Appraising prediction research: a guide and meta-review on bias and applicability assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Over the past few years, a large number of prediction models have been published, often of poor methodological quality. Seemingly objective and straightforward, prediction models provide a risk estimate for the outcome of interest, usually based on readily available clinical information. Yet, using models of substandard methodological rigour, especially without external validation, may result in incorrect risk estimates and consequently misclassification. To assess and combat bias in prediction research the prediction model risk of bias assessment tool (PROBAST) was published in 2019. This risk of bias (ROB) tool includes four domains and 20 signalling questions highlighting methodological flaws, and provides guidance in assessing the applicability of the model. In this paper, the PROBAST will be discussed, along with an in-depth review of two commonly encountered pitfalls in prediction modelling that may induce bias: overfitting and composite endpoints. We illustrate the prevalence of potential bias in prediction models with a meta-review of 50 systematic reviews that used the PROBAST to appraise their included studies, thus including 1510 different studies on 2104 prediction models. All domains showed an unclear or high ROB; these results were markedly stable over time, highlighting the urgent need for attention on bias in prediction research. This article aims to do just that by providing (1) the clinician with tools to evaluate the (methodological) quality of a clinical prediction model, (2) the researcher working on a review with methods to appraise the included models, and (3) the researcher developing a model with suggestions to improve model quality.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination.

OBJECTIVE: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation. STUDY DESIGN AND SETTING: We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n=556) and assessed the change in discrimination (dAUC) in external validation cohorts (n=1,147). RESULTS: PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P<0.001). CONCLUSION: High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.

Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide.

To fight COVID-19, global access to reliable data is vital. Given the rapid acceleration of new cases and the common sense of global urgency, COVID-19 is subject to thorough measurement on a country-by-country basis. The world is witnessing an increasing demand for reliable data and impactful information on the novel disease. Can we trust the data on the COVID-19 spread worldwide? This study aims to assess the reliability of COVID-19 global data as disclosed by local authorities in 202 countries. It is commonly accepted that the frequency distribution of leading digits of COVID-19 data shall comply with Benford's law. In this context, the author collected and statistically assessed 106,274 records of daily infections, deaths, and tests around the world. The analysis of worldwide data suggests good agreement between theory and reported incidents. Approximately 69% of countries worldwide show some deviations from Benford's law. The author found that records of daily infections, deaths, and tests from 28% of countries adhered well to the anticipated frequency of first digits. By contrast, six countries disclosed pandemic data that do not comply with the first-digit law. With over 82 million citizens, Germany publishes the most reliable records on the COVID-19 spread. In contrast, the Islamic Republic of Iran provides by far the most non-compliant data. The author concludes that inconsistencies with Benford's law might be a strong indicator of artificially fabricated data on the spread of SARS-CoV-2 by local authorities. Partially consistent with prior research, the United States, Germany, France, Australia, Japan, and China reveal data that satisfies Benford's law. Unification of reporting procedures and policies globally could improve the quality of data and thus the fight against the deadly virus.

Guidelines that use the GRADE approach often fail to provide complete economic information for recommendations: A systematic survey.

OBJECTIVE: Little is known about how developers and panel members report cost and cost effectiveness considerations in GRADE guideline Evidence-to-Decision (EtD) frameworks. A systematic survey was conducted to explore approaches and factors contributing to variability in economic information reporting. STUDY DESIGN AND SETTING: Guideline organization websites were systematically searched to create a convenience sample of guidelines. Reviewers screened published EtD frameworks and generated frequencies of reporting approaches. We used thematic analysis to summarize factors related to variability of economic information reporting. RESULTS: We included 142 guidelines. The overall rate of reporting economic information was high (91%); however, there was variability across completion of predefined EtD Likert-type judgments (70%), noting information as not identified across EtD framework domains (57%), and providing remarks to justify recommendations (38%). Six themes contributing to variability emerged, related to: intervention, population, payor, provider, healthcare resource use, and economic model building factors. Only 2 guidelines performed a GRADE certainty appraisal of economic outcomes. CONCLUSION: Completing predefined EtD Likert-type judgments, specifically reporting a literature review approach, study selection criteria and economic model building limitations, as well as linking these to recommendation justification remarks are potential areas for improved use, adoption and adaptation of recommendation, and transparency of GRADE EtD frameworks.

Homeopathy Assessment-Contribution of the Human Sciences.

