This paper discusses certain issues related to uncertainty in hazard identification. Research on the hypothesis that exposure to 50-60-Hz magnetic and electric fields (EMF) increases the risk of cancer has been ongoing for two decades. Epidemiological studies provide a somewhat consistent pattern indicating an increased risk for childhood leukemia and adult chronic lymphatic leukemia and possibly also for other leukemias and brain cancer. However, there is still no good candidate for a mechanism. Epidemiological studies have throughout the two decades been interpreted with great caution, and final evaluations as to carcinogenicity have been deferred. The reason for this carefulness may be the lack of knowledge about a plausible mechanism. The purpose of this paper is to discuss the process of weighing epidemiological data, experimental data, and other background information into a synthesis such that the evaluation can be based on all data combined. A Bayesian approach to this weighing is discussed along with some alternatives. The Bayesian approach provides a structure for the pooling of evidence and points out where subjective judgments come into play.
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http://dx.doi.org/10.1111/j.1749-6632.1999.tb08075.x | DOI Listing |
Surv Methodol
December 2024
Department of Statistical Science, 214a Old Chemistry Building, Duke University, Durham, NC 27708-0251.
When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993).
View Article and Find Full Text PDFEClinicalMedicine
February 2025
Department of Breast and Gynaecological Surgery, Institut Curie, Paris, France.
Background: Randomized clinical trials (RCTs) are fundamental to evidence-based medicine, but their real-world impact on clinical practice often remains unmonitored. Leveraging large-scale real-world data can enable systematic monitoring of RCT effects. We aimed to develop a reproducible framework using real-world data to assess how major RCTs influence medical practice, using two pivotal surgical RCTs in gynaecologic oncology as an example-the LACC (Laparoscopic Approach to Cervical Cancer) and LION (Lymphadenectomy in Ovarian Neoplasms) trials.
View Article and Find Full Text PDFHealth Serv Outcomes Res Methodol
October 2023
University of Florence, DiSIA and Florence Center for Data Science, and European University Institute, Department of Economics, Florence, Italy.
Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated.
View Article and Find Full Text PDFStat Med
February 2025
Department of Statistics, University of Connecticut, Storrs, Connecticut.
The use of mixed-effect models to understand the evolution of the human immunodeficiency virus (HIV) and the progression of acquired immune deficiency syndrome (AIDS) has been the cornerstone of longitudinal data analysis in recent years. However, data from HIV/AIDS clinical trials have several complexities. Some of the most common recurrences are related to the situation where the HIV viral load can be undetectable, and the measures of the patient can be registered irregularly due to some problems in the data collection.
View Article and Find Full Text PDFJ Biopharm Stat
January 2025
Department of Biostatistics, School of Medicine, Yokohama City University, Yokohama, Japan.
In the field of medicine, evaluating the diagnostic performance of new diagnostic methods can be challenging, especially in the absence of a gold standard. This study proposes a methodology for assessing the performance of diagnostic tests by estimating the posterior distribution of the score using latent class analysis, without relying on a gold standard. The proposed method utilizes Markov Chain Monte Carlo sampling to estimate the posterior distribution of the score, enabling a comprehensive evaluation of diagnostic test methods.
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