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Background: The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes. Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed. Results: Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making. Conclusions: This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies.

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Adequate and transparent reporting is necessary for critically appraising research. Yet, evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-empaneled a group of authors to develop methodological and statistical reporting guidelines identifying the minimum information needed to document and evaluate observational studies and clinical trials in oral health: the OHstat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The final version was subsequently approved by the Task Force in September 2021, submitted for journal review in 2022, and revised in 2023. The checklist consists of 48 guidelines: 5 for introductory information, 17 for methods, 13 for statistical analysis, 6 for results, and 7 for interpretation; 7 are specific to clinical trials. Each of these guidelines identifies relevant information, explains its importance, and often describes best practices. The checklist was published in multiple journals. The article was published simultaneously in JDR Clinical and Translational Research, the Journal of the American Dental Association, and the Journal of Oral and Maxillofacial Surgery. Completed checklists should accompany manuscripts submitted for publication to these and other oral health journals to help authors, journal editors, and reviewers verify that the manuscript provides the information necessary to adequately document and evaluate the research.

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Adequate and transparent reporting is necessary for critically appraising published research. Yet, ample evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-identified the minimum information needed to report and evaluate observational studies and clinical trials in oral health: the OHStat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The guidelines were subsequently revised by the Task Force's writing group. The guidelines draw heavily from the Consolidated Standards for Reporting Trials (CONSORT), Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and CONSORT harms guidelines and incorporate the SAMPL guidelines for reporting statistics, the CLIP principles for documenting images, and the GRADE indicating the quality of evidence. The guidelines also recommend reporting estimates in clinically meaningful units using confidence intervals, rather than relying on P values. In addition, OHStat introduces 7 new guidelines that concern the text itself, such as checking the congruence between abstract and text, structuring the discussion, and listing conclusions to make them more specific. OHStat does not replace other reporting guidelines; it incorporates those most relevant to dental research into a single document. Manuscripts using the OHStat guidelines will provide more information specific to oral health research.

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Adequate and transparent reporting is necessary for critically appraising published research. Yet, ample evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-identified the minimum information needed to report and evaluate observational studies and clinical trials in oral health: the OHStat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The guidelines were subsequently revised by the Task Force's writing group. The guidelines draw heavily from the Consolidated Standards for Reporting Trials (CONSORT), Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and CONSORT harms guidelines and incorporate the SAMPL guidelines for reporting statistics, the CLIP principles for documenting images, and the GRADE indicating the quality of evidence. The guidelines also recommend reporting estimates in clinically meaningful units using confidence intervals, rather than relying on P values. In addition, OHStat introduces 7 new guidelines that concern the text itself, such as checking the congruence between abstract and text, structuring the discussion, and listing conclusions to make them more specific. OHStat does not replace other reporting guidelines; it incorporates those most relevant to dental research into a single document. Manuscripts using the OHStat guidelines will provide more information specific to oral health research.

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Adequate and transparent reporting is necessary for critically appraising research. Yet, evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-empaneled a group of authors to develop methodological and statistical reporting guidelines identifying the minimum information needed to document and evaluate observational studies and clinical trials in oral health: the OHstat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The final version was subsequently approved by the Task Force in September 2021, submitted for journal review in 2022, and revised in 2023. The checklist consists of 48 guidelines: 5 for introductory information, 17 for methods, 13 for statistical analysis, 6 for results, and 7 for interpretation; 7 are specific to clinical trials. Each of these guidelines identifies relevant information, explains its importance, and often describes best practices. The checklist was published in multiple journals. The article was published simultaneously in JDR Clinical and Translational Research, the Journal of the American Dental Association, and the Journal of Oral and Maxillofacial Surgery. Completed checklists should accompany manuscripts submitted for publication to these and other oral health journals to help authors, journal editors, and reviewers verify that the manuscript provides the information necessary to adequately document and evaluate the research.

