Measurement and analysis of change in research scholars' knowledge and attitudes toward statistics after PhD coursework.

BACKGROUND: Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2-3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics. METHODS: One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2-3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought. RESULTS: Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2-3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively. CONCLUSION: The study concluded that the coursework program was beneficial for improving research scholars' knowledge and attitudes toward statistics. A refresher program 2-3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars' anxiety and attitudes toward statistics and its influence on learning outcomes.

The psychological reality of the learned "p < .05" boundary.

The .05 boundary within Null Hypothesis Statistical Testing (NHST) "has made a lot of people very angry and been widely regarded as a bad move" (to quote Douglas Adams). Here, we move past meta-scientific arguments and ask an empirical question: What is the psychological standing of the .05 boundary for statistical significance? We find that graduate students in the psychological sciences show a boundary effect when relating p-values across .05. We propose this psychological boundary is learned through statistical training in NHST and reading a scientific literature replete with "statistical significance". Consistent with this proposal, undergraduates do not show the same sensitivity to the .05 boundary. Additionally, the size of a graduate student's boundary effect is not associated with their explicit endorsement of questionable research practices. These findings suggest that training creates distortions in initial processing of p-values, but these might be dampened through scientific processes operating over longer timescales.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations.

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

Perceptions of attitudes toward statistics among medical undergraduates: insights from a regional medical college in China.

BACKGROUND: Among Chinese medical students, medical statistics is often perceived as a formidable subject. While existing research has explored the attitudes of Chinese postgraduate medical students towards statistics and its impact on academic performance, there is a scarcity of studies examining the attitudes of Chinese medical undergraduates on this subject. This study endeavors to scrutinize the attitudes of Chinese medical undergraduates towards statistics, assessing their ramifications on learning achievements, and delving into the influence of demographic factors. METHODS: 1266 medical undergraduates participated in this study, completing a questionnaire that included SATS-36 and additional queries. Furthermore, an examination was administered at the end of the medical statistics course. The analysis encompassed the SATS score and exam scores, examining both the overall participant population and specific demographic subgroups. RESULTS: Undergraduate medical students generally exhibit a favorable disposition towards statistics concerning Affect, Cognitive Competence, and Value components, yet harbor less favorable sentiments regarding the Difficulty component of SATS-36, aligning with previous research findings. In comparison to their postgraduate counterparts, undergraduates display heightened enthusiasm for medical statistics. However, they demonstrate a lower cognitive capacity in statistics and tend to underestimate both the value and difficulty of learning statistics. Despite these disparities, undergraduate medical students express a genuine interest in statistics and exhibit a strong dedication to mastering the subject. It is noteworthy that students' attitudes toward statistics may be influenced by their major and gender. Additionally, there exists a statistically significant positive correlation between learning achievement and the Affect, Cognitive Competence, Value, Interest, and Effort components of the SATS-36, while a negative correlation is observed with the Difficulty component. CONCLUSION: Educators should carefully consider the influence of attitudes toward statistics, especially the variations observed among majors and genders when formulating strategies and curricula to enhance medical statistics education.

Significance of plasma relaxin-2 levels in patients with primary hypertension and type 2 diabetes mellitus.

BACKGROUND: This study aimed to evaluate plasma relaxin­2 (RLN-2) levels in patients with arterial hypertension (AH) and their relationships with clinical and laboratory parameters. METHODS: The study involved 106 hypertensive patients, including 55 with type 2 diabetes mellitus (T2DM), and 30 control subjects. Plasma RLN-2 levels were measured using an enzyme-linked immunosorbent assay kit. RESULTS: RLN-2 levels were reduced in patients with AH compared to healthy volunteers (p < 0.001), and hypertensive patients with T2DM had lower RLN-2 levels than those without impaired glucose metabolism (p < 0.001). RLN­2 was negatively correlated with systolic blood pressure (SBP) (p < 0.001) and anthropometric parameters such as body mass index (BMI; p = 0.027), neck (p = 0.045) and waist (p = 0.003) circumferences, and waist-to-hip ratio (p = 0.011). RLN­2 also had inverse associations with uric acid levels (p = 0.019) and lipid profile parameters, particularly triglycerides (p < 0.001) and non-HDL-C/HDL­C (p < 0.001), and a positive relationship with HDL­C (p < 0.001). RLN­2 was negatively associated with glucose (p < 0.001), insulin (p = 0.043), HbA1c (p < 0.001), and HOMA-IR index (p < 0.001). Univariate binary logistic regression identified RLN­2 as a significant predictor of impaired glucose metabolism (p < 0.001). CONCLUSIONS: Decreased RLN-2 levels in patients with AH and T2DM and established relationships of RLN­2 with SBP and parameters of glucose metabolism and lipid profile suggest a diagnostic role of RLN­2 as a biomarker for AH with T2DM.