The recent questions about homeopathy raised by some sceptics have focused an awareness on this therapy and led different protagonists among the scientific community to seek a much-needed re-assessment. The inputs coming from external but benevolent experts will broaden the knowledge and the consciousness of the whole medical community, and more widely of the public, about the meaning and the value of homeopathy.Looking at this from the point of view of the human sciences gives a perspective on the universality of the philosophy that underlies homeopathic thinking, which is particularly visible in the methodological similarities between homeopathic provings and sociological or anthropological observations. It also explains how this view of health and care coincides with the expectations of the public, who no longer want a limited mechanical approach to the human body or more generally to the environment.The input to homeopathy of the human sciences, with their methodological tools and approaches, and highlighting the possibilities offered by mixed-methods research, could enable these notions to be heard and shared in the wider scientific community.

Representation of Women as Authors of Rheumatology Research Articles.

OBJECTIVE: In academic medicine, journal article authorship is central to career advancement and promotion. This study aimed to examine the contemporary representation of women as first and senior authors of rheumatology original research articles. METHODS: The gender of the first and senior author, disease category, research design, and funding source were extracted from rheumatology original research articles published in high-impact rheumatology and general medical journals between 2015 and 2019. RESULTS: The analysis included 7,651 original research articles. In total, 51.5% of the articles had women first authors (95% confidence interval [95% CI] 50.4-52.6%) and 35.3% had women senior authors (95% CI 34.2-36.4%). Women were significantly less likely to be first and senior authors of articles reporting randomized controlled trials compared with other clinical research designs (P < 0.001), and of articles reporting industry-funded/industry-initiated studies compared with studies not funded by industry (P ≤ 0.01). Of the articles reporting industry-funded/industry-initiated randomized controlled trials, women were first authors in 18.5% (95% CI 13.8-24.0%) and senior authors in 23.9% (95% CI 18.6-29.8%). CONCLUSION: In rheumatology research articles, there is gender parity for first authorship, but women are underrepresented in senior authorship positions. Underrepresentation of women in authorship is particularly apparent in articles reporting randomized controlled trials, and especially those that are initiated by industry.

Captação de recursos para pesquisas e o terceiro setor: o que os docentes sabem? / Research fundraising and third sector organizations: what do the professors know?

Resumo: Introdução: O crescimento socioeconômico de um país acontece por meio do desenvolvimento de ciência e tecnologia, e, por isso, a parceria com o terceiro setor é positiva ao auxiliar o financiamento de projetos científicos. Objetivos: Assim, o presente estudo objetiva avaliar as formas de captação de recursos mais utilizadas pelos docentes de Medicina para a execução de seus estudos científicos e verificar se há o reconhecimento do terceiro setor como opção para tal obtenção. Métodos: Trata-se de um estudo transversal analítico com abordagem quali-quantitativa, construído por meio do uso de um formulário individual aplicado aos docentes do curso médico de cinco instituições do ensino superior do Norte/Nordeste do Brasil. Os dados obtidos foram analisados por estatísticas descritiva e analítica. Resultados: Participaram 138 professores, de ambos os sexos, com idade média ± 43,2 anos. A pesquisa mais comumente desenvolvida pelos docentes foi a básica (56,1%); 68,8% utilizaram recursos próprios para o desenvolvimento de seus estudos; 18,8% obtiveram recursos de entidades de apoio à pesquisa; 75,4% conheciam alguma fundação de amparo à pesquisa, e as mais citadas foram a Fapespa (26,9%) e o CNPq (26,9%); 13,8% relataram conhecer sites e empresas multinacionais que fomentam projetos de pesquisas, mas somente 2,2% submeteram sua iniciativa às chamadas públicas em instituições internacionais para captação; quando perguntados sobre o terceiro setor, seus fundamentos e atores definidos pela legislação, o estudo apontou uma falta de conhecimento, com 100% de inadequação nas respostas sobre tais entidades; quanto à parceria entre o terceiro setor e o Estado, 83,3% sinalizaram desconhecimento;100% dos entrevistados desconhecem os critérios para uma entidade integrar tal setor, ao mesmo tempo que 76,8% afirmaram que a falta de orientação dos conceitos do ramo não é entrave para a captação de recursos às pesquisas. Conclusão: A maioria dos docentes utilizava recursos próprios para a realização de seus projetos, seguidos pelo uso de recursos públicos. Ademais, grande parte dos profissionais não reconheceu o terceiro setor como fonte patrocinadora, carecendo de informações que lhes possibilitassem desenvolver atividades de forma ampla e com as diversas oportunidades existentes, oferecidas por entidades desse setor.