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Adequate and transparent reporting is necessary for critically appraising published research. Yet, ample evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-identified the minimum information needed to report and evaluate observational studies and clinical trials in oral health: the OHStat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The guidelines were subsequently revised by the Task Force's writing group. The guidelines draw heavily from the Consolidated Standards for Reporting Trials (CONSORT), Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and CONSORT harms guidelines and incorporate the SAMPL guidelines for reporting statistics, the CLIP principles for documenting images, and the GRADE indicating the quality of evidence. The guidelines also recommend reporting estimates in clinically meaningful units using confidence intervals, rather than relying on P values. In addition, OHStat introduces 7 new guidelines that concern the text itself, such as checking the congruence between abstract and text, structuring the discussion, and listing conclusions to make them more specific. OHStat does not replace other reporting guidelines; it incorporates those most relevant to dental research into a single document. Manuscripts using the OHStat guidelines will provide more information specific to oral health research.

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BACKGROUND: Normalization, as a pre-processing step, can significantly affect the resolution of machine learning analysis for microbiome studies. There are countless options for normalization scheme selection. In this study, we examined compositionally aware algorithms including the additive log ratio (alr), the centered log ratio (clr), and a recent evolution of the isometric log ratio (ilr) in the form of balance trees made with the PhILR R package. We also looked at compositionally naïve transformations such as raw counts tables and several transformations that are based on relative abundance, such as proportions, the Hellinger transformation, and a transformation based on the logarithm of proportions (which we call "lognorm"). RESULTS: In our evaluation, we used 65 metadata variables culled from four publicly available datasets at the amplicon sequence variant (ASV) level with a random forest machine learning algorithm. We found that different common pre-processing steps in the creation of the balance trees made very little difference in overall performance. Overall, we found that the compositionally aware data transformations such as alr, clr, and ilr (PhILR) performed generally slightly worse or only as well as compositionally naïve transformations. However, relative abundance-based transformations outperformed most other transformations by a small but reliably statistically significant margin. CONCLUSIONS: Our results suggest that minimizing the complexity of transformations while correcting for read depth may be a generally preferable strategy in preparing data for machine learning compared to more sophisticated, but more complex, transformations that attempt to better correct for compositionality. Video Abstract.

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Statistical hypothesis testing compares the significance probability value and the significance level value to determine whether or not to reject the null hypothesis. This concludes "significant or not significant." However, since this process is a process of statistical hypothesis testing, the conclusion of "statistically significant or not statistically significant" is more appropriate than the conclusion of "significant or not significant." Also, in many studies, the significance level is set to 0.05 to compare with the significance probability value, p-value. If the p-value is less than 0.05, it is judged as "significant," and if the p-value is greater than 0.05, it is judged as "not significant." However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05. In a statistical hypothesis test, the conclusion depends on the setting of the significance level value, so the researcher must carefully set the significance level value. In this study, the stages of statistical hypothesis testing were examined in detail, and the exact conclusions accordingly and the contents that should be considered carefully when interpreting them were mentioned with emphasis on statistical hypothesis testing and significance level. In 11 original articles published in the Journal of Lipid and Atherosclerosis in 2022, the interpretation of hypothesis testing and the contents of the described conclusions were reviewed from the perspective of statistical hypothesis testing and significance level, and the content that I would like to be supplemented was mentioned.

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Oral conditions represent a critical public health challenge, and together with descriptive and predictive epidemiology, causal inference has a crucial role in developing and testing preventive oral health interventions. By identifying not just correlations but actual causes of disease, causal inference may quantify the average effect of interventions and guide policies. Although authors are not usually explicit about it, most oral health studies are guided by causal questions. However, methodological deficiencies limit their interpretability and the implementation of their findings. This manuscript is a call to action on the use of causal inference in oral research. Its application starts with asking theoretically sound questions and being explicit about causal relationships, defining the estimates to evaluate, and measuring them properly. Beyond promoting causal analytical approaches, we emphasize the need for more causal thinking to promote thoughtful research questions and the use of appropriate methods to answer them. Causal inference relies on the plausibility of assumptions underlying the data analysis and the quality of the data, and we argue that high-quality observational studies can be used to estimate average causal effects. Although individual efforts to embrace causal inference in dentistry are essential, they will not yield substantial results if not led by a systematic and structural change in the field. We urge scientific societies, funding bodies, dental schools, and journals to promote transparency in research, causal thinking, and causal inference projects to move the field toward more meaningful studies. It is also time for researchers to move forward and connect with the community, co-produce investigations and translate their findings, and engage in interventions that impact public health. We conclude by highlighting the importance of triangulating results from different data sources and methods to support causal inference and inform decision-making on interventions to effectively improve population oral health.