Good Statistical Practice-development of tailored Good Clinical Practice training for statisticians.

BACKGROUND: Statisticians are fundamental in ensuring clinical research, including clinical trials, are conducted with quality, transparency, reproducibility and integrity. Good Clinical Practice (GCP) is an international quality standard for the conduct of clinical trials research. Statisticians are required to undertake training on GCP but existing training is generic and, crucially, does not cover statistical activities. This results in statisticians undertaking training mostly unrelated to their role and variation in awareness and implementation of relevant regulatory requirements with regards to statistical conduct. The need for role-relevant training is recognised by the UK NHS Health Research Authority and the Medicines and Healthcare products Regulatory Agency (MHRA). METHODS: The Good Statistical Practice (GCP for Statisticians) project was instigated by the UK Clinical Research Collaboration (UKCRC) Registered Clinical Trials Unit (CTU) Statisticians Operational Group and funded by the National Institute for Health and Care Research (NIHR), to develop materials to enable role-specific GCP training tailored to statisticians. Review of current GCP training was undertaken by survey. Development of training materials were based on MHRA GCP. Critical review and piloting was conducted with UKCRC CTU and NIHR researchers with comment from MHRA. Final review was conducted through the UKCRC CTU Statistics group. RESULTS: The survey confirmed the need and desire for the development of dedicated GCP training for statisticians. An accessible, comprehensive, piloted training package was developed tailored to statisticians working in clinical research, particularly the clinical trials arena. The training materials cover legislation and guidance for best practice across all clinical trial processes with statistical involvement, including exercises and real-life scenarios to bridge the gap between theory and practice. Comprehensive feedback was incorporated. The training materials are freely available for national and international adoption. CONCLUSION: All research staff should have training in GCP yet the training undertaken by most academic statisticians does not cover activities related to their role. The Good Statistical Practice (GCP for Statisticians) project has developed and extensively piloted new, role-specific, comprehensive, accessible GCP training tailored to statisticians working in clinical research, particularly the clinical trials arena. This role-specific training will encourage best practice, leading to transparent and reproducible statistical activity, as required by regulatory authorities and funders.

Petty's instruments: the Down Survey, territorial natural history and the birth of statistics.

William Petty's work has usually been regarded as an epistemic break in the history of statistical and politico-economic thought. In this paper, I argue that Petty's statistical notions stemmed from the natural-historical techniques he originally implemented to manage the Down Survey. Following Bacon, who viewed the description of trades as a paramount branch of natural history, Petty approached the art of surveying itself as an object of natural-historical analysis. He partitioned the surveying work into individual tasks and implemented a meticulous division of labour, employing hundreds of disbanded soldiers as surveyors and using questionnaires to calibrate the responses of his 'instruments', as he called his specialized workers. By borrowing these methods from natural history to organize surveying work, Petty was able to conceptualize Ireland as a political body defined by tables of aggregate data. I then compare the Down Survey with John Graunt's observations on the bills of mortality to show that both are representative of a particular style of natural history, aimed at describing the natural and political state of a circumscribed territory. I close by considering other manifestations of 'territorial natural history', indicating a continuity between this research tradition and the appearance of statistics in the British Isles.

Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials.

BACKGROUND: Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians. RESULTS: It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered. CONCLUSION: Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.

Limitation of permutation-based differential correlation analysis.

The comparison of biological systems, through the analysis of molecular changes under different conditions, has played a crucial role in the progress of modern biological science. Specifically, differential correlation analysis (DCA) has been employed to determine whether relationships between genomic features differ across conditions or outcomes. Because ascertaining the null distribution of test statistics to capture variations in correlation is challenging, several DCA methods utilize permutation which can loosen parametric (e.g., normality) assumptions. However, permutation is often problematic for DCA due to violating the assumption that samples are exchangeable under the null. Here, we examine the limitations of permutation-based DCA and investigate instances where the permutation-based DCA exhibits poor performance. Experimental results show that the permutation-based DCA often fails to control the type I error under the null hypothesis of equal correlation structures.