Abstract: Introduction: The socio-economic growth of a country is reflected by the development of science and technology. Partnership with the third sector is therefore positive in terms of financial support for scientific projects. Objective: Accordingly, the present study aims to evaluate the most commonly used methods of fundraising by medical professors to conduct their scientific studies and whether there is recognition of the third sector as an option for such fundraising. Method: A cross-sectional analytical study with a qualitative and quantitative approach. The data were collected using an individual form, applied to medical course professors from five different universities in the north of Brazil, and analyzed using descriptive-analytical statistics. Results: The study included 138 professors, of both sexes, with an average age of ± 43.2 years. The type of research most commonly performed by professors was basic (56.1%); 68.8% used their own resources to develop their studies; 18.8% obtained funds from research support entities; 75.4% knew of a research support foundation, primarily FAPESPA (26.9%) and CNPQ (26.9%); 13.8% reported knowledge of websites and multinational companies that promote research projects, but only 2.2% submitted their initiative to public calls for funding from international institutions; when asked about the third sector, its fundamentals and actors defined by legislation, the study indicated a lack of knowledge, with 100% of inadequate answers about such entities; regarding partnership between the third sector and the State, 83.3% signaled ignorance; 100% of the interviewees were unaware of the criteria for an entity to be part of the sector, while 76.8% stated that lack of orientation about the concepts of the area is not an obstacle to raising funds for research. Conclusion: Most professors use their own funds to carry out their projects, followed by the use of public funds. Furthermore, most professionals did not recognize the third sector as a source of sponsorship, lacking information that would allow them to develop broad activities and enjoy the various opportunities offered by entities in this sector.

Review of economic data on closed system transfer drug for preparation and administration of hazardous drugs.

OBJECTIVES: The objectives of this study were to review economic data on the use of closed system drug transfer devices (CSTDs) for preparing and administering hazardous drugs, and to evaluate the quality of data reporting as defined by the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). METHODS: All references from a recent Cochrane review about CSTDs were evaluated for inclusion. A literature review was also conducted. Articles containing economic data about the use of CSTDs were retained for analysis. Two researchers independently graded the articles according to the 24-item CHEERS checklist. RESULTS: Of the 138 articles identified initially, 12 were retained for analysis. Nine of these studies did not report acquisition costs or did not detail acquisition costs. Six studies reported economic benefits associated with the used of CSTDs, all related to extending the beyond-use date. The mean number of CHEERS criteria fulfilled by the included articles was 9.2 (SD 2.4). CONCLUSIONS: CSTDs are costly to acquire. However, few studies have examined the economic impact of these devices, and the existing studies are incomplete. As a result, hospitals planning to implement these devices will be unable to make a sound economic evaluation. Robust economic evaluation of CSTDs is needed.

Identification of Optimal Measures of Human Abuse Potential: A Post Hoc Assessment of 19 Studies.

BACKGROUND: Human abuse potential studies include multiple measures to assess the subjective effects of central nervous system-active drugs. In this retrospective analysis, measurement properties of commonly used measures were assessed, and factor analysis was conducted to identify a core battery of measures. METHODS: Measures of positive, negative and other effects, for example, bipolar "at-the-moment" Drug Liking visual analog scale (VAS), were derived for active controls and placebo from 19 studies in recreational drug users (N = 570). Distribution, placebo response, variability, convergent/discriminant validity, parameter effect sizes (eg, maximum effect [Emax], time-averaged area under the effect curve), and predictive validity were evaluated. A factor analysis was conducted with 9 studies. RESULTS: Most parameters were not normally distributed. Bipolar VAS exhibited the lowest variability. Drug Liking VAS Emax was very sensitive, showed large effect sizes (>1.0), and was moderately to strongly correlated with Emax of other positive effects measures (r > 0.5), but weaker with less specific scales (eg, high, Any Effects VAS); time-averaged area under the effect curve showed higher variability and lower effect sizes. Maximum effect at any dose (EmaxD) was significantly correlated with Emax across all selected measures and showed higher effect sizes. In the overall factor analysis, factors could be categorized into positive effects/euphoria (77% of variance), negative effects (17.9%), and pharmacologic effects (5%). For predictive validity, effect sizes for Drug Liking VAS Emax/EmaxD were moderately correlated with postmarket adverse events related to abuse (R = 0.52). CONCLUSIONS: A core battery of 7 subjective measures was proposed, with additional measures added based on pharmacologic effects.

