Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Isabelle Kaiser
,
Sonja Mathes
,
Annette B. Pfahlberg
ORCID logo ,
Wolfgang Uter
ORCID logo ,
Carola Berking
,
Markus V. Heppt
ORCID logo ,
Theresa Steeb
ORCID logo ,
Katharina Diehl
,
Olaf Gefeller
* ORCID logo
Version 1 : Received: 6 May 2022 / Approved: 7 May 2022 / Online: 7 May 2022 (03:50:41 CEST)

A peer-reviewed article of this Preprint also exists.

Kaiser, I.; Mathes, S.; Pfahlberg, A.B.; Uter, W.; Berking, C.; Heppt, M.V.; Steeb, T.; Diehl, K.; Gefeller, O. Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies. Cancers 2022, 14, 3033. Kaiser, I.; Mathes, S.; Pfahlberg, A.B.; Uter, W.; Berking, C.; Heppt, M.V.; Steeb, T.; Diehl, K.; Gefeller, O. Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies. Cancers 2022, 14, 3033.

Abstract

Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict the 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 rating of ROB. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether 42 studies were assessed, of which a 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 results of the temporal analysis 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.

Keywords

risk prediction; prediction models; risk of bias; PROBAST; melanoma

Subject

Medicine and Pharmacology, Oncology and Oncogenics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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