Traditional methods of adjustment for multiple comparisons (e.g., Bonferroni adjustments) have fallen into disuse in epidemiological studies. However, alternative kinds of adjustment for data with multiple comparisons may sometimes be advisable. When a large number of comparisons are made, and when there is a high cost to investigating false positive leads, empirical or semi-Bayes adjustments may help in the selection of the most promising leads. Here we offer an example of such adjustments in a large surveillance data set of occupation and cancer in Nordic countries, in which we used empirical Bayes (EB) adjustments to evaluate standardized incidence ratios (SIRs) for cancer and occupation among craftsmen and laborers. For men, there were 642 SIRs, of which 138 (21%) had a P < 0.05 (13% positive with SIR > 1.0 and 8% negative with SIR < or = 1.0) when testing the null hypothesis of no cancer/occupation association; some of these were probably due to confounding by nonoccupational risk factors (e.g., smoking). After EB adjustments, there were 95 (15%) SIRs with P < 0.05 (10% positive and 5% negative). For women, there were 373 SIRs, of which 37 (10%) had P < 0.05 before adjustment (6% positive and 4% negative) and 13 (3%) had P < 0.05 after adjustment (2% positive and 1% negative). Several known associations were confirmed after EB adjustment (e.g., pleural cancer among plumbers, original SIR 3.2 (95% confidence interval, 2.5-4.1), adjusted SIR 2.0 (95% confidence interval, 1.6-2.4). EB can produce more accurate estimates of relative risk by shrinking imprecise outliers toward the mean, which may reduce the number of false positives otherwise flagged for further investigation. For example, liver cancer among chimney sweepers was reduced from an original SIR of 2.2 (range, 1.1-4.4) to an adjusted SIR of 1.1 (range, 0.9-1.4). A potentially important future application for EB is studies of gene-environment-disease interactions, in which hundreds of polymorphisms may be evaluated with dozens of environmental risk factors in large cohort studies, producing thousands of associations.
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JMIR Public Health Surveill
January 2025
School of Public Health, National Defense Medical Center, Taipei City, Taiwan.
Background: Japanese encephalitis (JE) is a zoonotic parasitic disease caused by the Japanese encephalitis virus (JEV), and may cause fever, nausea, headache, or meningitis. It is currently unclear whether the epidemiological characteristics of the JEV have been affected by the extreme climatic conditions that have been observed in recent years.
Objective: This study aimed to examine the epidemiological characteristics, trends, and potential risk factors of JE in Taiwan from 2008 to 2020.
Eur J Med Res
January 2025
The Department of Pediatrics, The Third Xiangya Hospital of Central South University, Changsha, 410013, Hunan, China.
Background: The systemic immune-inflammation index (SII) is an emerging marker of inflammation, and the onset of psoriasis is associated with inflammation. The aim of our study was to investigate the potential impact of SII on the incidence rate of adult psoriasis.
Methods: We conducted a cross-sectional study based on the National Health and Nutrition Examination Survey (NHANES) 2011-2014 data sets.
BMC Pharmacol Toxicol
January 2025
Yanzhou District People's Hospital, Jining, Shandong, China.
Background: Osteoporosis (OP), often termed the "silent epidemic," poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across various diseases.
Methods: This study explored FBXW4 by integrating DEGs from GEO datasets GSE2208, GSE7158, GSE56815, and GSE35956 with immune-related gene compilations from the ImmPort repository.
J Neuroeng Rehabil
January 2025
Luzerner Kantonsspital, University, Teaching and Research Hospital, University of Lucerne, Lucerne, Switzerland.
Background: Construct validity and responsiveness of upper limb outcome measures are essential to interpret motor recovery poststroke. Evaluating the associations between clinical upper limb measures and sensor-based arm use (AU) fosters a coherent understanding of motor recovery. Defining sensor-based AU metrics for intentional upper limb movements could be crucial in mitigating bias from walking-related activities.
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January 2025
London Centre for Primary Care, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
Background: The aim of the SURECAN trial is to evaluate a person-centred intervention, based on Acceptance and Commitment Therapy (ACT Plus ( +)), for people who have completed treatment for cancer with curative intent, but are experiencing poor quality of life. We present the statistical analysis plan for assessing the effectiveness and cost-effectiveness of the intervention in improving quality of life 1 year post randomisation.
Methods And Design: SURECAN is a multi-centre, pragmatic, two-arm, partially clustered randomised controlled superiority trial comparing the effectiveness of ACT + added to usual care with usual aftercare.
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