Decomposition of Explained Variation in the Linear Mixed Model

In the linear mixed model (LMM), the simultaneous assessment and comparison of dispersion relevance of explanatory variables associated with fixed and random effects remains an important open practical problem. Based on the restricted maximum likelihood equations in the variance components form of the LMM, we prove a proper decomposition of the sum of squares of the dependent variable into unbiased estimators of interpretable estimands of explained variation. This result leads to a natural extension of the well-known adjusted coefficient of determination to the LMM. Further, we allocate the novel unbiased estimators of explained variation to specific contributions of covariates associated with fixed and random effects within a single model fit. These parameter-wise explained variations constitute easily interpretable quantities, assessing dispersion relevance of covariates associated with both fixed and random effects on a common scale, thus allowing for a covariate ranking. For illustration, we contrast the variation explained by subjects and time in the longitudinal sleep deprivation study. By comparing the dispersion relevance of population characteristics and spatial levels, we determine literacy as a major driver of income inequality in Burkina Faso. Finally, we develop a novel relevance plot to visualize the dispersion relevance of high-dimensional genomic markers in Arabidopsis thaliana

The finite sample performance of semi- and nonparametric estimators for treatment effects and policy evaluation

This paper investigates the fi nite sample performance of a comprehensive set of semi- and nonparametric estimators for treatment and policy evaluation. In contrast to previous simulation studies which mostly considered semiparametric approaches relying on parametric propensity score estimation, we also consider more fl exible approaches based on semi- or nonparametric propensity scores, nonparametric regression, and direct covariate matching. In addition to (pair, radius, and kernel) matching, inverse probability weighting, regression, and doubly robust estimation, our studies also cover recently proposed estimators such as genetic matching, entropy balancing, and empirical likelihood estimation. We vary a range of features (sample size, selection into treatment, effect heterogeneity, and correct/misspecification) in our simulations and fi nd that several nonparametric estimators by and large outperform commonly used treatment estimators using a parametric propensity score. Nonparametric regression, nonparametric doubly robust estimation, nonparametric IPW, and one-to-many covariate matching perform best

Sensitivity and specificity of antibodies against HPV16 E6 and other early proteins for the detection of HPV16-driven oropharyngeal squamous cell carcinoma

To determine the sensitivity and specificity of HPV16 serology as diagnostic marker for HPV16-driven oropharyngeal squamous cell carcinoma (OPSCC), 214 HNSCC patients from Germany and Italy with fresh-frozen tumor tissues and sera collected before treatment were included in this study. Hundred and twenty cancer cases were from the oropharynx and 94 were from head and neck cancer regions outside the oropharynx (45 oral cavity, 12 hypopharynx and 35 larynx). Serum antibodies to early (E1, E2, E6 and E7) and late (L1) HPV16 proteins were analyzed by multiplex serology and were compared to tumor HPV RNA status as the gold standard. A tumor was defined as HPV-driven in the presence of HPV16 DNA and HPV16 transformation-specific RNA transcript patterns (E6*I, E1∧E4 and E1C). Of 120 OPSCC, 66 (55%) were HPV16-driven. HPV16 E6 seropositivity was the best predictor of HPV16-driven OPSCC (diagnostic accuracy 97% [95%CI 92–99%], Cohen's kappa 0.93 [95%CI 0.8–1.0]). Of the 66 HPV-driven OPSCC, 63 were HPV16 E6 seropositive, compared to only one (1.8%) among the 54 non-HPV-driven OPSCC, resulting in a sensitivity of 96% (95%CI 88–98) and a specificity of 98% (95%CI 90–100). Of 94 HNSCC outside the oropharynx, six (6%) were HPV16-driven. In these patients, HPV16 E6 seropositivity had lower sensitivity (50%, 95%CI 19–81), but was highly specific (100%, 95%CI 96–100). In conclusion, HPV16 E6 seropositivity appears to be a highly reliable diagnostic marker for HPV16-driven OPSCC with very high sensitivity and specificity, but might be less sensitive for HPV16-driven HNSCC outside the oropharynx

Human papillomavirus as prognostic marker with rising prevalence in neck squamous cell carcinoma of unknown primary: A retrospective multicentre study

Patients with neck squamous cell carcinomas of unknown primary tumour (NSCCUP) present with lymph node metastasis without evidence for a primary tumour. Most patients undergo an aggressive multimodal treatment, which induces severe, potentially unnecessary toxicity. Primary tumours of NSCCUP can be hidden in the oropharynx. Human papillomavirus (HPV) is causally involved in a subgroup of oropharyngeal squamous cell carcinomas (OPSCC) associated with early lymph node metastasis and good prognosis. Detection of markers for HPV transformation in NSCCUP could allow focussing on the oropharynx in primary tumour search and could be of value for choice and extent of treatment. In a retrospective multicentre study (Germany, Italy and Spain), we analysed metastatic lymph nodes from 180 NSCCUP patients for the presence of HPV DNA, HPV E6*I mRNA and cellular p16INK4a overexpression, a surrogate marker for HPV-induced transformation. HPV status, defined as positivity for viral mRNA with at least one additional marker, was correlated with clinical parameters and survival outcome. A substantial proportion (16%) of NSCCUP were HPV-driven, mainly by HPV16 (89%). HPV prevalence increased with year of diagnosis from 9% during 1998\u20132004 to 23% during 2005\u20132014 (p = 0.007). HPV-driven NSCCUP had significantly better overall and progression-free survival rates (p 64 0.008). Based on this survival benefit, it is contended that HPV RNA status should be included in NSCCUP diagnosis and in therapeutic decision-making. Deintensification of radiation in patients with HPV-driven NSCCUP, while concurrently concentrating on the oropharynx appears to be a promising therapeutic strategy, the efficacy of which should be assessed in prospective trials. To our knowledge, this is the largest study on HPV in NSCCUP

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training