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Background Aspirin has been considered to be beneficial in preventing cardiovascular diseases and cancer. Several pharmaco-epidemiology cohort studies have shown protective effects of aspirin on diseases using various statistical methods, with the Cox regression model being the most commonly used approach. However, there are some inherent limitations to the conventional Cox regression approach such as guarantee-time bias, resulting in an overestimation of the drug effect. To overcome such limitations, alternative approaches, such as the time-dependent Cox model and landmark methods have been proposed. This study aimed to compare the performance of three methods: Cox regression, time-dependent Cox model and landmark method with different landmark times in order to address the problem of guarantee-time bias. Methods Through statistical modeling and simulation studies, the performance of the above three methods were assessed in terms of type I error, bias, power, and mean squared error (MSE). In addition, the three statistical approaches were applied to a real data example from the Korean National Health Insurance Database. Effect of cumulative rosiglitazone dose on the risk of hepatocellular carcinoma was used as an example for illustration. Results In the simulated data, time-dependent Cox regression outperformed the landmark method in terms of bias and mean squared error but the type I error rates were similar. The results from real-data example showed the same patterns as the simulation findings. Conclusions While both time-dependent Cox regression model and landmark analysis are useful in resolving the problem of guarantee-time bias, time-dependent Cox regression is the most appropriate method for analyzing cumulative dose effects in pharmaco-epidemiological studies.

The advantage of an increasing amount of bioinformatics data on leukemias intrigued us to explore the hot-spot mutation profiles and investigate the implications of those hot-spot mutations in patient survival. We retrieved somatic mutations and their distribution in protein domains through data analysis of The Cancer Genome Atlas and cBioPortal databases. After determining differentially expressed mutant genes related to leukemia, we further conducted principal component analysis and single-factor Cox regression analyses. Moreover, survival analysis was performed for the obtained candidate genes, followed by a multi-factor Cox proportional hazard model method for the impacts of the candidate genes on the survival and prognosis of patients with leukemia. At last, the signaling pathways involved in leukemia were investigated by gene set enrichment analysis. There were 223 somatic missense mutation hot-spots identified with pertinence to leukemia, which were distributed in 41 genes. Differential expression in leukemia was witnessed in 39 genes. We found a close correlation between seven genes and the prognosis of leukemia patients, among which, three genes could significantly influence the survival rate. In addition, among these three genes, CD74 and P2RY8 were highlighted due to close pertinence with survival conditions of leukemia patients. Finally, data suggested that B cell receptor, Hedgehog, and TGF-beta signaling pathways were enriched in low-hazard patients. In conclusion, these data underline the involvement of hot-spot mutations of CD74 and P2RY8 genes in survival status of leukemia patients, highlighting their as novel therapeutic targets or prognostic indicators for leukemia patients.Summary of Graphical Abstract: We identified 223 leukemia-associated somatic missense mutation hotspots concentrated in 41 different genes from 2297 leukemia patients in the TCGA database. Differential analysis of leukemic and normal samples from the TCGA and GTEx databases revealed that 39 of these 41 genes showed significant differential expression in leukemia. These 39 genes were subjected to PCA analysis, univariate Cox analysis, survival analysis, multivariate Cox regression analysis, GSEA pathway enrichment analysis, and then the association with leukemia survival prognosis and related pathways were investigated.

