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.

Background: Prostate cancer (PCa) represents a significant healthcare problem. The critical clinical question is the need for a biopsy. Accurate risk stratification of patients before a biopsy can allow for individualised risk stratification thus improving clinical decision making. This study aims to build a risk calculator to inform the need for a prostate biopsy. Methods: Using the clinical information of 4801 patients an Irish Prostate Cancer Risk Calculator (IPRC) for diagnosis of PCa and high grade (Gleason ≥7) was created using a binary regression model including age, digital rectal examination, family history of PCa, negative prior biopsy and Prostate-specific antigen (PSA) level as risk factors. The discrimination ability of the risk calculator is internally validated using cross validation to reduce overfitting, and its performance compared with PSA and the American risk calculator (PCPT), Prostate Biopsy Collaborative Group (PBCG) and European risk calculator (ERSPC) using various performance outcome summaries. In a subgroup of 2970 patients, prostate volume was included. Separate risk calculators including the prostate volume (IPRCv) for the diagnosis of PCa (and high-grade PCa) was created. Results: IPRC area under the curve (AUC) for the prediction of PCa and high-grade PCa was 0.6741 (95% CI, 0.6591 to 0.6890) and 0.7214 (95% CI, 0.7018 to 0.7409) respectively. This significantly outperforms the predictive ability of cancer detection for PSA (0.5948), PCPT (0.6304), PBCG (0.6528) and ERSPC (0.6502) risk calculators; and also, for detecting high-grade cancer for PSA (0.6623) and PCPT (0.6804) but there was no significant improvement for PBCG (0.7185) and ERSPC (0.7140). The inclusion of prostate volume into the risk calculator significantly improved the AUC for cancer detection (AUC = 0.7298; 95% CI, 0.7119 to 0.7478), but not for high-grade cancer (AUC = 0.7256; 95% CI, 0.7017 to 0.7495). The risk calculator also demonstrated an increased net benefit on decision curve analysis. Conclusion: The risk calculator developed has advantages over prior risk stratification of prostate cancer patients before the biopsy. It will reduce the number of men requiring a biopsy and their exposure to its side effects. The interactive tools developed are beneficial to translate the risk calculator into practice and allows for clarity in the clinical recommendations.

Background: Prostate cancer (PCa) is the second most common cancer among men worldwide. Current diagnostic methods often lack sufficient sensitivity and specificity, leading to unnecessary biopsy. With growing use of MRI and EAU guideline recommendations, this review synthesised evidence on MRI-based risk calculators (RCs) for PCa diagnosis and compared their performance with traditional clinical RCs. Methods: A systematic search of Embase, Medline, Scopus, Cochrane Library, and Web of Science databases assessed the discriminatory ability of MRI-based RCs using Area Under the Curve (AUC). A meta-analysis was conducted to pool AUC estimates, assess heterogeneity, and compare the differences in discriminatory ability. Results: Of 2049 papers, 16 met the inclusion criteria. MRI-based RCs showed increased discrimination, with an AUC of 0.84 (95% CI: 0.81–0.86) for clinically significant PCa (csPCa), compared to 0.76 (95% CI: 0.73–0.79) for clinical models, and an AUC of 0.81 (95% CI: 0.78–0.84) for all PCa, compared to 0.74 (95% CI: 0.68–0.79). The pooled logit(AUC) difference was 0.49 units for csPCa and 0.37 units for all PCa. High heterogeneity was noted, likely due to PCa variability, and 31% of the studies had a high or unclear risk of bias, potentially affecting generali s ability. Conclusions: MRI-based RCs improve the diagnostic accuracy for PCa with the potential to reduce unnecessary biopsies and optimi s e healthcare resources, thereby supporting their integration into clinical practice.