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Resumo O objetivo deste estudo foi analisar a literatura científica da área de saúde bucal coletiva quanto ao cálculo, apresentação e discussão do tamanho do efeito em estudos observacionais. A literatura cientifica na área (2015 a 2019) foi analisada quanto: a) informações gerais (periódico e diretrizes aos autores, número de variáveis e desfechos), b) objetivo e coerência com o cálculo amostral apresentado; c) tamanho do efeito (apresentação, medida utilizada e coerência com a discussão dos dados e conclusão). Foram analisados 123 artigos, de 66 periódicos. A maioria dos artigos avaliados apresenta um único desfecho (74%) e não menciona a realização de cálculo amostral (69,9%). Dentre os que realizaram, para 70,3% havia coerência entre o cálculo amostral utilizado e o objetivo. Apenas 3,3% dos artigos mencionam o termo tamanho do efeito e 24,4% não o consideram na discussão dos resultados, apesar de terem calculado. A regressão logística foi a metodologia estatística mais utilizada (98,4%) e o Odds Ratio a medida de tamanho do efeito mais utilizada (94,3%), embora não tenha sido citada e discutida como uma medida de tamanho do efeito na maioria dos estudos (96,7%). Os pesquisadores, em sua maioria, restringiram a discussão dos resultados apenas à significância estatística encontrada nas associações testadas.

Abstract The objective of this study was to analyze the scientific literature in public oral health regarding calculation, presentation, and discussion of the effect size in observational studies. The scientific literature (2015 to 2019) was analyzed regarding: a) general information (journal and guidelines to authors, number of variables and outcomes), b) objective and consistency with sample calculation presentation; c) effect size (presentation, measure used and consistency with data discussion and conclusion). A total of 123 articles from 66 journals were analyzed. Most articles analyzed presented a single outcome (74%) and did not mention sample size calculation (69.9%). Among those who did, 70.3% showed consistency between sample calculation used and the objective. Only 3.3% of articles mentioned the term effect size and 24.4% did not consider that in the discussion of results, despite showing effect size calculation. Logistic regression was the most commonly used statistical methodology (98.4%) and Odds Ratio was the most commonly used effect size measure (94.3%), although it was not cited and discussed as an effect size measure in most studies (96.7%). It could be concluded that most researchers restrict the discussion of their results only to the statistical significance found in associations under study.

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INTRODUCTION: Randomized controlled trials (RCTs) that conduct subgroup analyses have the potential to provide information on treatment decisions in specific groups of patients from heterogeneous populations. Although we understand several factors can modify the incidence of venous thromboembolism (VTE) and the benefit/risk ratio of anticoagulation treatments, further evidence is warranted to show the heterogeneity of treatment effects in different subgroups of patients. AIMS: The primary purpose was to evaluate the appropriateness and interpretation of subgroup analysis performed on VTE RCTs reporting pharmacological interventions. MATERIALS AND METHODS: A systematic review of RCTs published between January 2017 and January 2022 was conducted. Claims of subgroup effects were evaluated with predefined criteria. High-quality claims of subgroup effect were further analyzed and discussed. RESULTS: Overall, 28 RCTs with a generally low bias risk were included. The purposes of the treatments included pharmacologic thromboprophylaxis (17), therapeutic dose anticoagulation (9), and catheter-directed pharmacologic thrombolysis (2). The evaluated subgroup analyses generally presented: a high number of subgroup analyses reported, a lack of prespecification, and a lack of usage of statistical tests for interaction. The authors reported 13 claims of subgroup effect; only two were considered potentially reliable to represent heterogeneity in the direction or magnitude of treatment effect. CONCLUSIONS: Subgroup analyses of VTE RCTs reporting pharmacologic interventions are generally methodologically poor. Most claims of subgroup effect did not meet critical criteria and lacked credibility. Clinicians in this field may proceed with scepticism when assessing claims of subgroup effects due to methodological concerns and misleading interpretations.

