STUDYING TRAVEL-RELATED INDIVIDUAL ASSESSMENTS AND DESIRES BY COMBINING HIERARCHICALLY STRUCTURED ORDINAL VARIABLES

Como se estuda o que não se diz: uma revisão sobre demanda oculta

Objetivos: Pessoas com demandas aparentemente bem delimitadas podem, muitas vezes, ter queixas ou preocupações ocultas ou tardiamente apresentadas, chamadas em inglês de hidden agenda, by the way syndrome ou doorknob syndrome (“síndrome da maçaneta”). Poucos trabalhos abordam o fenômeno em contexto clínico discutindo seus aspectos ou consequências sobre o cuidado do ponto de vista sociocultural. Uma pesquisa qualitativa sobre saúde dos homens realizada pelos autores exigiu estudar a produção científica relacionada à hidden agenda de forma descritiva e interpretativa. Métodos: Esta revisão, do tipo estado da questão, abarcou artigos que tratassem da hidden agenda (ou expressões similares) no contexto clínico geral ou especializado, médico ou multiprofissional, em português, inglês ou espanhol, no período de 2000 a 2014. Partindo de 210 resultados iniciais em 3 bases de dados internacionais, foram selecionadas 39 publicações, que foram avaliadas quanto a características objetivas e subjetivas. Resultados: Identificando os temas predominantes nas pesquisas, notamos que hidden agenda é a expressão mais específica, sendo demanda oculta sua melhor tradução. Quase todos os estudos relacionavam a atenção a demandas ocultas a melhores desfechos ou desdobramentos do atendimento, embora nenhum avaliasse essas consequências de forma aprofundada. Conclusão: A revisão deixa claro que a demanda oculta é um elemento problematizador para pensar a prática médica e potencializador para a clínica, especialmente ao buscar-se um cuidado integral e um diálogo efetivo entre profissional e usuário(a). São necessários estudos que articulem o fenômeno a aspectos socioculturais ou tecnoassistenciais e que contemplem alguma discussão sobre construção da demanda, ausente nos artigos revisados.

From single-SNP to wide-locus: genome-wide association studies identifying functionally related genes and intragenic regions in small sample studies

BACKGROUND

Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts.

METHODS

Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (µStat) are not confounded by unrealistic assumptions (e.g., linearity, independence).

RESULTS

By incorporating knowledge about relationships between SNPs, µGWAS (GWAS based on µStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes.

CONCLUSION

With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development.

Evaluation metrics for biostatistical and epidemiological collaborations

Increasing demands for evidence-based medicine and for the translation of biomedical research into individual and public health benefit have been accompanied by the proliferation of special units that offer expertise in biostatistics, epidemiology, and research design (BERD) within academic health centers. Objective metrics that can be used to evaluate, track, and improve the performance of these BERD units are critical to their successful establishment and sustainable future. To develop a set of reliable but versatile metrics that can be adapted easily to different environments and evolving needs, we consulted with members of BERD units from the consortium of academic health centers funded by the Clinical and Translational Science Award Program of the National Institutes of Health. Through a systematic process of consensus building and document drafting, we formulated metrics that covered the three identified domains of BERD practices: the development and maintenance of collaborations with clinical and translational science investigators, the application of BERD-related methods to clinical and translational research, and the discovery of novel BERD-related methodologies. In this article, we describe the set of metrics and advocate their use for evaluating BERD practices. The routine application, comparison of findings across diverse BERD units, and ongoing refinement of the metrics will identify trends, facilitate meaningful changes, and ultimately enhance the contribution of BERD activities to biomedical research.

Nonparametric methods for molecular biology

In 2003, the completion of the Human Genome Project (1) together with advances in computational resources (2) were expected to launch an era where the genetic and genomic contributions to many common diseases would be found. In the years following, however, researchers became increasingly frustrated as most reported 'findings' could not be replicated in independent studies (3). To improve the signal/noise ratio, it was suggested to increase the number of cases to be included to tens of thousands (4), a requirement that would dramatically restrict the scope of personalized medicine. Similarly, there was little success in elucidating the gene-gene interactions involved in complex diseases or even in developing criteria for assessing their phenotypes. As a partial solution to these enigmata, we here introduce a class of statistical methods as the 'missing link' between advances in genetics and informatics. As a first step, we provide a unifying view of a plethora of nonparametric tests developed mainly in the 1940s, all of which can be expressed as u-statistics. Then, we will extend this approach to reflect categorical and ordinal relationships between variables, resulting in a flexible and powerful approach to deal with the impact of (1) multiallelic genetic loci, (2) poly-locus genetic regions, and (3) oligo-genetic and oligo-genomic collaborative interactions on complex phenotypes.

U-Scores for Multivariate Data in Sports

In many sport competitions athletes, teams, or countries are evaluated based on several variables. The strong assumptions underlying traditional 'linear weight' scoring systems (that the relative importance, interactions and linearizing transformations of the variables are known) can often not be justified on theoretical grounds, and empirical 'validation' of weights, interactions and transformations, is problematic when a 'gold standard' is lacking. With μ-scores (u-scores for multivariate data) one can integrate information even if the variables have different scales and unknown interactions or if the events counted are not directly comparable, as long as the variables have an 'orientation'. Using baseball as an example, we discuss how measures based on μ-scores can complement the existing measures for 'performance' (which may depend on the situation) by providing the first multivariate measures for 'ability' (which should be independent of the situation). Recently, μ-scores have been extended to situations where count variables are graded by importance or relevance, such as medals in the Olympics (Wittkowski 2003) or Tour-de-France jerseys (Cherchye and Vermeulen 2006, 2007). Here, we present extensions to 'censored' variables (life-time achievements of active athletes), penalties (counting a win more than two ties) and hierarchically structured variables (Nordic, alpine, outdoor, and indoor Olympic events). The methods presented are not restricted to sports. Other applications of the method include medicine (adverse events), finance (risk analysis), social choice theory (voting), and economy (long-term profit).