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Arthroscopy : the Journal of... Apr 2021Despite great advances in our understanding of statistics, a focus on statistical significance and P values, or lack of significance and power, persists. Unfortunately,...
Despite great advances in our understanding of statistics, a focus on statistical significance and P values, or lack of significance and power, persists. Unfortunately, this dichotomizes research findings comparing differences between groups or treatments as either significant or not significant. This creates a false and incorrect sense of certainty. Statistics provide us a measure of the degree of uncertainty or random error in our data. To improve the way in which we communicate and understand our results, we must include in reporting a probability, or estimate, of our degree of certainty (or uncertainty). This will allow us to better determine the risks and benefits of a treatment or intervention. Approaches that allow us to estimate, account for, and report our degree of uncertainty include use of confidence intervals, P-value functions, and Bayesian inference (which incorporates prior knowledge in our analysis of new research data). Surprise values (S values, which convert P values to the number of successive identical results of flips of a fair coin) express outcomes in an intuitive manner less susceptible to dichotomizing results as significant or not significant. In the future, researchers may report P values (if they wish) but could go further and provide a confidence interval, draw a P-value function graph, or run a Bayesian analysis. Authors could calculate and report an S value. It is insufficient to mindlessly report results as significant versus not significant without providing a quantitative estimate of the uncertainty of the data.
Topics: Bayes Theorem; Confidence Intervals; Humans; Models, Statistical; Probability; Rotator Cuff; Uncertainty
PubMed: 33812509
DOI: 10.1016/j.arthro.2021.02.010 -
Zhong Nan Da Xue Xue Bao. Yi Xue Ban =... Feb 2023The number of gestational women has been increased in recent years, resulting in more adverse pregnancy outcomes. It is crucial to assess the coagulation function of...
OBJECTIVES
The number of gestational women has been increased in recent years, resulting in more adverse pregnancy outcomes. It is crucial to assess the coagulation function of pregnant women and to intervene in a timely manner. This study aims to analyze the influencing factors on thrombelastography (TEG) and explore the evaluation of TEG for gestational women.
METHODS
A retrospective study was conducted on 449 pregnant women who were hospitalized in the obstetrics department in Xiangya Hospital of Central South University from 2018 to 2020. We compared the changes on the TEG parameters among normal pregnant women between different age groups, different ingravidation groups, and different stages of pregnancy groups. The influence on TEG of hypertensive disorders in pregnancy (HDP) and gestational diabetes mellitus (GDM) as well as two diseases synchronization was explored.
RESULTS
Compared with the normal second trimester women, the R values and K values of TEG were increased, and α angle, CI values and LY30 values were decreased in third trimester women (all <0.05). Compared with normal group, the R values and CI values of TEG of the HDP group have significant difference (both <0.05). There were no significant difference of TEG between the GDM group, the HDP combined with GDM group and the normal group (all 0.05). Multiple linear regression analysis showed that the influencing factors for R value in TEG were weeks of gestation (<0.001) and mode of conception (<0.05), for α angle was weeks of gestation (<0.05), for MA value was mode of conception (<0.05), and for CI value was weeks of gestation (<0.05). The analysis of correlation between TEG with platelet (PLT) and coagulation routines represented that there was a correlation between TEG R values and activated partial thromboplastin time (APTT) (<0.01), and negative correlation between TEG CI values and APTT (<0.05). There was a negative correlation between TEG K values and FIB (<0.05). The correlation of α angle (<0.05), MA values (<0.01) and CI values (<0.05) with FIB were positive respectively.
CONCLUSIONS
The TEG parameters of 3 stages of pregnancy were different. The different ingravidation approach has effect on TEG. The TEG parameters were consistent with conventional coagulation indicators. The TEG can be used to screen the coagulation status of gestational women, recognize the abnormalities of coagulation and prevent the severe complication timely.
Topics: Female; Humans; Pregnancy; Thrombelastography; Blood Coagulation Tests; Retrospective Studies; Blood Coagulation; Blood Platelets; Diabetes, Gestational
PubMed: 36999466
DOI: 10.11817/j.issn.1672-7347.2023.210530 -
Clinical Pharmacology and Therapeutics Jun 2021Null hypothesis significance testing (NHST) with its benchmark P value < 0.05 has long been a stalwart of scientific reporting and such statistically significant... (Review)
Review
Null hypothesis significance testing (NHST) with its benchmark P value < 0.05 has long been a stalwart of scientific reporting and such statistically significant findings have been used to imply scientifically or clinically significant findings. Challenges to this approach have arisen over the past 6 decades, but they have largely been unheeded. There is a growing movement for using Bayesian statistical inference to quantify the probability that a scientific finding is credible. There have been differences of opinion between the frequentist (i.e., NHST) and Bayesian schools of inference, and warnings about the use or misuse of P values have come from both schools of thought spanning many decades. Controversies in this arena have been heightened by the American Statistical Association statement on P values and the further denouncement of the term "statistical significance" by others. My experience has been that many scientists, including many statisticians, do not have a sound conceptual grasp of the fundamental differences in these approaches, thereby creating even greater confusion and acrimony. If we let A represent the observed data, and B represent the hypothesis of interest, then the fundamental distinction between these two approaches can be described as the frequentist approach using the conditional probability pr(A | B) (i.e., the P value), and the Bayesian approach using pr(B | A) (the posterior probability). This paper will further explain the fundamental differences in NHST and Bayesian approaches and demonstrate how they can co-exist harmoniously to guide clinical trial design and inference.