The Lure of Beauty: People Select Representations of Statistical Information Largely Based on Attractiveness, Not Comprehensibility.

OBJECTIVE: People differ in whether they understand graphical or numerical representations of statistical information better. However, assessing these skills is often not feasible when deciding which representation to select or use. This study investigates whether people choose the representation they understand better, whether this choice can improve risk comprehension, and whether results are influenced by participants' skills (graph literacy and numeracy). METHODS: In an experiment, 160 participants received information about the benefits and side effects of painkillers using either a numerical or a graphical representation. In the "no choice" condition, the representation was randomly assigned to each participant. In the "choice" condition, participants could select the representation they would like to receive. The study assessed gist and verbatim knowledge (immediate comprehension and recall), accessibility of the information, attractiveness of the representation, as well as graph literacy and numeracy. RESULTS: In the "choice" condition, most (62.5%) chose the graphical format, yet there was no difference in graph literacy or numeracy (nor age or gender) between people who chose the graphical or the numerical format. Whereas choice slightly increased verbatim knowledge, it did not improve gist or overall knowledge compared with random assignment. However, participants who chose a representation rated the representation as more attractive, and those who chose graphs rated them as more accessible than those without a choice. LIMITATIONS: The sample consisted of highly educated undergraduate students with higher graph literacy than the general population. The task was inconsequential for participants in terms of their health. CONCLUSIONS: When people can choose between representations, they fail to identify what they comprehend better but largely base that choice on how attractive the representation is for them. HIGHLIGHTS: People differ systematically in whether they understand graphical or numerical representations of statistical information better. However, assessing these underlying skills to get the right representation to the right people is not feasible in practice. A simple and efficient method to achieve this could be to let people choose among representations themselves.However, our study showed that allowing participants to choose a representation (numerical v. graphical) did not improve overall or gist knowledge compared with determining the representation randomly, even though it did slightly improve verbatim knowledge.Rather, participants largely chose the representation they found more attractive. Most preferred the graphical representation, including those with low graph literacy.It would therefore be important to develop graphical representations that are not only attractive but also comprehensible even for people with low graph literacy.

"Selection of statistical tests influences the interpretation of a study outcome" a comment on "Giriraja KV, Bhatnagar SK, Tomlinson L, Sancilio F. An open-label, multicenter, phase 2 study of a food enriched with docosahexaenoic acid in adults with sickle cell disease. Prostaglandins Leukot Essent Fatty Acids. 2023 Jun; 193:102574. Doi: 10.1016/j.plefa.2023.102574. Epub 2023 Apr 7. PMID: 37121179".

Parametric tests such as t-tests require a normal distribution of data. However, the determination of normal distribution may not be conclusive while dealing with a small sample size. Non-parametric tests such as Wilcoxon tests may be used in this situation, as these tests do not require normal distribution.

Potencia estadística en investigación médica. ¿Qué postura tomar cuando los resultados de la investigación son significativos? / Statistical power in medical research. What stance should be taken when the research results are significant?

Cuando el investigador pide subvención y autorización a entidades financieras para llevar a cabo su proyecto, entre las primeras cuestiones que le plantean está: ¿qué potencia estadística tiene este estudio que usted propone? Si el investigador responde, por ejemplo, el 90%, y el evaluador se da por satisfecho, es seguro que no conoce realmente el tema. La potencia de un estudio no es única. Depende de determinados parámetros y ocurre que, en la mayoría de los casos, variando ligeramente los valores de esos parámetros, la potencia toma un valor aceptable. Si no es así, y a pesar de ello se lleva a cabo el estudio, y sus resultados son muy significativos, no ha lugar a cuestionar el éxito encontrado argumentando que el estudio tenía poca potencia. Tan sólo es momento de celebrarlo. (AU)

When researchers request funding and authorisation from financial institutions to carry out their project, one of the first questions they are asked is: what is the statistical power of the study you are proposing? If the researcher answers, for example, 90%, and the evaluator is satisfied, it is certain that he/she is not really familiar with the subject. The power of a study is not unique. It depends on certain parameters and what happens is that, in most cases, by introducing a slight variation in the values of these parameters, the power takes on an acceptable value. If this is not the case and the study is carried out anyway, and its results are very significant, there is no room to question its success by arguing that the power of the study was very low. It is just the time to celebrate. (AU)