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Background: The direct method for reference interval (RI) estimating is limited due to the requirement of resources, difficulties in defining a non-diseased population, or ethical problems in obtaining samples. We estimated the RI for inflammatory biomarkers using an indirect method (RII). Methods: C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and presepsin (PSEP) data of patients visiting a single hospital were retrieved from April 2009 to April 2021. Right-skewed data were transformed using the Box-Cox transformation method. A mixed population of non-diseased and diseased distributions was assumed, followed by latent profile analysis for the two classes. The intersection point of the distribution curve was estimated as the RI. The influence of measurement size was evaluated as the ratio of abnormal values and adjustment (n×bandwidth) of the distribution curve. Results: The RIs estimated by the proposed RII method (existing method) were as follows: CRP, 0-4.1 (0-4.7) mg/L; ESR, 0-10.2 (0-15) mm/hr and PSEP, 0-411 (0-300) pg/mL. Measurement sizes ≥2,500 showed stable results. An abnormal-to-normal value ratio of 0.5 showed the most accurate result for CRP. Adjustment values ≤5 or >5 were applicable for a measurement size <25,000 or ≥25,000, respectively. Conclusions: The proposed RII method could provide additional information for RI verification or estimation with some limitations.

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BACKGROUND: Statistical methods (SM) are a ubiquitous tool in research. This study aimed to review SM used in original article published in the Archives of Iranian Medicine (AIM) and assess their effect on article acceptance period. METHODS: The original articles published in the period 2015-2019 from volumes 18 to 22 and issues 1 to 12 of the AIM were reviewed and six items such as SM, study design, statistical population, sample size, software and acceptance period were extracted. Mean (SD), frequency (percentage) and multiple response analysis (MRA) were used for description. The Kruskal-Wallis test and Spearman correlation coefficient were used for data analysis in SPSS 26 with significance level at 5%. RESULTS: During the study period, 423 original articles were reviewed. The statistical population in most of them was patients (38.8% and 164 articles), and most studies (51.5% and 218 articles) had a sample size of less than 500 people. The study design in most of the articles was analytical-observational (55.1% and 233 articles), and 79.7% (337 articles) used SPSS for data analysis. The median (IQR) acceptance period was 194 (134.25). MRA results showed that the highest rate of use of SM was related to descriptive statistics (277 articles, 30.3%) and Chi square test (130 articles, 14.2%). In the last two years, the acceptance period had a declining trend. There was no significant relation between mentioned variables and acceptance period (P>0.05). CONCLUSION: Contrary to the researchers' misconceptions, the acceptance period was not affected by SM, study design, statistical population, sample size, or type of software.

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Researchers who use the results of statistical analyses to draw conclusions about collected data must write a statistical analysis section in their manuscript. Describing statistical analyses in precise detail is as important as presenting the dosages of drugs and methodology of interventions. It is also essential for scientific accuracy and transparency in scientific research. We evaluated the quality of the statistical analysis sections of clinical research articles published in the Korean Journal of Anesthesiology between February 2020 and February 2021. Using a Likert scale where 1, 2, and 3 represented "not described at all," "partially described," and "fully described," respectively, the following 6 items were assessed: 1) stating of the statistical analysis methods used, 2) rationale for and detailed description of the statistical analysis methods used, 3) parameters derived from the statistical analyses, 4) type and version of the statistical software package used, 5) significance level, and 6) sidedness of the test (one-sided vs. two-sided). The first 3 items evaluate issues directly related to the statistical analysis methods used and last 3 are indirectly related items. In all the included articles, the statistical analysis methods used were stated (score of 3). However, only 4 articles (12.9%) fully described the sidedness of the test (score of 3). Authors tend not to describe the sidedness of statistical analysis tests in the methodology section of clinical research articles. It is essential that the sidedness be described in research studies.