Breast cancer represents a heterogeneous condition in which the interaction between host immune response and primary oncogenic events can impact disease progression. Ratios of systemic blood-based immunocytes have emerged as clinically-relevant prognostic biomarkers in cancer patients. The NLR (neutrophil-to-lymphocyte ratio) has been shown to be prognostic in a variety of cancers, including breast cancer. However, evaluation of the prognostic value for overall survival (OS) and disease-free survival (DFS) of other key immunocyte ratios—neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-white cell count ratio (NWR), lymphocyte-to-white cell count ratio (LWR), monocyte-to-white cell count ratio (MWR), platelet-to-lymphocyte (PLR)—by breast cancer subtypes in a neoadjuvant chemotherapy (NAC) cohort remains to be fully explored. An NAC-treated breast cancer cohort, comprised of Luminal A, Luminal B, HER2-positive, and triple negative/basal breast cancers, treated at a tertiary referral center (minimum 3-year follow-up), was used to calculate immunocyte ratios and immunocyte cut-off values, calculated with >80% specificity (using decision tree modeling). The association with subtype-specific OS, DFS, and tumor grade was analyzed using cut offs calculated using both receiver operating characteristic curves and decision tree modelling. Decision tree calculated ratios showed that LMR (5.29) and MWR (0.06) were significantly associated with Luminal A OS (p = 0.004 and p = 0.022) and DFS (p = 0.004 and p = 0.022), and Luminal B OS (p = 0.027 and p = 0.008) and DFS (p = 0.005 and p = 0.007). NLR (1.79) and LWR (0.30) were significantly associated with HER2-positive OS (p = 0.013 and p = 0.043). NLR (1.79) and NWR (0.62) were significantly associated with DFS (p = 0.035 and p = 0.021). No significant association we observed between any immunocyte ratio in the triple negative cohort. Our results demonstrate the subtype-specific prognostic value of immunocyte ratios in NAC-treated breast cancer patients. Further validation of immunocyte ratios will provide clinicians with a new prognostic aid for disease management and monitoring.

Information graphics or infographics combine visual representations of information or data with text. They have been used in health research to disseminate research findings, translate knowledge and address challenges in health communication to lay audiences. There is emerging evidence of the design of infographics with the involvement of patients and the public in health research. Approaches to involvement include public and patient involvement, patient engagement and participatory research approaches. To date, there has been no comprehensive review of the literature on the design of infographics with patients and the public in health research. This paper presents a protocol and methodological framework for a scoping review to identify and map the available evidence for the involvement of patients and the public in infographics design in health research. It has been informed by preliminary searches and discussions and will guide the conduct and reporting of this review.

Background: OncotypeDX Recurrence Score© (RS) is a commercially available 21-gene expression assay which estimates prognosis and guides chemoendocrine prescription in early-stage estrogen-receptor positive, human epidermal growth factor receptor-2-negative (ER+/HER2−) breast cancer. Limitations of RS testing include the cost and turnaround time of several weeks. Aim: Our aim is to develop a user-friendly surrogate nomogram capable of predicting RS. Methods: Multivariable linear regression analyses were performed to determine predictors of RS and RS > 25. Receiver operating characteristic analysis produced an area under the curve (AUC) for each model, with training and test sets were composed of 70.3% (n = 315) and 29.7% (n = 133). A dynamic, user-friendly nomogram was built to predict RS using R (version 4.0.3). Results: 448 consecutive patients who underwent RS testing were included (median age: 58 years). Using multivariable regression analyses, postmenopausal status (β-Coefficient: 0.25, 95% confidence intervals (CIs): 0.03–0.48, p = 0.028), grade 3 disease (β-Coefficient: 0.28, 95% CIs: 0.03–0.52, p = 0.026), and estrogen receptor (ER) score (β-Coefficient: −0.14, 95% CIs: −0.22–−0.06, p = 0.001) all independently predicted RS, with AUC of 0.719. Using multivariable regression analyses, grade 3 disease (odds ratio (OR): 5.67, 95% CIs: 1.32–40.00, p = 0.037), decreased ER score (OR: 1.33, 95% CIs: 1.02–1.66, p = 0.050) and decreased progesterone receptor score (OR: 1.16, 95% CIs: 1.06–1.25, p = 0.002) all independently predicted RS > 25, with AUC of 0.740 for the static and dynamic online nomogram model. Conclusions: This study designed and validated an online user-friendly nomogram from routinely available clinicopathological parameters capable of predicting outcomes of the 21-gene RS expression assay.