Archetypal analysis for ordinal data

Archetypoid analysis (ADA) is an exploratory approach that explains a set of continuous observations as mixtures of pure (extreme) patterns. Those patterns (archetypoids) are actual observations of the sample which makes the results of this technique easily interpretable, even for non-experts. Note that the observations are approximated as a convex combination of the archetypoids. Archetypoid analysis, in its current form, cannot be applied directly to ordinal data. We propose and describe a two-step method for applying ADA to ordinal responses based on the ordered stereotype model. One of the main advantages of this model is that it allows us to convert the ordinal data to numerical values, using a new data-driven spacing that better reflects the ordinal patterns of the data, and this numerical conversion then enables us to apply ADA straightforwardly. The results of the novel method are presented for two behavioural science applications. Finally, the proposed method is also compared with other unsupervised statistical learning methods

A rapid review of sexual wellbeing definitions and measures: should we now include sexual wellbeing freedom?

An increasing number of studies refer to sexual wellbeing and/or seek to measure it, and the term appears across various policy documents, including sexual health frameworks in the UK. We conducted a rapid review to determine how sexual wellbeing has been defined, qualitatively explored and quantitatively measured. Eligible studies selected for inclusion from OVID Medline, PsychInfo, PubMed, Embase, CINAHL were: in English language, published after 2007, were peer-reviewed full articles, focused on sexual wellbeing (or proxies for, e.g. satisfaction, function), and quantitatively or qualitatively assessed sexual wellbeing. We included studies with participants aged 16–65. Given study heterogeneity, our synthesis and findings are reported using a narrative approach. We identified 162 papers, of which 10 offered a definition of sexual wellbeing. Drawing upon a socio-ecological model, we categorised the 59 dimensions we identified from studies under three main domains: cognitive-affect (31 dimensions); inter-personal (22 dimensions); and socio-cultural (6 dimensions). Only 11 papers were categorised under the socio-cultural domain, commonly focusing on gender inequalities or stigma. We discuss the importance of conceptualising sexual wellbeing as individually experienced but socially and structurally influenced, including assessing sexual wellbeing freedom: a person’s freedom to achieve sexual wellbeing, or their real opportunities and liberties

Goodness-of-fit and generalized estimating equation methods for ordinal responses based on the stereotype model

Background: Data with ordinal categories occur in many diverse areas, but methodologies for modeling ordinal data lag severely behind equivalent methodologies for continuous data. There are advantages to using a model specifically developed for ordinal data, such as making fewer assumptions and having greater power for inference. Methods: The ordered stereotype model (OSM) is an ordinal regression model that is more flexible than the popular proportional odds ordinal model. The primary benefit of the OSM is that it uses numeric encoding of the ordinal response categories without assuming the categories are equally-spaced. Results: This article summarizes two recent advances in the OSM: (1) three novel tests to assess goodness-of-fit; (2) a new Generalized Estimating Equations approach to estimate the model for longitudinal studies. These methods use the new spacing of the ordinal categories indicated by the estimated score parameters of the OSM. Conclusions: The recent advances presented can be applied to several fields. We illustrate their use with the well-known arthritis clinical trial dataset. These advances fill a gap in methodologies available for ordinal responses and may be useful for practitioners in many applied fieldsThis research has been supported by Marsden grant E2987-3648 administrated by the Royal Society of New Zealand, by grant 2017 SGR 622 (GRBIO) administrated by the Departament d’Economia i Coneixement de la Generalitat de Catalunya (Spain) and by the Ministerio de Ciencia e Innovación (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033].Peer ReviewedPostprint (published version

