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. 2025 Mar 1;25(1):58.
doi: 10.1186/s12874-025-02516-2.

The reporting quality and methodological quality of dynamic prediction models for cancer prognosis

Peijing Yan #  1   2 , Zhengxing Xu #  3 , Xu Hui  4   5 , Xiajing Chu  6 , Yizhuo Chen  7 , Chao Yang  1 , Shixi Xu  8 , Huijie Cui  1 , Li Zhang  1 , Wenqiang Zhang  1 , Liqun Wang  9 , Yanqiu Zou  1 , Yan Ren  10 , Jiaqiang Liao  1 , Qin Zhang  11 , Kehu Yang  4   5   12 , Ling Zhang  13 , Yunjie Liu  1 , Jiayuan Li  1 , Chunxia Yang  1 , Yuqin Yao  9 , Zhenmi Liu  14   15 , Xia Jiang  16   17   18 , Ben Zhang  19   20   21
Affiliations

The reporting quality and methodological quality of dynamic prediction models for cancer prognosis

Peijing Yan et al. BMC Med Res Methodol. .

Abstract

Background: To evaluate the reporting quality and methodological quality of dynamic prediction model (DPM) studies on cancer prognosis.

Methods: Extensive search for DPM studies on cancer prognosis was conducted in MEDLINE, EMBASE, and the Cochrane Library databases. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and the Prediction model Risk of Bias Assessment Tool (PROBAST) were used to assess reporting quality and methodological quality, respectively.

Results: A total of 34 DPM studies were identified since the first publication in 2005, the main modeling methods for DPMs included the landmark model and the joint model. Regarding the reporting quality, the median overall TRIPOD adherence score was 75%. The TRIPOD items were poorly reported, especially the title (23.53%), model specification, including presentation (55.88%) and interpretation (50%) of the DPM usage, and implications for clinical use and future research (29.41%). Concerning methodological quality, most studies were of low quality (n = 30) or unclear (n = 3), mainly due to statistical analysis issues.

Conclusions: The Landmark model and joint model show potential in DPM. The suboptimal reporting and methodological qualities of current DPM studies should be improved to facilitate clinical application.

Keywords: Cancer prognosis; Dynamic prediction models; Methodological characteristics; Methodological quality; Reporting quality.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of study selection
Fig. 2
Fig. 2
Methodological characteristics of dynamic prediction models for cancer prognosis. AUC, area under the curve
Fig. 3
Fig. 3
The overall TRIPOD adherence score of individual studies for (A) different study types and (B) different risks of bias. D-IV, development without internal validation; D, development with internal validation; D + EV, development with external validation. FDR, false discovery rate
Fig. 4
Fig. 4
Overall adherence per TRIPOD item. *items not applicable for a development study; †items might not be applicable for a specific study
Fig. 5
Fig. 5
Risk of bias assessment of the included studies. (A) The proportion of the risk of bias across all included studies for four domains and overall, (B) the risk of bias for each included study, and (C) the proportion of the risk of bias types across all included studies for each signaling question. MLCWG, Multidisciplinary Larynx Cancer Working Group; Y, yes; PY, probably yes; NI, no information; PN, probably no; N, no
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