after a previous LTMSA episode due to mood or anxiety disorders. Mood or anxiety disorders and shorter previous episode duration were predictors of recurrent LTMSA. No associations were found for age, gender, company size, full-time equivalent and job tenure. CONCLUSION: Employees should be monitored adequately after they fully returned to work after LTMSA. It is recommended to monitor high-risk employees (i.e. employees with mood or anxiety disorders and short LTMSA episode) more intensively, also beyond full return to work. Moreover, diagnosis of anxiety and depressive symptoms should be given a higher priority in occupational healthcare.

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Objective: This population-based study aims to assess the survival benefits of selective neck dissection (SND) compared to neck observation in patients with T1/T2N0M0 major salivary gland malignancy (MSGC). Methods: We conducted a retrospective review of T1/T2N0M0 MSGC patients who underwent primary tumor surgical extirpation with or without elective neck dissection in the Surveillance, Epidemiology, and End Results database (SEER) from 2004-2015. The impact of SND and clinical variables on overall survival (OS) and disease-specific survival (DSS) was evaluated using Univariate and Multivariate Cox proportional hazards regression models. Kaplan-Meier survival curves were generated, and survival rates were assessed via the log-rank test. Results: Of 3778 post-operative T1-T2N0M0 MSGC patients, 2305 underwent elective neck dissection, while 1473 did not. Median follow-up was 106 months. Univariate and Multivariate analysis identified SND as a prognostic factor for OS in all the study population. After stratified analysis, we found that in the poorly high-grade (differentiated and undifferentiated) patients, the survival showed a significant OS and DSS benefit after receiving SND compared with the neck observations [HR for OS (95%CI): 0.571(0.446-0.731), P<0.001] and [HR for DSS (95%CI): 0.564(0.385-0.826), P=0.003], other than in the well differentiated or moderately differentiated subgroup. Especially, when the pathological is squamous cell carcinoma, the results show that the people underwent SND had better prognosis, not only in OS [HR (95%CI): 0.532(0.322-0.876), P=0.013], but also in DSS [HR (95%CI): 0.330(0.136-0.797), P=0.014]. The multivariate analysis also yielded encouraging results, compared with neck observation, receiving SND bought about a significant independent OS (adjusted HR, 0.555; 95% CI, 0.328-0.941; P=0.029) and DSS (adjusted HR, 0.349; 95% CI, 0.142-0.858; P=0.022) advantage in high grade squamous cell carcinoma MSGC patients. The Kaplan-Meier survival curves also demonstrated that adjusted SND still had significantly better OS(P=0.029) and DSS(P=0.022) than the observation group in patients with high-grade squamous cell carcinoma of MSGC. Conclusion: Poorly differentiated and undifferentiated T1/T2N0M0 major salivary gland malignancy treated with selective neck dissection demonstrated superior survival compared to neck observation, especially in the pathological subtype of squamous cell carcinoma. These findings suggest the potential benefits of multimodal therapy for appropriately selected patients, emphasizing significant clinical implications.

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BACKGROUND: Visceral leishmaniasis (VL) is a neglected tropical disease that mostly affects the working class and impoverished segments of society, having a significant negative effect on the economic development of the affected nation. While anti-leishmanial medications lower mortality among VL patients, patients may still die or require more time to recover while receiving treatment. In this regard, there are limited studies in Ethiopia. This study aims to determine the time to recovery and its associated predictors among adult VL patients at Metema Hospital, Metema, Ethiopia. METHODS: A hospital-based cross-sectional study was employed and the data were collected from patient's charts from September 2017 to September 2021. Data were entered and analysed using EpiData version 3.1, Stata version 14.2 and R version 3.4.0 statistical software. Kaplan-Meier survival curves and logrank tests were used to compare the survival time. The Cox proportional hazards model assumption and model fitness were checked and used to identify statistical association predictors in VL patients. RESULTS: The Cox proportional hazards model was fitted. The overall medium recovery time was 7 d (minimum 4, maximum 14). The variables of nasal bleeding (adjusted hazard ratio [aHR] 0.44 [95% confidence interval {CI} 0.19 to 0.89]), no comorbidity (aHR 2.29 [95% CI 1.27 to 4.11]), relapse of VL (aHR 0.33 [95% CI 0.15 to 0.75]), low parasite load (aHR 2.58 [95% CI 1.48 to 4.51]) and ambulatory (aHR 3.26 [95% CI 2.45 to 6.53]) were significantly associated with time to recovery in VL patients. CONCLUSIONS: Patients with comorbidities, nasal bleeding, relapse of VL, bedridden and high parasite load should be treated and monitored carefully to recover quickly from their illness.