Nonparametric item response models: a comparison on recovering true scores / Modelos de resposta ao item não-paramétricos: comparando a recuperação de escores verdadeiros / Modelos de respuesta al item no-paramétricos: comparando la recuperación de puntuaciones verdaderas

Nonparametric procedures are used to add flexibility to models. Three nonparametric item response models have been proposed, but not directly compared: the Kernel smoothing (KS-IRT); the Davidian-Curve (DC-IRT); and the Bayesian semiparametric Rasch model (SP-Rasch). The main aim of the present study is to compare the performance of these procedures in recovering simulated true scores, using sum scores as benchmarks. The secondary aim is to compare their performances in terms of practical equivalence with real data. Overall, the results show that, apart from the DC-IRT, which is the model that performs the worse, all the other models give results quite similar to those when sum scores are used. These results are followed by a discussion with practical implications and recommendations for future studies.(AU)

Procedimentos não paramétricos são usados para adicionar flexibilidade aos modelos. Três modelos não paramétricos de resposta ao item foram propostos, mas não comparados diretamente: o Kernel smoothing (KS-IRT); a Curva Davidiana (DC-IRT); e o modelo semiparamétrico Rasch Bayesiano (SP-Rasch). O objetivo principal do presente estudo é comparar o desempenho desses procedimentos na recuperação de escores verdadeiros simulados, utilizando escores de soma como benchmarks. O objetivo secundário é comparar seus desempenhos em termos de equivalência prática com dados reais. De forma geral, os resultados mostram que, além do DC-IRT, que é o modelo que apresenta o pior desempenho, todos os outros modelos apresentam resultados bastante semelhantes aos de quando se usam somatórios. Esses resultados são seguidos de uma discussão com implicações práticas e recomendações para estudos futuros.(AU)

Se utilizan procedimientos no paramétricos para agregar flexibilidad a los modelos. Se propusieron tres modelos de respuesta al ítem no paramétricos, pero no se compararon directamente: Kernel smoothing (KS-IRT); la curva davidiana (DC-IRT); y el modelo bayesiano de Rasch semiparamétrico (SP-Rasch). El objetivo principal del presente estudio es comparar el desempeño de estos procedimientos en la recuperación de puntajes verdaderos simulados, utilizando puntajes de suma como puntos de referencia. El objetivo secundario es comparar su desempeño en términos de equivalencia práctica con datos reales. En general, los resultados muestran que, a excepción de DC-IRT, que es el modelo con peor desempeño, todos los otros modelos presentan resultados bastante similares a los obtenidos cuando se utilizan sumatorios. Estos resultados son seguidos por una discusión con implicaciones prácticas y recomendaciones para estudios futuros.(AU)

[Statistical power in medical research. What stance should be taken when the research results are significant?] / Potencia estadística en investigación médica. ¿Qué postura tomar cuando los resultados de la investigación son significativos?

When researchers request funding and authorisation from financial institutions to carry out their project, one of the first questions they are asked is: what is the statistical power of the study you are proposing? If the researcher answers, for example, 90%, and the evaluator is satisfied, it is certain that he/she is not really familiar with the subject. The power of a study is not unique. It depends on certain parameters and what happens is that, in most cases, by introducing a slight variation in the values of these parameters, the power takes on an acceptable value. If this is not the case and the study is carried out anyway, and its results are very significant, there is no room to question its success by arguing that the power of the study was very low. It is just the time to celebrate.

TITLE: Potencia estadística en investigación médica. ¿Qué postura tomar cuando los resultados de la investigación son significativos?Cuando el investigador pide subvención y autorización a entidades financieras para llevar a cabo su proyecto, entre las primeras cuestiones que le plantean está: ¿qué potencia estadística tiene este estudio que usted propone? Si el investigador responde, por ejemplo, el 90%, y el evaluador se da por satisfecho, es seguro que no conoce realmente el tema. La potencia de un estudio no es única. Depende de determinados parámetros y ocurre que, en la mayoría de los casos, variando ligeramente los valores de esos parámetros, la potencia toma un valor aceptable. Si no es así, y a pesar de ello se lleva a cabo el estudio, y sus resultados son muy significativos, no ha lugar a cuestionar el éxito encontrado argumentando que el estudio tenía poca potencia. Tan sólo es momento de celebrarlo.