Assessment of Trends in the Design, Accrual, and Completion of Trials Registered in ClinicalTrials.gov by Sponsor Type, 2000-2019.

Importance: ClinicalTrials.gov is a valuable resource that can be used to trace the state and nature of trials. Since its launch in 2000, more than 345 000 trials have been registered. Little is known about the characteristics and trends in clinical trials over time and how they differ by sponsor type. Objective: To assess trends in clinical trials registered in ClinicalTrials.gov over time and by sponsor type. Design, Setting, and Participants: This cross-sectional study included clinical trials (interventional studies) registered in ClinicalTrials.gov from January 1, 2000, through December 31, 2019. The trials were grouped by lead sponsor: National Institutes of Health (NIH) and other US government agencies, industry, and other sources (foundations, universities, hospitals, clinics, and others). A static version of the Clinical Trials Transformation Initiative Aggregate Analysis of ClinicalTrials.gov database was downloaded on January 1, 2020, for analysis. Main Outcomes and Measures: ClinicalTrials.gov registration fields, including overall status, phase, intervention, number of sites, use of masking and randomization, sample size, and time to study completion by start year and lead sponsor (organization that provided funding or support for a clinical study). Results: A total of 245 999 clinical trials (interventional studies) were started between 2000 and 2019, of which 135 144 (54.9%) were completed. Among completed trials, 5113 (3.8%) were sponsored by the NIH or a US government agency, 48 668 (36.0%) by industry, and 81 363 (60.2%) by other sources. Most trials were single center (61.3%), randomized (65.6%), and phase 1 to 2 (35.5%) or did not have a US Food and Drug Administration-defined phase (38.4%), with fewer drug trials being conducted over time. Sample sizes were small (median, 60; interquartile range [IQR], 30-160) and diminished over time. Trial median completion times varied by lead sponsor: 3.4 years (IQR, 1.9-5.0 years) for NIH- and US government-sponsored trials, 1.2 years (IQR, 0.5-2.4 years) for industry trials, and 2.1 years (IQR, 1.1-3.7) for trials sponsored by other sources. Conclusions and Relevance: The findings suggest that the composition and design of trials changed from 2000 to 2019 and differed substantially by sponsor type. Increased funding toward larger randomized clinical trials may be warranted to inform clinical decision-making and guide future research.

Expanding Data Reporting Capacity of Free and Charitable Clinics: A Quality Improvement Project.

BACKGROUND: In 2018, member clinics of the Virginia Association of Free and Charitable Clinics (VAFCC) provided over 235,500 visits to un/underinsured patients. A survey of VAFCC members found that only 67% report on clinical outcome measures and 56% do not collect social determinant of health (SDOH) data. OBJECTIVE: The purpose of this project was to determine if the provision of web-based technical assistance toolkits and peer mentoring improve quality and data reporting capacity of VAFCC member clinics. METHODS: Clinics that self-selected were provided with 16 weeks of customized interventions including SDOH Data Reporting Toolkit, CMS Quality Data Reporting Toolkit, Electronic Health Record Implementation Toolkit, and peer mentoring. RESULTS: Post-implementation, 100% of participating clinics reported that the resources provided benefited their organizations and increased their capacity to report. CONCLUSIONS: The provision of technical assistance, tangible resources, and customized peer mentoring can better equip Free and Charitable Clinics (FCCs) to tell the story of their patients' social barriers and clinical outcomes. IMPLICATIONS FOR NURSING: DNP prepared nurses working with vulnerable populations are positioned to assist FCCs in documenting their relevance in the safety net system. Enhancing the ability of FCCs to collect and report data will allow them to demonstrate the provision of high-quality care, despite limited resources.

Economic Evaluation Studies.