Topics: Algorithms; Bayes Theorem; Data Interpretation, Statistical; Humans; Probability; Research Design
PubMed: 32748400
DOI: 10.1002/cpt.2004 -
Scientific Reports Feb 2022Recently Liu and Xie proposed a p-value combination test based on the Cauchy distribution (CCT). They showed that when the significance levels are small, CCT can control...
Recently Liu and Xie proposed a p-value combination test based on the Cauchy distribution (CCT). They showed that when the significance levels are small, CCT can control type I error rate and the resulting p-value can be simply approximated using a Cauchy distribution. One very special and attractive property of CCT is that it is applicable to situations where the p-values to be combined are dependent. However, in this paper, we show that under some conditions the commonly used MinP test is much more powerful than CCT. In addition, under some other situations, CCT is powerless at all. Therefore, we should use CCT with caution. We also proposed new robust p-value combination tests using a second MinP/CCT to combine the dependent p-values obtained from CCT and MinP applied to the original p-values. We call the new tests MinP-CCT-MinP (MCM) and CCT-MinP-CCT (CMC). We study the performance of the new tests by comparing them with CCT and MinP using comprehensive simulation study. Our study shows that the proposed tests, MCM and CMC, are robust and powerful under many conditions, and can be considered as alternatives of CCT or MinP.
Topics: Computer Simulation; Humans; Models, Statistical; Probability
PubMed: 35210502
DOI: 10.1038/s41598-022-07094-7 -
Physical Therapy Research 2022Clinical research based on epidemiological study designs requires a good understanding of statistical analysis. This paper discusses the common misconceptions of... (Review)
Review
Clinical research based on epidemiological study designs requires a good understanding of statistical analysis. This paper discusses the common misconceptions of p-values so that researchers and readers of research papers will be able to properly present and understand the results of null hypothesis significance testing (NHST). The p-values calculated by NHST are categorized as three different types: "significant at p <0.05," "significant at p <0.01," or "not significant." If specified, they may be written as p = 0.124. The 95% confidence interval (CI) of the supplementary statistics is presented regardless of the p-value, and the range of the CI is observed and discussed to determine whether the results are clinically valid. The effect size (ES), which is a measure of the magnitude of the effect, is also referenced and discussed. However, the ES should not be overestimated. It is important to examine the actual descriptive statistics and consider them comprehensively as much as possible. A high detection power of 80% or more indicates that NHST with high accuracy was applied. However, even when it falls below 80%, it is important to consider the limitations of the study, because the results are not completely useless.
PubMed: 36118788
DOI: 10.1298/ptr.R0019 -
Emergency Medicine International 2022To investigate the changes in thromboelastography (TEG) in patients with dyslipidemia to study its effect on the blood coagulation status.
PURPOSE
To investigate the changes in thromboelastography (TEG) in patients with dyslipidemia to study its effect on the blood coagulation status.
METHODS
131 patients hospitalized in Fujian Provincial Jinshan Hospital from January 2018 to December 2020 were selected, and 64 cases in the hyperlipidemia (HL) group and 67 cases in the non-HL group were set according to whether their blood lipids were abnormal. By measuring the changes of each parameter of TEG in patients, the relevant parameters value, value, angle, and MA value were calculated. And routine blood coagulation (PT, APTT, INR, FIB, and TT) and routine blood (platelet count) tests were performed on all study subjects to analyze the changes of each index of the coagulation function and each parameter of TED in both groups and explore the clinical value of TEG on HL diseases.
RESULTS
Compared with the non-HL group, and K values decreased, and angle and MA values increased in the HL group ( < 0.05). PT, APTT, and INR values decreased, and FIB values increased in the HL group compared with the nonhyperlipidemic group ( < 0.05). The TT levels were similar in the non-HL group and the HL group ( > 0.05). Compared with the non-HL group, PLT values decreased, and PDW and MPV values increased in the HL group ( < 0.05). value was positively correlated with APTT, = 0.373, =0.002. value was negatively correlated with PLT, = -0.399, =0.002. angle and MA values were positively correlated with PLT, = 0.319/0.475, =0.010/ < 0.001. The rest of the indexes did not correlate with each parameter of TEG significant correlation.
CONCLUSION
TEG can predict the hypercoagulability and hypocoagulability of blood by the changes of value, value, angle, and MA to evaluate the effect of hyperlipidemia on the coagulation status, which is important for guiding the adjustment of lipid-lowering, antithrombotic, and anticoagulation programs in patients with atherosclerosis combined with hyperlipidemia or postsurgery combined with hyperlipidemia.
