The roles, challenges, and merits of the p value

Exploring oral microbiome in oral squamous cell carcinoma across environment-associated sample types

The relationship between the oral microbiome and oral squamous cell carcinoma (OSCC) has been extensively investigated. Nonetheless, most previous studies were single-center, resulting in the absence of systematic evaluations. To address this gap, we performed a comprehensive meta-analysis on 1,255 samples from OSCC-related 16S rRNA gene data sets, representing a diverse range of OSCC phenotypes. It is recognized that the progression of cancer is related to the alterations in the microbiome among different phenotypes. Our findings revealed distinct microbiome characteristics among different sample types, with Biopsy (Bios) and Swab samples exhibiting significant differences between phenotypes. In Bios samples, the microbiomes of the Cancer group and the normal tissue adjacent to the tumor (NAT) group display a higher similarity, while both differ from the microbiome of the Fibroepithelial polyp (FEP) group. Moreover, the identified differential genera and pathways corresponded with these observations. We developed a diagnostic model using the random forest algorithm on Swab samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.918. Importantly, this model exhibited considerable effectiveness (AUC = 0.849) when applied to another sequencing platform. Taken together, our study provides a comprehensive overview of the oral microbiome during various OSCC progression stages, potentially enhancing early detection and treatment.IMPORTANCEThis study answers key questions regarding the universal microbial characteristics and comprehensive oral microbiome dynamics during oral squamous cell carcinoma (OSCC) progression. By integrating multiple data sets, we examine the following critical aspects: (1) Do different sample types harbor distinct microbial communities within the oral cavity? (2) Which sample types offer greater potential for investigating OSCC progression? (3) How are the oral microbiomes of the Cancer group, normal tissue adjacent to the tumor group, and Fibroepithelial polyp group related, and what is their potential association with OSCC development? (4) Can a diagnostic model based on microbial signatures effectively distinguish between Cancer and Health groups using Swab samples?

The null hypothesis significance test and the dichotomization of the p-value: Errare Humanum Est

Decision-making in healthcare is complex and needs to be based on the best scientific evidence. In this process, information derived from statistical analysis of data is crucial, which can be developed from either frequentist or Bayesian perspectives. When it comes to the frequentist field, the null hypothesis significance test (NHST) and its p-value is one of the most widely used techniques in different disciplines. However, NHST has been subjected to questioning from different academic points of view, which has led to it being considered as one of the causes of the so-called replicability crisis in science. In this review article, we provide a brief historical account of its development, summarize the underlying methods, describe some controversies and limitations, address misuse and misinterpretation, and finally give some scopes and reflections in the context of biomedical research.

Evaluation of a peer-led research best practices training for community health workers and promotoras

Introduction

Community health workers and promotoras (CHW/Ps) increasingly support research conducted in communities but receive variable or no training. We developed a culturally and linguistically tailored research best practices course for CHW/Ps that can be taken independently or in facilitated groups. The purpose of this study was to evaluate the facilitated training.

Methods

CHW/Ps were recruited from communities and partners affiliated with study sites in Michigan, Florida, and California. They participated in virtual or in-person training facilitated by a peer in English or Spanish and then completed a survey about their abilities (i.e., knowledge and skills for participating in research-related work) and perceptions of the training. Linear regression analyses were used to examine differences in training experience across several factors.

Results

A total of 394 CHW/Ps, mean age 41.6 ± 13.8 years, completed the training and survey (n = 275 English; 119 Spanish). Most CHW/Ps were female (80%), and 50% identified as Hispanic, Latino, or Spanish. Over 95% of CHW/Ps rated their abilities as improved after training; 98% agreed the course was relevant to their work and felt the training was useful. Small differences were observed between training sites.

Discussion

Most CHW/Ps rated the training positively and noted improved knowledge and skills for engaging in research-related work. Despite slight site differences, the training was well received, and CHW/Ps appreciated having a facilitator with experience working in community-based settings. This course offers a standard and scalable approach to training the CHW/P workforce. Future studies can examine its uptake and effect on research quality.

