PubMed · 8951320
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M J Graney. 1996. Can multivariate analysis rule out causality?. https://doi.org/10.1111/j.1532-5415.1996.tb04075.x
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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.
The ideal study of occupational exposure to electromagnetic fields and cancer risk would have a clear exposure source, historically stable exposures, and comparable groups of exposed and unexposed workers. Cohorts of railway workers have marked exposure contrasts and limited job changes and provide marginally adequate study sizes, but there have been important changes in their exposures over time, and the field frequency involved is unusual. The results of Minder and Pfluger's study (Am J Epidemiol 2001;153:825--35) add modest support for an association between electromagnetic field exposure and leukemia. However, given the large size and high quality of a number of previous studies of occupational electromagnetic field exposure and cancer, additional studies similar to past ones are unlikely to yield important new insights.
Physical symptoms account for more than half of all outpatient visits, but the specific causes and treatments of many common symptoms are frequently not well established. Because clinical research has primarily targeted specific diseases, symptoms-based research is still an emerging field of scientific inquiry, and optimal methods are still being developed. This paper considers eight important issues in sampling and measurement. First, recognition of the exceptionally high base rates of many symptoms is an essential primary step in selecting appropriate case-patients and controls. Second, the type of population studied (community, primary care clinic, subspecialty clinic, or symptom clinic) can greatly influence the severity and other characteristics of the symptom being evaluated. Third, symptom detection relies on three types of symptoms: documented, elicited, and volunteered. Fourth, temporal factors include recency of onset, episodicity, and duration. Fifth, severity can be assessed with single- or multiple-item symptom-specific scales as well as evaluation of generic functional status and health-related quality-of- life measures. Sixth, recognition of a symptom's cause can be strengthened by explicit criteria, uniform assessment protocols, and multiple raters. Seventh, comorbidity that should be assessed includes medical disorders, psychiatric disorders, and other symptoms. And eighth, since death and serious illness are uncommon, alternative outcomes that might be measured include symptom alleviation, health-related quality of life, patient and provider satisfaction, and health care costs.