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OBJECTIVE: Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction. DESIGN: A retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland. SETTING: Fifteen primary health care centers of the PHCCA. SUBJECTS: All patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses. MAIN OUTCOME MEASURES: Sensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier. RESULTS: The classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses. CONCLUSION: In a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction.KeypointsLittle is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.

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In research, policy, and practice, continuous variables are often categorized. Statisticians have generally advised against categorization for many reasons, such as loss of information and precision as well as distortion of estimated statistics. Here, a different kind of problem with categorization is considered: the idea that, for a given continuous variable, there is a unique set of cut points that is the objectively correct or best categorization. It is shown that this is unlikely to be the case because categorized variables typically exist in webs of statistical relationships with other variables. The choice of cut points for a categorized variable can influence the values of many statistics relating that variable to others. This essay explores the substantive trade-offs that can arise between different possible cut points to categorize a continuous variable, making it difficult to say that any particular categorization is objectively best. Limitations of different approaches to selecting cut points are discussed. Contextual trade-offs may often be an argument against categorization. At the very least, such trade-offs mean that research inferences, or decisions about policy or practice, that involve categorized variables should be framed and acted upon with flexibility and humility. LAY SUMMARY: In research, policy, and practice, continuous variables are often turned into categorical variables with cut points that define the boundaries between categories. This involves choices about how many categories to create and what cut-point values to use. This commentary shows that different choices about which cut points to use can lead to different sets of trade-offs across multiple statistical relationships between the categorized variable and other variables. These trade-offs mean that no single categorization is objectively best or correct. This context is critical when one is deciding whether and how to categorize a continuous variable.

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Statistical presentation of data is key to understanding patterns and drawing inferences about biomedical phenomena. In this article, we provide an overview of basic statistical considerations for data analysis. Assessment of whether tested parameters are distributed normally is important to decide whether to employ parametric or non-parametric data analyses. The nature of variables (continuous or discrete) also determines analysis strategies. Normally distributed data can be presented using means with standard deviations (SD), whereas non-parametric measures such as medians (with range or interquartile range) should be used for non-normal distributions. While the SD provides a measure of data dispersion, the standard error provides estimates of the 95% confidence interval i.e. the actual mean in the population. Univariable analyses should be directed to denote effect sizes, as well as test a priori hypothesis (i.e. null hypothesis significance testing). Univariable analyses should be followed up by suitable adjusted multivariable analyses such as linear or logistic regression. Linear correlation statistics can help assess whether two variables change hand in hand. Concordance rather than correlation should be used to compare outcome measures of disease states. Prior sample size calculation to ensure adequate study power is recommended for studies which have analogues in the literature with SDs. Statistical considerations for systematic reviews should include appropriate use of meta-analysis, assessment of heterogeneity, publication bias assessment when there are more than ten studies, and quality assessment of studies. Since statistical errors are responsible for a significant proportion of retractions, appropriate statistical analysis is mandatory during study planning and data analysis.