PubMed: 35990371
DOI: 10.1155/2022/1927881 -
Veterinary and Comparative Oncology Sep 2022Statements such as 'trend towards' and 'tended to' in regards to 'statistical significance' are ambiguous and reflect the continued focus on proximity of p-values to...
Statements such as 'trend towards' and 'tended to' in regards to 'statistical significance' are ambiguous and reflect the continued focus on proximity of p-values to .05. By themselves, p-values do not provide a measure of effect size or other information needed to judge clinical importance. The goal of this study was to examine original research articles in the journal Veterinary and Comparative Oncology over 2 years, and describe the use of 'trend' statements. Articles were reviewed for 'trend' and 'tended to' statements, the number of statements made per paper, whether a p-value and/or 95% confidence interval was provided to accompany these statements, and what the p-value was (if provided). We noted 15.8% of articles included at least one 'trend' or 'tended to' statement to describe differences between groups. Specifically, 10.5% of articles used these statements to describe differences without providing the associated p-value, or where p > .05. Of the total 36 'trend' statements noted, six were not accompanied by p-values and eight were associated with p-values >.10. Similarly, four of the 16 'tended to' statements were not accompanied by p-values, and one was associated with a p-value >.10. These data reveal a similar emphasis on p-value proximity in veterinary oncology literature compared to that of human oncology literature. Furthermore, these findings highlight the ambiguity of 'trend' statements and support the development of additional guidelines (e.g., the de-emphasis of p-value proximity to an arbitrary threshold, and effect size reporting) for interpreting results in veterinary oncology research.
Topics: Animals; Humans; Medical Oncology
PubMed: 35274802
DOI: 10.1111/vco.12811 -
Proceedings of the National Academy of... Aug 2020In randomized experiments, Fisher-exact values are available and should be used to help evaluate results rather than the more commonly reported asymptotic values. One...
In randomized experiments, Fisher-exact values are available and should be used to help evaluate results rather than the more commonly reported asymptotic values. One reason is that using the latter can effectively alter the question being addressed by including irrelevant distributional assumptions. The Fisherian statistical framework, proposed in 1925, calculates a value in a randomized experiment by using the actual randomization procedure that led to the observed data. Here, we illustrate this Fisherian framework in a crossover randomized experiment. First, we consider the first period of the experiment and analyze its data as a completely randomized experiment, ignoring the second period; then, we consider both periods. For each analysis, we focus on 10 outcomes that illustrate important differences between the asymptotic and Fisher tests for the null hypothesis of no ozone effect. For some outcomes, the traditional value based on the approximating asymptotic Student's distribution substantially subceeded the minimum attainable Fisher-exact value. For the other outcomes, the Fisher-exact null randomization distribution substantially differed from the bell-shaped one assumed by the asymptotic test. Our conclusions: When researchers choose to report values in randomized experiments, 1) Fisher-exact values should be used, especially in studies with small sample sizes, and 2) the shape of the actual null randomization distribution should be examined for the recondite scientific insights it may reveal.
Topics: Cross-Over Studies; Data Interpretation, Statistical; Humans; Models, Statistical; Random Allocation; Randomized Controlled Trials as Topic; Research Personnel; Sample Size
PubMed: 32703808
DOI: 10.1073/pnas.1915454117 -
Multiple Sclerosis and Related Disorders Nov 2020There remains a pendulum swing to avoid p-values, but the binary, use or don't use p-values may be replacing one problem with another. This paper elaborates on the use... (Review)
Review
There remains a pendulum swing to avoid p-values, but the binary, use or don't use p-values may be replacing one problem with another. This paper elaborates on the use of p-values and effect sizes, points out their utility and reminds that the context of the question remains critically important. There is no single measure that provides the sole value of a study. The over or under interpretation of various measures is cautioned. Researchers need to keep in mind there is bio in biostatistics and statistics in biology and medicine. Both are needed to understand results in context.
PubMed: 33296983
DOI: 10.1016/j.msard.2020.102587 -
Entropy (Basel, Switzerland) Apr 2023Minimum Bayes factors are commonly used to transform two-sided -values to lower bounds on the posterior probability of the null hypothesis, in particular the bound...
Minimum Bayes factors are commonly used to transform two-sided -values to lower bounds on the posterior probability of the null hypothesis, in particular the bound -e·p·log(p). This bound is easy to compute and explain; however, it does not behave as a Bayes factor. For example, it does not change with the sample size. This is a very serious defect, particularly for moderate to large sample sizes, which is precisely the situation in which -values are the most problematic. In this article, we propose adjusting this minimum Bayes factor with the information to approximate an exact Bayes factor, not only when is a -value but also when is a pseudo--value. Additionally, we develop a version of the adjustment for linear models using the recent refinement of the Prior-Based BIC.
PubMed: 37190406
DOI: 10.3390/e25040618