Skin cutaneous melanoma (SKCM) is a highly aggressive malignancy, and understanding the mechanisms underlying its metastasis is essential for improving patient prognosis. N6-methyladenosine (m6A) RNA modification is involved in tumor progression; however, its specific role in SKCM metastasis remains poorly defined. The present study aimed to identify m6A-related regulatory genes associated with SKCM metastasis and to assess their impact on the tumor immune microenvironment. Expression data from primary and metastatic SKCM samples were obtained from the Gene Expression Omnibus (GSE8401, GSE15605, GSE46517 and GSE65904) and The Cancer Genome Atlas-SKCM databases. A metastasis-risk prediction model was constructed using least absolute shrinkage and selection operator-Cox regression analysis. Differential expression analysis, functional enrichment, Pearson correlation, single-sample gene set enrichment analysis and competing endogenous (ce)RNA network analysis were performed. Key gene expression levels were evaluated using reverse transcription-quantitative PCR and immunohistochemistry. A total of 94 metastasis-related mRNAs were identified as differentially expressed, of which 45 demonstrated significant associations with m6A regulators. Among them, 12 genes were associated with patient prognosis, with cadherin 3 (CDH3), keratin 17 (KRT17), plakophilin 1 (PKP1) and cellular retinoic acid binding protein 2 (CRABP2) identified as key candidates. A ceRNA network comprising these four mRNAs, 13 long noncoding RNAs, and 20 microRNAs was constructed. These core genes demonstrated significantly higher expression levels in tumor tissues compared with in adjacent normal tissues, were associated with a worse overall survival, and revealed strong correlations with immune cell infiltration, particularly mast cells and Th17 cells. In conclusion, m6A RNA modification may contribute to SKCM metastasis by regulating the expression of CDH3, KRT17, PKP1 and CRABP2, as well as modulating the tumor immune microenvironment. These findings offer novel insights into the metastatic mechanisms of SKCM and identify potential biomarkers for its diagnosis, prognosis and targeted immunotherapy.

Proportional hazard Cox regression models are overwhelmingly used for analyzing time-dependent outcomes. Despite their associated hazard ratio is a valuable index for the difference between populations, its strong dependency on the underlying assumptions makes it a source of misinterpretation. Recently, a number of works have dealt with the subtleties and limitations of this interpretation. Besides, a number of alternative indices and different Cox-type models have been proposed. In this work, we use synthetic data, motivated by a real-world problem, for showing the strengths and weaknesses of some of those methods in the analysis of time-dependent outcomes. We use the power of synthetic data for considering observable results but also utopian designs.

Background The population of adult residential care homes has been shown to have high morbidity and mortality in relation to COVID‐19. Methods We examined 3115 hospital discharges to a national cohort of 1068 adult care homes and subsequent outbreaks of COVID‐19 occurring between 22 February and 27 June 2020. A Cox proportional hazards regression model was used to assess the impact of time‐dependent exposure to hospital discharge on incidence of the first known outbreak, over a window of 7‐21 days after discharge, and adjusted for care home characteristics, including size and type of provision. Results A total of 330 homes experienced an outbreak, and 544 homes received a discharge over the study period. Exposure to hospital discharge was not associated with a significant increase in the risk of a new outbreak (hazard ratio 1.15, 95% CI 0.89, 1.47, P = .29) after adjusting for care home characteristics. Care home size was the most significant predictor. Hazard ratios (95% CI) in comparison with homes of <10 residents were as follows: 3.40 (1.99, 5.80) for 10‐24 residents; 8.25 (4.93, 13.81) for 25‐49 residents; and 17.35 (9.65, 31.19) for 50+ residents. When stratified for care home size, the outbreak rates were similar for periods when homes were exposed to a hospital discharge, in comparison with periods when homes were unexposed. Conclusion Our analyses showed that large homes were at considerably greater risk of outbreaks throughout the epidemic, and after adjusting for care home size, a discharge from hospital was not associated with a significant increase in risk.

The association between N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations and in-hospital and 1-year mortality in acute exacerbations of chronic obstructive pulmonary disease (AECOPD) patients is largely unknown. Our objective was to explore the usefulness of NT-proBNP concentrations in AECOPD patients as a prognostic marker for in-hospital and 1-year mortality. NT-proBNP levels were measured in patients upon admission and laboratory and clinical data were also recorded. The cut-point for the NT-proBNP concentration level for in-hospital death was obtained using the receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression and Cox regression were used in the analyses of factors of in-hospital and 1-year mortality. A total of 429 patients were enrolled. Twenty-nine patients died during hospitalization and 59 patients died during the 1-year follow-up. Patients who died in-hospital compared with those in-hospital survivors were older (80.14±6.56 vs 75.93±9.45 years, p=0.003), had a higher percentage of congestive heart failure (65.52% vs 33.75%, p<0.001), had higher NT-proBNP levels (5767.00 (1372.50-12,887.00) vs 236.25 (80.03-1074.75) ng/L, p<0.001), higher neutrophil counts (10.52±5.82 vs 7.70±4.31, p=0.016), higher D-dimer levels (1231.62±1921.29 vs 490.11±830.19, p=0.048), higher blood urea nitrogen levels (9.91±6.33 vs 6.51±4.01 mmol/L, p=0.001), a lower body mass index (19.49±3.57 vs 22.19±4.76, p=0.003), and higher hemoglobin levels (122.34±25.36 vs 130.57±19.63, p=0.034). The area under the ROC curve (AUC) for NT-proBNP concentration was 0.88 (95% confidence interval [CI], 0.84-0.93). NT-proBNP concentrations ≥551.35 ng/L were an independent prognostic factor for both in-hospital and 1-year mortality after adjustment for relative risk (RR) (RR=29.54, 95% CI 3.04-286.63, p=0.004 for the multivariate logistic regression analysis) and hazard ratio (HR) (HR=4.47, 95% CI, 2.38-8.41, p <0.001 for the multivariate cox regression analysis). NT-proBNP was a strong and independent predictor of in-hospital and 1-year mortality in AECOPD patients.

