Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models.

Our findings are consistent with recent global trends toward increasing iron use. Such trends may have economic implications given the high cost of ESAs and the relative affordability of iron. In addition, the potential harm of excessive iron dosing may need to be considered.

Stromal tumour infiltrating lymphocytes (sTILs) are a strong prognostic marker in triple negative breast cancer (TNBC). Consistency scoring sTILs is good and was excellent when an internet-based scoring aid developed by the TIL-WG was used to score cases in a reproducibility study. This study aimed to evaluate the reproducibility of sTILs assessment using this scoring aid in cases from routine practice and to explore the potential of the tool to overcome variability in scoring. Twenty-three breast pathologists scored sTILs in digitized slides of 49 TNBC biopsies using the scoring aid. Subsequently, fields of view (FOV) from each case were selected by one pathologist and scored by the group using the tool. Inter-observer agreement was good for absolute sTILs (ICC 0.634, 95% CI 0.539–0.735, p < 0.001) but was poor to fair using binary cutpoints. sTILs heterogeneity was the main contributor to disagreement. When pathologists scored the same FOV from each case, inter-observer agreement was excellent for absolute sTILs (ICC 0.798, 95% CI 0.727–0.864, p < 0.001) and good for the 20% (ICC 0.657, 95% CI 0.561–0.756, p < 0.001) and 40% (ICC 0.644, 95% CI 0.546–0.745, p < 0.001) cutpoints. However, there was a wide range of scores for many cases. Reproducibility scoring sTILs is good when the scoring aid is used. Heterogeneity is the main contributor to variance and will need to be overcome for analytic validity to be achieved.

In a clinical setting, biomarkers are typically measured and evaluated as biological indicators of a physiological state. Population based reference ranges, known as ‘static’ or ‘normal’ reference ranges, are often used as a tool to classify a biomarker value for an individual as typical or atypical. However, these ranges may not be informative to a particular individual when considering changes in a biomarker over time since each observation is assessed in isolation and against the same reference limits. To allow early detection of unusual physiological changes, adaptation of static reference ranges is required that incorporates within-individual variability of biomarkers arising from longitudinal monitoring in addition to between-individual variability. To overcome this issue, methods for generating individualised reference ranges are proposed within a Bayesian framework which adapts successively whenever a new measurement is recorded for the individual. This new Bayesian approach also allows the within-individual variability to differ for each individual, compared to other less flexible approaches. However, the Bayesian approach usually comes with a high computational cost, especially for individuals with a large number of observations, that diminishes its applicability. This difficulty suggests that a computational approximation may be required. Thus, methods for generating individualised adaptive ranges by the use of a time-efficient approximate Expectation-Maximisation (EM) algorithm will be presented which relies only on a few sufficient statistics at the individual level.

Objective: It is theorised that adhesive-free wound care developed specifically for patients with hidradenitis suppurativa (HS) can improve their quality of life (QoL). Our study aimed to investigate the impact of a novel wound care device on Dermatology Life Quality Index (DLQI) scores, and other factors related to experienced pain, time spent changing dressings, comfort, ease of use and body image. Method: A 21-day, single-arm, unblinded, pilot trial was conducted to assess ease of use and the impact of effective wound care on various aspects of wound management in patients with HS. Participants were provided two trial garments and trial dressings as required, to use over a 21-day period in the home setting. A seven-item questionnaire and the DLQI questionnaire was completed on days 0, 7, 14 and 21. Results: All 15 participants were female, aged >18 years old and with a diagnosis of HS. Mean DLQI score at baseline (day 0) was 19.3, which was reduced to 4.53 on day 21, a significant improvement in 100% of participants (p<0.001). High levels of dressing-related pain, assessed using an 11-point Visual Analogue Scale, reduced from 5.53 at baseline to 0.8 on day 21. Other significant improvements in terms of patient comfort, time spent on changing dressings, body confidence and the dressing's ability to retain exudate were also noted. Conclusion: The results illustrated the improvement made to study participants' day-to-day activities and QoL when effective HS-specific wound care products were provided. Wound care is an essential component in the treatment journey of patients.