Prisoner knowledge about head injury is improved by brief psychoeducation

Introduction: The high prevalence of head injury (HI) in prisoners and its association with offending indicates a need for interventions. However, there is little evidence and none for the effectiveness of psychoeducation in improving prisoner knowledge about HI and its effects. Methods: Small groups of males in two Scottish prisons underwent a 1 hour psychoeducation session delivered by PowerPoint and combined with question and answer, video clips and a booklet about HI. A pre-post intervention design was used to assess knowledge about HI from vignettes. Participants indicated effects of HI using unprompted free recall and then with a questionnaire (the Symptom Checklist; SCL), pre-education (n = 34), post-education (n = 19) and at 4-week follow-up (n = 11). Free recall was scored using symptom lists from national guidelines (FR-SIGN) or the SCL (FR-SCL). Within-subject comparisons were made between pre-intervention, post-intervention and follow-up scores. Results: Knowledge about HI significantly increased pre- to post-education for FR-SIGN (d = 0.91; 95% CI 0.62, 2.53) and FR-SCL (d = 0.99; 95% CI 0.95, 4.00) without decrement at follow-up (FR-SIGN d = 1.27; 95% CI 0.53, 2.56; FR-SCL r = 0.60). Scores on the SCL did not change over time (p > .05). Conclusion: Prisoner knowledge about HI was improved by brief psychoeducation suitable for delivery in prisons

Simple and objective prediction of survival in patients with lung cancer: staging the host systemic inflammatory response

Background. Prediction of survival in patients diagnosed with lung cancer remains problematical. The aim of the present study was to examine the clinical utility of an established objective marker of the systemic inflammatory response, the Glasgow Prognostic Score, as the basis of risk stratification in patients with lung cancer. Methods. Between 2005 and 2008 all newly diagnosed lung cancer patients coming through the multidisciplinary meetings (MDTs) of four Scottish centres were included in the study. The details of 882 patients with a confirmed new diagnosis of any subtype or stage of lung cancer were collected prospectively. Results. The median survival was 5.6 months (IQR 4.8–6.5). Survival analysis was undertaken in three separate groups based on mGPS score. In the mGPS 0 group the most highly predictive factors were performance status, weight loss, stage of NSCLC, and palliative treatment offered. In the mGPS 1 group performance status, stage of NSCLC, and radical treatment offered were significant. In the mGPS 2 group only performance status and weight loss were statistically significant. Discussion. This present study confirms previous work supporting the use of mGPS in predicting cancer survival; however, it goes further by showing how it might be used to provide more objective risk stratification in patients diagnosed with lung cancer

Row mixture-based clustering with covariates for ordinal responses

Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of individual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the individuals’ similar patterns of responses and the effects of the covariates on the individuals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set"This work has been supported by the Ministerio de Ciencia e Innovación (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033], and by Grant 2021 SGR 01421 (GRBIO) administrated by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain). Daniel Fernández is member of the Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM). Daniel Fernández is a Serra Húnter Fellow"Peer ReviewedPostprint (published version

Comparison of the prognostic value of ECOG-PS, MGPS and BMI/WL: Implications for a clinically important framework in the assessment and treatment of advanced cancer

BACKGROUND AND AIMS:The systemic inflammatory response is associated with the loss of lean tissue, anorexia, weakness, fatigue and reduced survival in patients with advanced cancer and therefore is important in the definition of cancer cachexia. The aim of the present study was to carry out a direct comparison of the prognostic value of Eastern Cooperative Oncology Group Performance Status (ECOG-PS), modified Glasgow Prognostic Score (mGPS) and Body Mass Index/Weight Loss Grade (BMI/WL grade) in patients with advanced cancer. METHOD:All data were collected prospectively across 18 sites in the UK and Ireland. Patient's age, sex, ECOG-PS, mGPS and BMI/WL grade were recorded, as were details of underlying disease including metastases. Survival data were analysed using univariate and multivariate Cox regression. RESULTS:A total of 730 patients were assessed. The majority of patients were male (53%), over 65 years of age (56%), had an ECOG-PS>0/1 (56%), mGPS≥1 (56%), BMI≥25 (51%), <2.5% weight loss (57%) and had metastatic disease (86%). On multivariate cox regression analysis ECOG-PS (HR 1.61 95%CI 1.42-1.83, p < 0.001), mGPS (HR 1.53, 95%CI 1.39-1.69, p < 0.001) and BMI/WL grade (HR 1.41, 95%CI 1.25-1.60, p < 0.001) remained independently associated with overall survival. In patients with a BMI/WL grade 0/1 both ECOG and mGPS remained independently associated with overall survival. CONCLUSION:The ECOG/mGPS framework may form the basis of risk stratification of survival in patients with advanced cancer

Glimpses of the future: designing fictions for mixed-reality performances

No abstract available