PubMed · 15626043
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Emphysema.. https://pubmed.ncbi.nlm.nih.gov/15626043/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P < .05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio < 1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P < 5 × 10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.
When competing risks are present, two types of analysis can be performed: modelling the cause specific hazard and modelling the hazard of the subdistribution. This paper contrasts these two methods and presents the benefits of each. The interpretation is specific to the analysis performed. When modelling the cause specific hazard, one performs the analysis under the assumption that the competing risks do not exist. This could be beneficial when, for example, the main interest is whether the treatment works in general. In modelling the hazard of the subdistribution, one incorporates the competing risks in the analysis. This analysis compares the observed incidence of the event of interest between groups. The latter analysis is specific to the structure of the observed data and it can be generalized only to another population with similar competing risks.
Explore the source record for details and available documents.