Sibship Size, Height and Cohort Selection: A Methodological Approach

This article deals with the historical relationship between the number of siblings in a family or household and height, a proxy for biological living standards. Ideally, this relationship is better assessed when we have evidence on the exact number of siblings in a family from its constitution onwards. However, this generally requires applying family reconstitution techniques, which, unfortunately, is not always possible. In this latter case, scholars must generally settle for considering only particular benchmark years using population censuses, from which family and household structures are derived. These data are then linked to the height data for the young males of the family or household. Height data are generally obtained from military records. In this matching process, several decisions have to be taken, which, in turn, are determined by source availability and the number of available observations. Using data from late 19th-century Catalonia, we explore whether the methodology used in matching population censuses and military records as described above might affect the relationship between sibship size and biological living standards and, if so, to what extent. We conclude that, while contextual factors cannot be neglected, the methodological decisions made in the initial steps of research also play a role in assessing this relationship.

Analytic approaches to clinical validation of results from preclinical models of glioblastoma: a systematic review

BackgroundAnalytic approaches to clinical validation of results from preclinical models are important in assessment of their relevance to human disease. This systematic review examined consistency in reporting of glioblastoma cohorts from The Cancer Genome Atlas (TCGA) and assessed whether studies included patient characteristics in their survival analyses.MethodsWe searched Embase and Medline on 02Feb21 for studies using preclinical models of glioblastoma published after Jan2008 that used data from TCGA to validate the association between at least one molecular marker and overall survival in adult patients with glioblastoma. Main data items included cohort characteristics, statistical significance of the survival analysis, and model covariates.ResultsThere were 58 eligible studies from 1,751 non-duplicate records investigating 126 individual molecular markers. In 14 studies published between 2017 and 2020 using TCGA RNA microarray data that should have the same cohort, the median number of patients was 464.5 (interquartile range 220.5-525). Of the 15 molecular markers that underwent more than one univariable or multivariable survival analyses, five had discrepancies between studies. Covariates used in the 17 studies that used multivariable survival analyses were age (76.5%), pre-operative functional status (35.3%), sex (29.4%) MGMT promoter methylation (29.4%), radiotherapy (23.5%), chemotherapy (17.6%), IDH mutation (17.6%) and extent of resection (5.9%).ConclusionsPreclinical glioblastoma studies that used TCGA for validation did not provide sufficient information about their cohort selection and there were inconsistent results. Transparency in reporting and the use of analytic approaches that adjust for clinical variables can improve the reproducibility between studies.Importance of the StudyDespite using the same data from The Cancer Genome Atlas, translational preclinical studies in glioblastoma research included different numbers of patients into their analyses and their results were inconsistent.Fewer than a third of the studies used multivariable survival analysis to adjust for clinical variables but most did not take treatment factors into account.Greater transparency in cohort selection from open access data and integration of clinical variables into analyses will help improve reproducibility in glioblastoma research.

A Recommender System to Help Refining Clinical Research Studies

The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.