Ten quick tips for electrocardiogram (ECG) signal processing

The electrocardiogram (ECG) is a powerful tool to measure the electrical activity of the heart, and the analysis of its data can be useful to assess the patient's health. In particular, the computational analysis of electrocardiogram data, also called ECG signal processing, can reveal specific patterns or heart cycle trends which otherwise would be unnoticeable by medical experts. When performing ECG signal processing, however, it is easy to make mistakes and generate inflated, overoptimistic, or misleading results, which can lead to wrong diagnoses or prognoses and, in turn, could even contribute to bad medical decisions, damaging the health of the patient. Therefore, to avoid common mistakes and bad practices, we present here ten easy guidelines to follow when analyzing electrocardiogram data computationally. Our ten recommendations, written in a simple way, can be useful to anyone performing a computational study based on ECG data and eventually lead to better, more robust medical results.

Recycled Aggregate Integration for Enhanced Performance of Polymer Concrete

The objective of the research outlined in this paper is to propose an eco-friendly solution that simultaneously contributes to improving the characteristics of polymer composites. The analyzed solution entails the use of recycled aggregate from crushed concrete rubble. The authors conducted experiments to test the consistency, density, flexural strength, compressive strength, and microstructure of polymer concrete (PC) with different proportions of recycled aggregate (RA). It was found that PC with RA had a higher compressive strength, 96 MPa, than PC with natural aggregate, 89.1 MPa, owing to the formation of a double-layer shell of resin and calcium filler on the surface of porous RA grains. Using a resin with a lower viscosity could improve the performance of PC with RA by filling the cracks and penetrating deeper into the pores. RA is a valuable material for PC production, especially when it contains porous grains with poor mechanical properties, which are otherwise unsuitable for other applications. This article also highlights the environmental and economic benefits of using RA in PC, as it can reduce waste generation and natural resource consumption.

Reinterpretation of the results of randomized clinical trials

BACKGROUND

Randomized clinical trials (RCTs) shape our clinical practice. Several studies report a mediocre replicability rate of the studied RCTs. Many researchers believe that the relatively low replication rate of RCTs is attributed to the high p value significance threshold. To solve this problem, some researchers proposed using a lower threshold, which is inevitably associated with a decrease in the study power.

METHODS

The results of 22 500 RCTs retrieved from the Cochrane Database of Systematic Reviews (CDSR) were reinterpreted using 2 fixed p significance threshold (0.05 and 0.005), and a recently proposed flexible threshold that minimizes the weighted sum of errors in statistical inference.

RESULTS

With p < 0.05 criterion, 28.5% of RCTs were significant; p < 0.005, 14.2%; and p < flexible threshold, 9.9% (2/3 of significant RCTs based on p < 0.05 criterion, were found not significant). Lowering the p cut-off, although decreases the false-positive rate, is not generally associated with a lower weighted sum of errors; the false-negative rate increases (the study power decreases); important treatments may be left undiscovered. Accurate calculation of the optimal p value thresholds needs knowledge of the variance in each study arm, a posteriori.

CONCLUSIONS

Lowering the p value threshold, as it is proposed by some researchers, is not reasonable as it might be associated with an increase in false-negative rate. Using a flexible p significance threshold approach, although results in a minimum error in statistical inference, might not be good enough too because only a rough estimation may be calculated a priori; the data necessary for the precise computation of the most appropriate p significance threshold are only available a posteriori. Frequentist statistical framework has an inherent conflict. Alternative methods, say Bayesian methods, although not perfect, would be more appropriate for the data analysis of RCTs.

On the use of receiver operating characteristic curve analysis to determine the most appropriate p value significance threshold

BACKGROUND

p value is the most common statistic reported in scientific research articles. Choosing the conventional threshold of 0.05 commonly used for the p value in research articles, is unfounded. Many researchers have tried to provide a reasonable threshold for the p value; some proposed a lower threshold, eg, 0.005. However, none of the proposals has gained universal acceptance. Using the analogy between the diagnostic tests with continuous results and statistical inference tests of hypothesis, I wish to present a method to calculate the most appropriate p value significance threshold using the receiver operating characteristic curve (ROC) analysis.

RESULTS

As with diagnostic tests where the most appropriate cut-off values are different depending on the situation, there is no unique cut-off for the p significance threshold. Unlike the previous proposals, which mostly suggest lowering the threshold to a fixed value (eg, from 0.05 to 0.005), the most appropriate p significance threshold proposed here, in most instances, is much less than the conventional cut-off of 0.05 and varies from study to study and from statistical test to test, even within a single study. The proposed method provides the minimum weighted sum of type I and type II errors.

CONCLUSIONS

Given the perplexity involved in using the frequentist statistics in a correct way (dealing with different p significance thresholds, even in a single study), it seems that the p value is no longer a proper statistic to be used in our research; it should be replaced by alternative methods, eg, Bayesian methods.