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BACKGROUND: In the past 20 years, various methods have been introduced to construct disease networks. However, established disease networks have not been clinically useful to date because of differences among demographic factors, as well as the temporal order and intensity among disease-disease associations. OBJECTIVE: This study sought to investigate the overall patterns of the associations among diseases; network properties, such as clustering, degree, and strength; and the relationship between the structure of disease networks and demographic factors. METHODS: We used National Health Insurance Service-National Sample Cohort (NHIS-NSC) data from the Republic of Korea, which included the time series insurance information of 1 million out of 50 million Korean (approximately 2%) patients obtained between 2002 and 2013. After setting the observation and outcome periods, we selected only 520 common Korean Classification of Disease, sixth revision codes that were the most prevalent diagnoses, making up approximately 80% of the cases, for statistical validity. Using these data, we constructed a directional and weighted temporal network that considered both demographic factors and network properties. RESULTS: Our disease network contained 294 nodes and 3085 edges, a relative risk value of more than 4, and a false discovery rate-adjusted P value of <.001. Interestingly, our network presented four large clusters. Analysis of the network topology revealed a stronger correlation between in-strength and out-strength than between in-degree and out-degree. Further, the mean age of each disease population was related to the position along the regression line of the out/in-strength plot. Conversely, clustering analysis suggested that our network boasted four large clusters with different sex, age, and disease categories. CONCLUSIONS: We constructed a directional and weighted disease network visualizing demographic factors. Our proposed disease network model is expected to be a valuable tool for use by early clinical researchers seeking to explore the relationships among diseases in the future.

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There are several different approaches to analyze event-related potentials (ERPs) at single-subject level, and the aim of the current study is to provide information for choosing a method based on its ability to detect ERP effects and factors influencing the results. We used data from 79 healthy participants with EEG referenced to mastoid average and investigated the detection rate of auditory N400 effect in single-subject analysis using five methods: visual inspection of participant-wise averaged ERPs, analysis of variance (ANOVA) for amplitude averages in a time window, cluster-based non-parametric testing, a novel Bayesian approach and Studentized continuous wavelet transform (t-CWT). Visual inspection by three independent raters yielded N400 effect detection in 85% of the participants in at least one paradigm (active responding or passive listening), whereas ANOVA identified the effect in 68%, the cluster-method in 59%, the Bayesian method in 89%, and different versions of t-CWT in 22-59% of the participants. Thus, the Bayesian method was the most liberal and also showed the greatest concordance between the experimental paradigms (active/passive). ANOVA detected significant effect only in cases with converging evidence from other methods. The t-CWT and cluster-based method were the most conservative methods. As we show in the current study, different analysis methods provide results that do not completely overlap. The method of choice for determining the presence of an ERP component at single-subject level thus remains unresolved. Relying on a single statistical method may not be sufficient for drawing conclusions on single-subject ERPs.

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BACKGROUND: Until recently, there was no approved adjuvant therapy (AT) for renal cell carcinoma (RCC) unless sunitinib was approved in the US. We evaluated clinical opinion and estimated use regarding different treatment options and patient selection of AT in RCC patients based on current scientific data and individual experience in Germany. METHODS: We conducted an anonymous survey during a national urology conference in 01/2017. Answers of 157 urologists treating RCC patients could be included. Questions were related to practice setting, treatment of RCC, follow-up strategy, physicians' personal opinion and individually different important parameters regarding S-TRAC and ASSURE-trial. RESULTS: 82% were office based. 67% were located in larger cities. 83% reported that nephron-sparing surgery (NSS) was performed in tumors with diameter < 4 cm. Follow-up was done mainly in concordance with guideline recommendations. 68% treated an average of 2.9 patients/year with systemic therapy. Therapy was predominantly advocated using sunitinib (94%). Urologists were informed about S-TRAC and ASSURE-trial. For 47%, reported hazard ratio is the most important parameter to understand trial results followed by overall survival (OS) in 46%, disease-free survival in 38%, and results of other trials in 34%. The most convincing parameter to decide on AT is OS (69%). 62% placed their confidence in ASSURE over STRAC-trial. 44% would use AT for 12 months. Nodal involvement was the most common denominator for use of AT. 82% favor sunitinib as AT. CONCLUSIONS: A minority of urologists would use AT and are more confident in ASSURE-trial. Reluctance of prescribing AT mainly is based on lack of OS data and conflicting trial results.

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