Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies

Kaiser I, Mathes S, Pfahlberg AB, Uter W, Berking C, Heppt M, Steeb T, Diehl K, Gefeller O (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Cancers MDPI

Book Volume: 14

Article Number: 3033

Journal Issue: 12

DOI: 10.3390/cancers14123033

Abstract

Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall ROB rating. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether, 42 studies were assessed, of which the vast majority (n = 34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our temporal analysis results showed a significant reduction in the number of studies with high ROB for the domain “analysis”. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.

Authors with CRIS profile

Isabelle Kaiser Lehrstuhl für Biometrie und Epidemiologie Wolfgang Uter Professur für Epidemiologie Carola Berking Lehrstuhl für Haut- und Geschlechtskrankheiten Markus Heppt Department of Dermatology Katharina Diehl Professur für Epidemiologie und Public Health Olaf Gefeller Lehrstuhl für Biometrie und Epidemiologie

Involved external institutions

Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Kaiser, I., Mathes, S., Pfahlberg, A.B., Uter, W., Berking, C., Heppt, M.,... Gefeller, O. (2022). Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies. Cancers, 14(12). https://dx.doi.org/10.3390/cancers14123033

MLA:

Kaiser, Isabelle, et al. "Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies." Cancers 14.12 (2022).

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