63 research outputs found
A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R
We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we consider the case when S matrices are available, each describing the dissimilarities among the same n objects, possibly expressed by S subjects (judges), or measured under different experimental conditions, or with reference to different characteristics of the objects themselves. In particular, we focus on binary dissimilarities, taking values 0 or 1 depending on whether or not two objects are deemed as dissimilar. We are interested in analyzing such data using multidimensional scaling (MDS). Differently from standard MDS algorithms, our goal is to cluster the dissimilarity matrices and, simultaneously, to extract an MDS configuration specific for each cluster. To this end, we develop a fully Bayesian three-way MDS approach, where the elements of each dissimilarity matrix are modeled as a mixture of Bernoulli random vectors. The parameter estimates and the MDS configurations are derived using a hybrid Metropolis-Gibbs Markov Chain Monte Carlo algorithm. We also propose a BIC-like criterion for jointly selecting the optimal number of clusters and latent space dimensions. We illustrate our approach referring both to synthetic data and to a publicly available data set taken from the literature. For the sake of efficiency, the core computations in the package are implemented in C/C++. The package also allows the simulation of multiple chains through the support of the parallel package
Holistic analysis of the life course: Methodological challenges and new perspectives
Abstract We survey state-of-the-art approaches to study trajectories in their entirety, adopting a holistic perspective, and discuss their strengths and weaknesses. We begin by considering sequence analysis (SA), one of the most established holistic approaches. We discuss the inherent problems arising in SA, particularly in the study of the relationship between trajectories and covariates. We describe some recent developments combining SA and Event History Analysis, and illustrate how weakening the holistic perspective—focusing on sub-trajectories—might result in a more flexible analysis of life courses. We then move to some model-based approaches (included in the broad classes of multistate and of mixture latent Markov models) that further weaken the holistic perspective, assuming that the difficult task of predicting and explaining trajectories can be simplified by focusing on the collection of observed transitions. Our goal is twofold. On one hand, we aim to provide social scientists with indications for informed methodological choices and to emphasize issues that require consideration for proper application of the described approaches. On the other hand, by identifying relevant and open methodological challenges, we highlight and encourage promising directions for future research
Colombian Women’s Life Patterns: A Multivariate Density Regression Approach
Women in Colombia face difficulties related to the patriarchal traits of
their societies and well-known conflict afflicting the country since 1948. In
this critical context, our aim is to study the relationship between baseline
socio-demographic factors and variables associated to fertility, partnership
patterns, and work activity. To best exploit the explanatory structure, we
propose a Bayesian multivariate density regression model, which can accommodate
mixed responses with censored, constrained, and binary traits. The flexible
nature of the models allows for nonlinear regression functions and non-standard
features in the errors, such as asymmetry or multi-modality. The model has
interpretable covariate-dependent weights constructed through normalization,
allowing for combinations of categorical and continuous covariates.
Computational difficulties for inference are overcome through an adaptive
truncation algorithm combining adaptive Metropolis-Hastings and sequential
Monte Carlo to create a sequence of automatically truncated posterior mixtures.
For our study on Colombian women's life patterns, a variety of quantities are
visualised and described, and in particular, our findings highlight the
detrimental impact of family violence on women's choices and behaviors.Comment: to appear in Bayesian analysi
The Italian version of the Physical Therapy Patient Satisfaction Questionnaire - [PTPSQ-I(15)]: psychometric properties in a sample of inpatients
Background: In a previous study we described the translation, cultural adaptation, and validation of the Italian version of the PTPSQ [PTPSQ-I(15)] in outpatients. To the authors' knowledge, the PTPSQ was never studied in a hospital setting.The aims of this study were: (1) to establish the psychometric properties of the Physical Therapy Patient Satisfaction Questionnaire [PTPSQ- I(15)] in a sample of Italian inpatients, and (2) to investigate the relationships between the characteristics of patients and physical therapists and the indicators of satisfaction. Methods. The PTPSQ-I(15) was administered to inpatients in a Physical Medicine and Rehabilitation Unit. Reliability of the PTPSQ-I(15) was measured by internal consistency (Cronbach's ) and test-retest stability (ICC 3,1). The internal structure was investigated by factor analysis. Divergent validity was measured by comparing the PTPSQ-I(15) with a Visual Analogue Scale (VAS) for pain and with a 5-point Likert-type scale evaluating the Global Perceived Effect (GPE) of the physical therapy treatment. Results: The PTPSQ-I(15) was administered to 148 inpatients, and 73 completed a second administration. The PTPSQ-I(15) showed high internal consistency ( = 0.949) and test-retest stability (ICC = 0.996). Divergent validity was moderate for the GPE (r = - 0.502, P < 0.001) and strong for the VAS (r = -0.17, P = 0.07). Factor analysis showed a one-factor structure. Conclusions: The administration of PTPSQ-I(15) to inpatients demonstrated strong psychometric properties and its use can be recommended with Italian-speaking population. Further studies are suggested on the concurrent validity and on the psychometric properties of the PTPSQ-I(15) in different hospital settings or with other pathological condition
Life-course perspective on personality traits and fertility with sequence analysis
We investigate the link between personality traits (PTs) and fertility, accounting for the possible interplay with other key life course events. Using data from German Socio-Economic Panel survey, we build sequence-type representations of fertility, union and job careers between the ages of 20 and 40. We rely on multichannel sequence analysis (MSA) and on the Partitioning around Medoids algorithm to cluster individuals with similar experiences, and relate clusters to PTs via multinomial regression. We also develop a procedure to apply standard and MSA to truncated trajectories. This enables inclusion of individuals whose trajectories were otherwise observed for a limited age span, notably belonging to younger cohorts. We show that PTs relate to these (portions of) life-course trajectories, of which fertility is only one outcome
Sequence analysis: its past, present, and future
This article marks the occasion of Social Science Research’s 50th anniversary by reflecting on the
progress of sequence analysis (SA) since its introduction into the social sciences four decades ago,
with focuses on the developments of SA thus far in the social sciences and on its potential future
directions.
