Comment on “Temporal bone carcinoma: Novel prognostic score based on clinical and histological features”

To the Editor, With great interest we have read the study by Zanoletti et al. in which the authors describe the development of a novel prognostic score based on the clinical and histological characteristics of 44 patients with consecutive temporal bone squamous cell carcinoma (TBSCC) treated with primary en bloc surgery to predict recurrence rates and disease-specific mortality. We strongly encourage the development of prognostic models to predict disease-free survival (DFS) and diseasespecific survival (DSS) in TBSCC patients. However, we noticed several methodological and reporting issues in the study by Zanoletti et al., of which we will discuss the four most important ones below based on two essential guidelines for prediction models, which we will introduce first. Currently, there is an increased interest in prediction research to aid health care providers to estimate the risk that a specific disease or condition is present or will occur in the future in order to support their decision making. However, suboptimally performed or poorly reported prediction research is likely to have limited reliability and applicability and will, therefore, be of little use in patient management. Therefore, tools such as TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) and PROBAST (Prediction model Risk Of Bias ASsessment Tool) have been developed to provide guidance. TRIPOD provides recommendations for the reporting of studies developing, validating, or updating a prediction model. PROBAST enables a structured and transparent approach to assessing the risk of bias and the applicability of studies that develop, validate, or update prediction models. These documents also provide valuable references of studies to consider when designing, conducting, and analyzing prediction model studies. Authors of prediction studies should adhere to these guidelines. Our first concern is the sample size. The sample size must be large enough to develop a prediction model that is reliable when applied to new individuals in the target population. A simple rule of thumb for calculating the sample size, which has been widely applied, is to have at least 10 events per parameter (EPP) estimated. Given Zanoletti et al.'s sample size of 44 patients, of whom 20 (45.5%) had a recurrence (i.e., an event), the number of parameters to be estimated in their model should not have exceeded two according to this rule of thumb. Zanoletti et al., however, included seven, that is, “T category according to the revised Pittsburgh staging system” (three parameters to be estimated), “histological grade” (two parameters to be estimated), “dura mater involvement” (one parameter to be estimated), and “nonanterior spread from T4 lesions” (one parameter to be estimated). Recently, more formal sample size calculations have been proposed in order to ensure precise predictions and to minimize overfitting. According to these methods, the sample size needed would have been at least 384 patients. * Thus, the sample size of 44 patients as used by Zonoletti et al. is far too less to develop an adequate and precise prediction model. For this same reason we did not proceed on a prediction model to predict the DFS of patients with primary TBSCC (presented on April 20, 2018 at the 13th Congress of the European Skull base Society in Warsaw Poland). Although it comprised even 324 patients, the sample size was not high enough to develop an adequate prediction model. Second, although most prediction models are derived by using multivariable regression model, the novel TBSCC prognosis scoring system as proposed by Zanoletti et al. combines predictors and scores based on the hazard ratio (HR) of univariate Cox proportional hazards regression models. In addition, these univariable models were fitted using different sample sizes. The scoring of the predictors “Revised Pittsburgh staging system: T category” and “Histological grade (G)” are based on the HR using the total study group (n = 44), while the scoring of the predictors “Dural involvement if T4 category” and “Tumor spread if T4 category” are based on the HR using only the T4-classified tumors (n = 21). Therefore, it is not clear how to score the predictors “Dural involvement if T4 category” and “Tumor spread if T4 category” for tumors that are not classified as a T4. For these predictors, the T1-3 classified tumors should have been included. Apparently, data on these T1-3 classified tumors are available as data from each patient included in the study are presented in Table 1. Received: 2 December 2020 Accepted: 3 February 2021