Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

Bayes Theorem and Protopathic Bias: Methodological Concerns When Addressing the Impact of Fetal Heart Rate Patterns on the Cesarean Section Rate

Over the last 30 years, the caesarean section rate has reached global epidemic proportions. This trend is driven by multiple factors, an important one of which is the use and inconsistent interpretation of the electronic fetal monitoring (EFM) system. Despite its introduction in the 1960s, the EFM has not definitively improved neonatal outcomes, yet it has since significantly contributed to a seven-fold increase in the caesarean section rate. As we attempt to reduce the caesarean rates in the developed world, we should consider focusing on areas that have garnered little attention in the literature, such as physician sensitization to the poor predictive power of the EFM and the research method biases that are involved in studying the abnormal heart rate patterns-umbilical cord pH relationship. Herein, we apply Bayes theorem to different clinical scenarios to illustrate the poor predictive power of the EFM, as well as shed light on the principle of protopathic bias, which affects the classification of research outcomes among studies addressing the effects of the EFM on caesarean rates. We propose and discuss potential solutions to the aforementioned considerations, which include the re-examination of guidelines with which we interpret fetal heart rate patterns and the development of noninvasive technologies that evaluate fetal pH in real time.

A causal inference framework for cancer cluster investigations using publicly available data

Often, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the US Centers for Disease Control and Prevention recommend that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach has several limitations that are well documented in the existing literature. In this paper we propose a novel causal inference framework for cancer cluster investigations, rooted in the potential outcomes framework. Assuming that a source of hazard representing a potential cause of increased cancer rates in the community is identified a priori, we focus our approach on a causal inference estimand which we call the causal SIR (cSIR). The cSIR is a ratio defined as the expected cancer incidence in the exposed population divided by the expected cancer incidence for the same population under the (counterfactual) scenario of no exposure. To estimate the cSIR we need to overcome two main challenges: 1) identify unexposed populations that are as similar as possible to the exposed one to inform estimation of the expected cancer incidence under the counterfactual scenario of no exposure, and 2) publicly available data on cancer incidence for these unexposed populations are often available at a much higher level of spatial aggregation (e.g. county) than what is desired (e.g. census block group). We overcome the first challenge by relying on matching. We overcome the second challenge by building a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired level of spatial aggregation. In simulations, our statistical approach was shown to provide dramatically improved results, i.e., less bias and better coverage, than the current approach to SIR analyses. We apply our proposed approach to investigate whether trichloroethylene vapor exposure has caused increased cancer incidence in Endicott, New York.

Comparative study on the efficacy of sodium hypochlorite, aqueous ozone, and peracetic acid in the elimination of Salmonella from cattle manure contaminated various surfaces supported by Bayesian analysis

Providing the dairy industry with an effective and safe disinfectant is considered a key step in improving the farm hygiene and biosecurity. Salmonella infection via foodborne transmission remains a major public health threat. The main objective of this study was therefore to characterize and compare the decontamination power of NaOCl, aqueous-O₃, and PAA against cattle manure based-Salmonella heavily contaminated various surfaces (plastic, nylon, rubber, and wood) using Bayesian analysis. In a crossover design, 14 strips of each material were randomly assigned between 3 groups, treatment (n = 6), positive-control (contaminated with feces-Salmonella mixture, but not exposed to disinfectants, n = 6), and negative control (laboratory blank, inoculated only with sterile water, n = 2). The strips were soaked in cattle manure inoculated with 10⁷–10⁸ of Salmonella Typhimurium-Choleraesuis (aSTC) and exposed to 50 mL of 200 ppm NaOCl, 9 ppm aqueous-O₃, or 400 ppm PAA for 4 minutes. Bayesian methods were used for analysis. On plastic and nylon surfaces, NaOCl, aqueous-O_3, or PAA reduce aSTC population to a safe level (>5.0-log₁₀) within 4 minutes. On rubber surface, PAA and aqueous-O₃ can produce a reduction in aSTC population 50% and 30% higher than NaOCl with posterior probabilities of 97% and 90%, respectively. However, PAA can produce reduction factor on wood surface 40% higher than aqueous-O₃ and NaOCl with posterior probabilities of 97% and 73%, respectively. We conclude that smooth surfaces were most effectively decontaminated. Peracetic acid of 400 ppm can provide an effective means for controlling Salmonella population heavily contaminated various surfaces in dairy operations. However, the safe residues and strong reactivity makes aqueous-O₃ and PAA attractive alternative disinfectants for improving farm hygiene and biosecurity.

Use of Bayesian statistics in drug development: Advantages and challenges

Mainly, two statistical methodologies are applicable to the design and analysis of clinical trials: frequentist and Bayesian. Most traditional clinical trial designs are based on frequentist statistics. In frequentist statistics prior information is utilized formally only in the design of a clinical trial but not in the analysis of the data. On the other hand, Bayesian statistics provide a formal mathematical method for combining prior information with current information at the design stage, during the conduct of the trial, and at the analysis stage. It is easier to implement adaptive trial designs using Bayesian methods than frequentist methods. The Bayesian approach can also be applied for post-marketing surveillance purposes and in meta-analysis. The basic tenets of good trial design are same for both Bayesian and frequentist trials. It has been recommended that the type of analysis to be used (Bayesian or frequentist) should be chosen beforehand. Switching to an analysis method that produces a more favorable outcome after observing the data is not recommended.