Polygenic risk prediction models for colorectal cancer: a systematic review

Sassano, Michele; Mariani, Marco; Quaranta, Gianluigi; Pastorino, Roberta; Boccia, Stefania

doi:10.1186/s12885-021-09143-2

BMC Cancer

Table 3 Results of the risk of bias for each domain of the PROBAST tool

From: Polygenic risk prediction models for colorectal cancer: a systematic review

First author, year [ref]	Risk of bias (ROB)								Applicability						Overall
	Participants		Predictors		Outcome		Analysis		Participants		Predictors		Outcome		Risk of Bias	Applicability
	Dev	Val	Dev	Val	Dev	Val	Dev	Val	Dev	Val	Dev	Val	Dev	Val	Risk of Bias	Applicability
Abe M, 2017 [22]	High	High	High	High	Unclear	Unclear	High	High	Low	Low	Low	Low	Low	Low	High	Low
Balavarca Y, 2019 [23]	High		High		Low		High		High		Low		Low		High	High
Chandler PD, 2018 [24]	Low		High		High		High		Low		Low		Low		High	Low
Cho YA, 2019 [25]	High		High		High		High		Low		Low		Low		High	Low
de Kort S, 2019 [26]	Low		High		Low		High		Low		Low		Low		High	Low
Dunlop MG, 2013 [27]	High	High	Unclear	Unclear	Unclear	Unclear	Low	Low	Low	Low	Low	Low	Low	Low	High	Low
Hiraki LT, 2013 [28]	High		High		High		High		Low		Low		Low		High	Low
Hosono S, 2016 [29]	High	High	High	High	Unclear	Unclear	High	High	Low	Low	Low	Low	Low	Low	High	Low
Hsu L, 2015 [30]	High	Low	Low	Low	Low	Low	Unclear	Unclear	Low	Low	Low	Low	Low	Low	High	Low
Huyghe JR, 2019 [31]	Low		Low		Low		Unclear		Low		Low		Low		Unclear	Low
Ibáñez-Sanz G, 2017 [32]	High		Unclear		Low		Unclear		Low		Low		Low		High	Low
Iwasaki M, 2017 [33]	Low		Unclear		Low		Unclear		Low		Low		Low		High*	Low
Jenkins MA, 2019 [34]		High		Low		High		Unclear		Low		Low		Low	High	Low
Jeon J, 2018 [35]	High	High	Low	Low	Low	Low	Unclear	Unclear	Low	Low	Low	Low	Low	Low	High	Low
Jo J, 2012 [36]	Low		Unclear		Low		High		Low		Low		Low		High	Low
Jung KJ, 2015 [37]	Low		Unclear		Low		High		Low		Low		Low		High	Low
Jung SY, 2019 [38]	Low		High		Unclear		High		High		High		Low		High	High
Marshall KW, 2010 [39]	High	High	Unclear	Unclear	Low	Low	High	High	Unclear	Unclear	Low	Low	Low	Low	High	Unclear
Prizment AE, 2013 [40]	Low		Low		Low		High		High		Low		Low		High	High
Rodriguez-Broadbent H, 2017 [41]	High		High		High		High		High		Low		Low		High	High
Schmit SL, 2019 [42]	High	High	Unclear	Unclear	Low	Low	Unclear	Unclear	Low	Low	Low	Low	Low	Low	High	Low
Shi Z, 2019 [43]	Low		Low		Low		Unclear		Low		Low		Low		Unclear	Low
Smith T, 2018 [44]		Low		Low		Unclear		High		Low		Low		Low	High	Low
Thrift AP, 2015 [45]	High		High		High		High		High		Low		Low		High	Low
Thrift AP, 2015 [46]	High		High		High		High		High		Low		Low		High	Low
Wang HM, 2013 [47]	High		Unclear		Low		High		Unclear		Low		Low		High	Unclear
Wang K, 2018 [48]	Low		Low		Low		High		Low		Low		Low		High	Low
Weigl K, 2018 [49]	High	High	Unclear	Unclear	Low	Low	High	High	High	High	Low	Low	Low	Low	High	High
Weigl K, 2018 [50]	High		Unclear		Low		High		Low		Low		Low		High	Low
Xin J, 2018 ^a [51]		Low		Unclear		Unclear		High		Low		Low		High	High	High
Xin J, 2019 [52]	High		Unclear		Low		Unclear		Low		Low		Low		High	Low
Yeh CC, 2007 [53]	High		Unclear		Low		High		Low		Low		Low		High	Low
Zhang L, 2017 [54]	High		Unclear		Unclear		High		Low		Low		Low		High	Low

In the risk of bias assessment, “low” means low risk of bias, “high” means high risk of bias, and “unclear” means it was not possible to assess the risk of bias. In the applicability section, “high” means high concern for applicability, “low” means low concern for applicability, and “unclear” means it was not possible to assess the applicability. Risk of bias assessed with the PROBAST tool
* = a high risk of bias was assigned because of the lack of external validation, among other reasons
^a = quality assessment conducted only for the validation phase of the study, since model development involved a simulated population (among our exclusion criteria)

Back to article page

ISSN: 1471-2407

Contact us

Submission enquiries: bmccancer@biomedcentral.com
General enquiries: ORSupport@springernature.com