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BACKGROUND: Astrocytoma is a type of adult-type diffuse gliomas that includes diffuse astrocytoma (DA) and anaplastic astrocytoma (AA). However, comprehensive investigations into the risk assessment and prognosis of DA and AA using population-based studies remain noticeably scarce. METHODS: In this study, we developed 2 predictive nomograms to evaluate the susceptibility and prognosis associated with DA and AA. The study cohort comprised 3837 individuals diagnosed with DA or AA between 2010 and 2019 selected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent predictors were identified and used to construct the nomograms for overall death and cancer-specific death rates. The performance of the models was assessed using C-index, calibration curves, and receiver operating characteristic curve, and the clinical applicability was evaluated using decision curve analysis. RESULTS: The receiver operating characteristic curves in this study show excellent clinical applicability and predictive power. Notably, the area under the curves of the training and verification queues was higher than 0.80, thereby cementing the models' precision. Additionally, the calibration plots demonstrate that the anticipated mortality rates strikingly match the measured values. This alignment of figures is sustained in the validation cohort. Furthermore, the decision curve analysis corroborates the models' translational potential, reinforcing their relevance within real-world clinical settings. CONCLUSIONS: The presented nomograms have not only exhibited good predictive performance but also showcased pragmatic clinical utility in prognosticating patient outcomes. Significantly, this will undoubtedly serve as a valuable asset for oncologists, facilitating informed treatment decisions and meticulous follow-up planning.

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BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

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Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time- and memory-efficient single-pass parallel scan algorithms for Cox proportional hazards models and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders of magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics. The above implementation is available in the open-source R package Cyclops (Suchard et al., 2013).

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Objective: To assess the effectiveness and clinical value of case-cohort design and determine prognostic factors of breast cancer patients in Xinjiang on the basis of case-cohort design. Methods: The survival data with different sample characteristics were simulated by using Cox proportional risk models. To evaluate the effectiveness for the case-cohort, entire cohort, and simple random sampling design by comparing the mean, coefficient of variation, etc., of covariate parameters. Furthermore, the prognostic factors of breast cancer patients in Xinjiang were determined based on case-cohort sampling designs. The models were comprehensively evaluated by likelihood ratio test, the area under the receiver operating characteristic curve (AUC), and Akaike Information Criterion (AIC). Results: In a simulations study, the case-cohort design shows better stability and improves the estimation efficiency when the censored rate is high. In the breast cancer data, molecular subtypes, T-stage, N-stage, M-stage, types of surgery, and postoperative chemotherapy were identified as the prognostic factors of patients in Xinjiang. These models based on the different sampling designs both passed the likelihood ratio test (p<0.05). Moreover, the model constructed under the case-cohort design had better fitting effect (AIC=3,999.96) and better discrimination (AUC=0.807). Conclusion: Simulations study confirmed the effectiveness of case-cohort design and further determined the prognostic factors of breast cancer patients in Xinjiang based on this design, which presented the practicality of case-cohort design in actual data.

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Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as MMP9, PIK3R6, IFNG, and PGF, exhibiting significant associations with overall survival and relapse-free survival in ccRCC patients. Cox multivariate PH analysis, considering gene expression and age at diagnosis, further confirmed the prognostic potential of MMP9, IFNG, and PGF genes. These findings enhance our understanding of ccRCC and provide valuable insights into potential prognostic biomarkers that can aid healthcare professionals in risk stratification and treatment decision-making. The study also establishes a foundation for future research, validation, and clinical translation of the identified prognostic biomarkers, paving the way for personalized approaches in the management of KIRC.

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Individual patient data (IPD) meta-analyses build upon traditional (aggregate data) meta-analyses by collecting IPD from the individual studies rather than using aggregated summary data. Although both traditional and IPD meta-analyses produce a summary effect estimate, IPD meta-analyses allow for the analysis of data to be performed as a single dataset. This allows for standardization of exposure, outcomes, and analytic methods across individual studies. IPD meta-analyses also allow the utilization of statistical methods typically used in cohort studies, such as multivariable regression, survival analysis, propensity score matching, uniform subgroup and sensitivity analyses, better management of missing data, and incorporation of unpublished data. However, they are more time-intensive, costly, and subject to participation bias. A separate issue relates to the meta-analytic challenges when the proportional hazards assumption is violated. In these instances, alternative methods of reporting time-to-event estimates, such as restricted mean survival time should be used. This statistical primer summarizes key concepts in both scenarios and provides pertinent examples.

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Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation.

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