Background/Objectives: Topical imiquimod is an established non-invasive treatment for superficial basal cell carcinoma (sBCC). However, data on treatment-associated changes in tumor microvascularization remain limited. This study investigated vascular changes before and after imiquimod therapy using multimodal non-invasive imaging. Methods: In this single-center, prospective observational study, 31 basal cell carcinomas in 20 patients were examined before and 12–16 weeks after topical imiquimod therapy (5%, five times weekly for six weeks) using dermoscopy, dynamic optical coherence tomography (D-OCT), and line-field confocal optical coherence tomography (LC-OCT). Analyses were performed as paired before-and-after comparisons. While approved for sBCC, a small number of thin nodular and infiltrative BCCs were included exploratorily; subgroup analyses were not powered. Results: Dermoscopy showed a nominally significant shift toward smaller vessel diameter categories after therapy (ATS = 8.183, df = 1, p = 0.004). D-OCT-derived parameters (vessel density, vessel diameter, and depth of the vascular plexus) did not show nominally significant changes. LC-OCT showed nominally lower apparent intratumoral flow scores (ATS = 13.285, df = 1, p < 0.001), reduced occurrence of vessel-wall-associated intraluminal structures showing a rolling-like motion pattern (86.7% before treatment versus 33.3% after treatment; ATS = 13.357; df = 1, p < 0.001), and a reduction in maximum vessel diameter (ATS = 6.110, df = 1, p = 0.013). The primary LC-OCT inferential analyses were performed at the lesion level without adjustment for within-patient clustering and should therefore be interpreted as exploratory. An additional patient-cluster-adjusted paired change-score sensitivity analysis for LC-OCT maximum vessel diameter yielded a directionally consistent estimate (−17.81 µm; 95% CI: −34.40 to −1.23; p = 0.037). The primary exploratory endpoints were LC-OCT–based apparent intratumoral flow and maximum vessel diameter; secondary endpoints included dermoscopic and D-OCT–based vascular parameters. In the exploratory response-stratified analysis, the change in LC-OCT-based maximum vessel diameter did not differ significantly among the assigned response groups (Kruskal–Wallis H = 3.870, df = 2, raw p = 0.144; BH-adjusted p = 0.753). Conclusions: LC-OCT detected several exploratory vascular changes between the pre-treatment examination and follow-up and may provide complementary information for the non-invasive assessment of BCC after imiquimod therapy. Given the exploratory design, limited sample size, and lack of systematic histological confirmation, these findings are hypothesis-generating and require validation in larger prospective studies.

Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in advance and does not account for data-driven variable selection. As a consequence, naive post-selection inference may be biased and misleading. In right-censored survival settings, these issues may be further exacerbated by the additional uncertainty induced by censoring. We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso, in the context of the Cox model. The methods considered include sample splitting, post-selection inference procedures that condition explicitly on the Lasso selection event, and the debiased Lasso. Because these methods address different inferential targets, we distinguish selected-submodel targets from full-model targets and interpret empirical coverage, interval width, power, and type I error accordingly. Their performance is examined in a neutral simulation study reflecting realistic covariate structures and censoring rates commonly encountered in biomedical applications. The primary focus is post-selection inference after Cox-Lasso variable selection, not a comprehensive benchmark of very-high-dimensional variable-selection performance. To complement the simulation results, we illustrate the practical behavior of these procedures in an applied example using a publicly available survival dataset.

Aim: To conduct a psychometric validation and to evaluate the clinical relevance of the Quality of Life after Brain Injury in children aged 6 to 7 years (QOLIBRI‐KIDDY) self‐reported and proxy‐ or parent‐reported questionnaires for traumatic brain injury (TBI)‐specific health‐related quality of life (HRQoL). Method: One hundred and seven parent–child dyads completed the QOLIBRI‐KIDDY, generic HRQoL, and symptom assessments. Analyses examined internal consistency, test–retest reliability, factorial structure, construct and known‐group validity, and International Classification of Functioning, Disability and Health (ICF) item linking. Results: Both versions demonstrated good‐to‐excellent total score internal consistencies (Cronbach's α and McDonald ω > 0.8) and acceptable‐to‐good subscale consistencies (0.34–0.79). Acceptable (proxy) to good (self) test–retest reliability (intraclass correlation coefficient = 0.60–0.73) and a 6‐scale structure (cognition, self, daily life and autonomy, social relationships, emotions, physical problems) was found. TBI‐specific HRQoL correlated moderately to strongly with generic health status (self‐report Pearson's r = 0.57; parent‐report r = 0.71). Poorer proxy‐report HRQoL was associated with more health problems, anxiety, depression, and more severe TBI, with medium‐to‐large effects (Cohen's d = 0.45–1.28). Items were mapped to 27 two‐level ICF categories. Interpretation: Both instruments are reliable and valid, enabling TBI‐specific HRQoL assessment over the lifespan. Identifying the subjective meaning of disability by covering key ICF domains assists early impairment detection, planning of individualized care, and prevention of long‐term adverse outcomes.

Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in advance and does not account for data-driven variable selection. As a consequence, naive post-selection inference may be biased and misleading. In right-censored survival settings, these issues may be further exacerbated by the additional uncertainty induced by censoring. We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso, in the context of the Cox model. The methods considered include sample splitting, exact post-selection inference, and the debiased Lasso. Their performance is examined in a neutral simulation study reflecting realistic covariate structures and censoring rates commonly encountered in biomedical applications. To complement the simulation results, we illustrate the practical behavior of these procedures in an applied example using a publicly available survival dataset.