Economic evaluations, including cost-effectiveness analyses, are frameworks for decision-making. They help to illustrate tradeoffs between selecting one choice over another. This form of analysis is of great power and value to the health-care system. Health-care decisions are complex; they require synthesis of a myriad of data variables and sources, and the impact of the choices made is significant. Given this importance and the increasing demand and complexity of health decisions, it is imperative to ensure that economic evaluations are of high quality, comprehensive, and follow the guidelines and recommendations of experts in the field. This article provides an overview of the types of economic evaluations and their role in decision-making. It also discusses key study design considerations, including methods, scope, results, and reporting. Links to published checklists are provided along with additional sources of information, including a glossary of terms (Appendix), to guide the researcher to produce high-quality economic evaluations and guide the reviewer to provide high-quality feedback during the review process.

Evaluating the implementation of an early supported discharge (ESD) program for stroke survivors: A mixed methods longitudinal case study.

BACKGROUND: Early supported discharge (ESD) models of care for stroke survivors coordinate inpatient and community services, with the aim of reducing length of stay. While there is an established evidence base around the clinical outcomes of ESD), less is known about the implementation of this approach into existing stroke care service. The aim of this case study was to describe staff perceptions of the implementation of an ESD model of care for stroke survivors at a large metropolitan public hospital in Australia. METHODS: This case study utilised a mixed methods design, which was designed in explicit alignment with the Consolidated Framework for Implementation Research (CFIR). Participants included staff that referred patients for ESD, and staff involved in the planning, implementation or delivery of ESD. Survey data was collected at three time points (ESD commencement, 3 months and 6 months), and focus groups were undertaken at the conclusion of the study. All quantitative data was analysed descriptive, while qualitative data was evaluated using thematic analysis. RESULTS: Results from both sources of data identified changes in staff perceptions of ESD implementation over time. While very few changes were statistically significant, they were diverse patterns of change across the CFIR constructs over time. The characteristics of individuals and ESD characteristics attracted consistently positive perceptions, while patient needs and resources was the most prevalent theme within the data. While perceptions of factors related to the inner setting were mixed, there was a steady improvement in perceptions about the process across the later stages of implementation. CONCLUSIONS: The sophistication of knowledge translation and implementation in modern complex healthcare environments is highlighted by the multiple interactions between the CFIR domains and constructs. While the implementation process described was generally positive and effective, using the CFIR as a framework confirmed that it also entailed some challenges and unanticipated outcomes.

Measuring individual benefits of psychiatric treatment using longitudinal binary outcomes: Application to antipsychotic benefits in non-cannabis and cannabis users.

We present and evaluate a method for predicting individual treatment benefits based on random effects logistic regression models of binary outcomes that change over time. The method uses empirical Bayes predictors based on patients' characteristics and responses to treatment. It is applicable to both 1-dimentional and 2-dimentional personalized medicine models. Comparisons between predicted and true benefits for simulated new patients using correlations, relative biases and mean-squared errors were used to evaluate prediction performance. The predicted benefits had relatively small biases and relatively high correlations with the true benefits in the simulated new patients. The predictors also captured estimated overall population trends in the evolution of individual benefits. The proposed approach can be used to retrospectively evaluate patients' responses in a clinical trial, or to retrospectively or prospectively predict individual benefits of different treatments for new patients using patients' characteristics and previous responses. The method is used to examine changes in the disorganized dimension of antipsychotic-naïve patients from an antipsychotic randomized clinical trial. Retrospective prediction of individual benefits revealed that more cannabis users had slower and lower responses to antipsychotic treatment as compared to non-cannabis users, revealing cannabis use as a negative prognostic factor for psychotic disorders in the disorganized dimension.

Comparison of two treatments in the presence of competing risks.

Competing risks data arise frequently in clinical trials, and a common problem encountered is the overall homogeneity between two groups. In competing risks analysis, when the proportional subdistribution hazard assumption is violated or two cumulative incidence function (CIF) curves cross; currently, the most commonly used testing methods, for example, the Gray test and the Pepe and Mori test, may lead to a significant loss of statistical testing power. In this article, we propose a testing method based on the area between the CIF curves (ABC). The ABC test captures the difference over the whole time interval for which survival information is available for both groups and is not based on any special assumptions regarding the underlying distributions. The ABC test was also extended to test short-term and long-term effects. We also consider a combined test and a two-stage procedure based on this new method, and a bootstrap resampling procedure is suggested in practice to approximate the limiting distribution of the combined test and two-stage test. An extensive series of Monte Carlo simulations is conducted to investigate the power and the type I error rate of the methods. In addition, based on our simulations, our proposed TS, Comb, and ABC tests have a relatively high power in most situations. In addition, the methods are illustrated using two different datasets with different CIF situations.