The application of SA in the social sciences, especially in life course research, has mushroomed
in the last decade and a half. Using a life course analogy, we examined the birth of SA in the social
sciences and its childhood (the first wave), its adolescence and young adulthood (the second
wave), and its future mature adulthood in the paper.
The paper provides a summary of (1) the important SA research and the historical contexts in
which SA was developed by Andrew Abbott, (2) a thorough review of the many methodological
developments in visualization, complexity measures, dissimilarity measures, group analysis of
dissimilarities, cluster analysis of dissimilarities, multidomain/multichannel SA, dyadic/polyadic
SA, Markov chain SA, sequence life course analysis, sequence network analysis, SA in other social
science research, and software for SA, and (3) reflections on some future directions of SA
including how SA can benefit and inform theory-making in the social sciences, the methods
currently being developed, and some remaining challenges facing SA for which we do not yet
have any solutions. It is our hope that the reader will take up the challenges and help us improve
and grow SA into maturity
A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden
Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2–43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3–25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4–2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4–30.4%), 8.8% (95%CI: 7.3–10.5%) and 0.4% (95%CI: 0.1–0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0–0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9–19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1–2%) and 19.2% (95%CI: 10.9–30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3–21) days; the length of stay in ICU was 11 (IQR: 6–19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions
Infection fatality ratio of SARS-CoV-2 in Italy
We analyzed 5,484 close contacts of COVID-19 cases from Italy, all of them
tested for SARS-CoV-2 infection. We found an infection fatality ratio of 2.2%
(95%CI 1.69-2.81%) and identified male sex, age >70 years, cardiovascular
comorbidities, and infection early in the epidemics as risk factors for death
Probability of symptoms and critical disease after SARS-CoV-2 infection
We quantified the probability of developing symptoms (respiratory or fever
\geq 37.5 {\deg}C) and critical disease (requiring intensive care or resulting
in death) of SARS-CoV-2 positive subjects. 5,484 contacts of SARS-CoV-2 index
cases detected in Lombardy, Italy were analyzed, and positive subjects were
ascertained via nasal swabs and serological assays. 73.9% of all infected
individuals aged less than 60 years did not develop symptoms (95% confidence
interval: 71.8-75.9%). The risk of symptoms increased with age. 6.6% of
infected subjects older than 60 years had critical disease, with males at
significantly higher risk.Comment: sample increased: results updated with new records coming from the
ongoing serological survey
Sequence analysis: Its past, present, and future
This article marks the occasion of Social Science Research's 50th anniversary by reflecting on the progress of sequence analysis (SA) since its introduction into the social sciences four decades ago, with focuses on the developments of SA thus far in the social sciences and on its potential future directions. The application of SA in the social sciences, especially in life course research, has mushroomed in the last decade and a half. Using a life course analogy, we examined the birth of SA in the social sciences and its childhood (the first wave), its adolescence and young adulthood (the second wave), and its future mature adulthood in the paper. The paper provides a summary of (1) the important SA research and the historical contexts in which SA was developed by Andrew Abbott, (2) a thorough review of the many methodological developments in visualization, complexity measures, dissimilarity measures, group analysis of dissimilarities, cluster analysis of dissimilarities, multidomain/multichannel SA, dyadic/polyadic SA, Markov chain SA, sequence life course analysis, sequence network analysis, SA in other social science research, and software for SA, and (3) reflections on some future directions of SA including how SA can benefit and inform theory-making in the social sciences, the methods currently being developed, and some remaining challenges facing SA for which we do not yet have any solutions. It is our hope that the reader will take up the challenges and help us improve and grow SA into maturity.</p
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