Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Life experiences throughout the ifespan: What do people say (or not) about them?

Life experiences have been a topic of interest for researchers and clinicians for decades. Current knowledge is rooted in two distinct approaches, i.e., personality psychology and psychosomatics. Whereas the first is interested in ordinary life stories of nonclinical individuals, based on a more qualitative, in-depth, and person-driven approach, psychosomatics stresses negative events, mainly in clinical samples, and presents a more quantitative, general, and construct-driven approach. Consequently, available evidence is dispersed and unrelated and many basic questions remain unanswered. This study aimed to explore occurrence, developmental stage, valence, and impact of life experiences and to analyze critical answering patterns (i.e., “I don’t remember,” missingness). Through a cross-sectional retrospective design, 394 adults from the community answered the Lifetime Experiences Scale, which covers 75 life experiences organized in eight domains (i.e., school, job, health, leisure, living conditions, adverse experiences, achievements, and people and relationships). Occurrence of life experiences varied greatly, and the mean number of experiences reported was approximately 30. Regarding developmental stage, most experiences were reported in just one stage—mainly adulthood—however, some could be considered chronic. Globally, life experiences tended to be clearly rated as positive or as negative; additionally, assessed experiences were mainly appraised as positive. Moreover, participants presented their experiences as significant, rating them as high impact. Overall, critical answering patterns were not very expressive: “I don’t remember” and missing answers were below 2 and 5%, respectively, in the majority of experiences. These findings offer several important new insights, suggesting that life experiences are mainly an idiosyncratic topic.This manuscript is part of a doctoral dissertation, which had the support of the Portuguese Foundation for Science and Technology (FCT), through the PhD grant with the reference SFRH/ BD/76022/2011, funded by POPH-QREN-Typology 4.1-Advanced Training, reimbursed by the European Social Fund and national funds from State Budget. This study was conducted at Psychology Research Centre (UID/PSI/01662/2013), University of Minho, and supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Education and Science through national funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653).info:eu-repo/semantics/publishedVersio

Search for pairs of highly collimated photon-jets in pp collisions at √s = 13 TeV with the ATLAS detector

Results of a search for the pair production of photon-jets—collimated groupings of photons—in the ATLAS detector at the Large Hadron Collider are reported. Highly collimated photon-jets can arise from the decay of new, highly boosted particles that can decay to multiple photons collimated enough to be identified in the electromagnetic calorimeter as a single, photonlike energy cluster. Data from proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 36.7  fb−1, were collected in 2015 and 2016. Candidate photon-jet pair production events are selected from those containing two reconstructed photons using a set of identification criteria much less stringent than that typically used for the selection of photons, with additional criteria applied to provide improved sensitivity to photon-jets. Narrow excesses in the reconstructed diphoton mass spectra are searched for. The observed mass spectra are consistent with the Standard Model background expectation. The results are interpreted in the context of a model containing a new, high-mass scalar particle with narrow width, X, that decays into pairs of photon-jets via new, light particles, a. Upper limits are placed on the cross section times the product of branching ratios σ×B(X→aa)×B(a→γγ)2 for 200  GeV<mX<2  TeV and for ranges of ma from a lower mass of 100 MeV up to between 2 and 10 GeV, depending upon mX. Upper limits are also placed on σ×B(X→aa)×B(a→3π0)2 for the same range of mX and for ranges of ma from a lower mass of 500 MeV up to between 2 and 10 GeV