Drug-induced disseminated intravascular coagulation: a pharmacovigilance study on World Health Organization's database.

BACKGROUND: Disseminated intravascular coagulation (DIC) occurs in several clinical conditions, including drug therapy. We aim to investigate the association between the administration of several drug classes and the onset of DIC by using the reports of Adverse Drug Reactions (ADR) collected in Vigibase, the World Health Organization (WHO) database of ADR. METHODS: We collected reports of drug-related DIC from 1968 to September 2015, classified in Vigibase according to the MedDRA (Medical Dictionary for Regulatory Activities) term "Disseminated intravascular coagulation". A disproportionality analysis using Reporting Odds Ratio (ROR) with 95% Confidence Interval (CI95%) was performed. RESULTS: Overall, 4653 reports of drug-associated DIC were retrieved and the 75.9% of them was serious according to WHO seriousness criteria. DIC was significantly (ROR > 1, lower limit of CI95% > 1) associated with 88 drugs, mainly antineoplastic agents, antithrombotic agents and antibacterials for systemic use. Among of the most frequently reported individual drugs we found dabigatran (94 reports) ROR = 1.34 (CI95% 1.08-1.67), oxaliplatin and bevacizumab both with 75 reports and ROR = 1.77 (1.38-2.27) and 2.02 (1.57-2.61), respectively. CONCLUSION: A substantial number of drugs, widely used in the clinical practice, may be associated with the potential occurrence of DIC. For many of these drugs, the ADR is not acknowledged in the corresponding Summary of Product Characteristics. The high number of drugs involved underlines the importance of evaluate this condition such as an ADR that might occur during drug therapy.

Reweighting Randomized Controlled Trial Evidence to Better Reflect Real Life - A Case Study of the Innovative Medicines Initiative.

Evidence from randomized controlled trials available for timely health technology assessments of new pharmacological treatments and regulatory decision making may not be generalizable to local patient populations, often resulting in decisions being made under uncertainty. In recent years, several reweighting approaches have been explored to address this important question of generalizability to a target population. We present a case study of the Innovative Medicines Initiative to illustrate the inverse propensity score reweighting methodology, which may allow us to estimate the expected treatment benefit if a clinical trial had been run in a broader real-world target population. We learned that identifying treatment effect modifiers, understanding and managing differences between patient characteristic data sets, and balancing the closeness of trial and target patient populations with effective sample size are key to successfully using this methodology and potentially mitigating some of this uncertainty around local decision making.

Time series analyses with psychometric data.

Understanding of interactional dynamics between several processes is one of the most important challenges in psychology and psychosomatic medicine. Researchers exploring behavior or other psychological phenomena mostly deal with ordinal or interval data. Missing values and consequential non-equidistant measurements represent a general problem of longitudinal studies from this field. The majority of process-oriented methodologies was originally designed for equidistant data measured on ratio scales. Therefore, the goal of this article is to clarify the conditions for satisfactory performance of longitudinal methods with data typical in psychological and psychosomatic research. This study examines the performance of the Johansen test, a procedure incorporating a set of sophisticated time series techniques, in reference to data quality utilizing a Monte Carlo method. The main results of the conducted simulation studies are: (1) Time series analyses require samples of at least 70 observations for an accurate estimation and inference. (2) Discrete data and failing equidistance of measurements due to irregular missing values appear unproblematic. (3) Relevant characteristics of stationary processes can be adequately captured using 5- or 7-point ordinal scales. (4) For trending processes, at least 10-point scales are necessary to ensure an acceptable quality of estimation and inference.

A curve similarity approach to parallelism testing in bioassay.

Potency determination via bioassay is a relative measure that requires an evaluation of parallelism between the dose-response relationships of a reference standard and a sample material. Typical approaches for assessing parallelism include difference ([Formula: see text]-value) and equivalence tests. These traditional methods rely on a statistical assessment of model parameters as opposed to direct evaluation of the similarity of the dose-response curves. We propose a simple curve similarity approach that tests the hypothesis that the sample material is a dilution or concentration of the reference standard. The test is achieved by quantifying and normalizing the total area between the two curves and provides a single composite measure of parallelism. Both a frequentist and a Bayesian approach to the test are provided. We show through a simulation study that the curve similarity approach overcomes the drawbacks of the traditional methods and is effective at detecting parallelism and non-parallelism for bioassays.