The asymmetry of residuals about the origin is a severe issue in estimating a Box-Cox transformed model. In the framework of uncertainty theory, there are such theoretical issues regarding the least-squares estimation (LSE) and maximum likelihood estimation (MLE) of the linear models after the Box-Cox transformation on the response variables. Heretofore, only weighting methods for least-squares analysis have been available. This article proposes an uncertain alternative Box-Cox model to alleviate the asymmetry of residuals and avoid λ tending to negative infinity for uncertain LSE or uncertain MLE. Such symmetry of residuals about the origin is reasonable in applications of experts’ experimental data. The parameter estimation method was given via a theorem, and the performance of our model was supported via numerical simulations. According to the numerical simulations, our proposed ‘alternative Box-Cox model’ can overcome the problems of a grossly underestimated lambda and the asymmetry of residuals. The estimated residuals neither deviated from zero nor changed unevenly, in clear contrast to the LSE and MLE for the uncertain Box-Cox model downward biased residuals. Thus, though the LSE and MLE are not applicable on the uncertain Box-Cox model, they fit the uncertain alternative Box-Cox model. Compared with the uncertain Box-Cox model, the issue of a systematically underestimated λ is not likely to occur in our uncertain alternative Box-Cox model. Both the LSE and MLE can be used directly without constructing a weighted estimation method, offering better performance in the asymmetry of residuals.

As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network‐Regularized high‐dimensional Cox‐regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event‐free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.

Objectives The oldest‐old (aged ≥80 years) are the fastest growing population segment and age is related to cognitive impairment. We aimed to estimate the association between cognitive impairment and all‐cause mortality, in addition to the relationship with different cognitive subdomains among the oldest‐old in China. Methods We analyzed 25,285 participants recruited from 22 out of 30 provinces in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 1998 to 2008, with mortality follow‐up until 2014. Cognitive function was measured by the Chinese‐version 30‐item Mini‐Mental State Examination (MMSE), classified as no (MMSE score: 25–30), mild (18–24), moderate (10–17), and severe (0–9) impairment. We used time‐dependent Cox model to evaluate the relationship between time‐varying cognition and mortality. Results The relationship between cognition and mortality showed a dose–response pattern among the overall population. Compared to those with no impairment, participants with moderate (HR = 1.41, 95% CI 1.28–1.56) and severe (HR = 1.77, 95% CI 1.59–1.96) cognitive impairment showed increased mortality risk. Impairment in the subdomain of orientation was independently associated with increased mortality risk (HR = 1.20, 95% CI 1.05–1.36) among participants without overall cognitive impairment. Urban and rural residents had similar mortality risk. Conclusions A consistent dose–response pattern existed between cognitive impairment and all‐cause mortality. Orientation was associated with mortality in the population without cognitive impairment. Similar mortality regardless of residence areas indicated scarce health care and treatment for cognitive impairment in China from 1998 to 2014. Based on 25,285 Chinese participants 80 years or older, we found a clear negative dose–response relationship between the severity of time‐varying cognitive impairment and the probability of survival for both female and male and the total population. The relationship between cognition and mortality was similar for urban and rural residents, and the dose–response pattern disappeared in the highest educated group. The orientation subscale was associated with mortality risk independent of the overall MMSE score.

Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (  = 490) for a training and testing dataset, and GSE50081 (  = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted  < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.