A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer

Research output: Contribution to journalArticle

Abstract

Objectives To assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC).

Design/setting A narrative systematic review of international peer reviewed journals

Data source Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist.

Primary and secondary outcome measures Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.

Results 263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).

Conclusions Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias.
LanguageEnglish
Article numbere027192
Number of pages9
JournalBMJ Open
Volume9
Issue number8
DOIs
Publication statusPublished - 21 Aug 2019

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Pancreatic Neoplasms
Checklist
Databases
Neoplasms
Adenocarcinoma
Nomograms
Peer Review
Validation Studies
PubMed
MEDLINE
Sample Size
Registries
Outcome Assessment (Health Care)

Keywords

  • prognostic model development
  • pancreatic ductal adenocarcinoma
  • PDAC
  • post resection

Cite this

@article{01da726d57d84c63a943796d2a216345,
title = "A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer",
abstract = "Objectives To assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC).Design/setting A narrative systematic review of international peer reviewed journalsData source Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist.Primary and secondary outcome measures Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.Results 263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).Conclusions Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias.",
keywords = "prognostic model development, pancreatic ductal adenocarcinoma, PDAC, post resection",
author = "Alison Bradley and {Van Der Meer}, Robert and McKay, {Colin J}",
year = "2019",
month = "8",
day = "21",
doi = "10.1136/bmjopen-2018-027192",
language = "English",
volume = "9",
journal = "BMJ Open",
issn = "2044-6055",
number = "8",

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TY - JOUR

T1 - A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer

AU - Bradley, Alison

AU - Van Der Meer, Robert

AU - McKay, Colin J

PY - 2019/8/21

Y1 - 2019/8/21

N2 - Objectives To assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC).Design/setting A narrative systematic review of international peer reviewed journalsData source Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist.Primary and secondary outcome measures Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.Results 263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).Conclusions Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias.

AB - Objectives To assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC).Design/setting A narrative systematic review of international peer reviewed journalsData source Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist.Primary and secondary outcome measures Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.Results 263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).Conclusions Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias.

KW - prognostic model development

KW - pancreatic ductal adenocarcinoma

KW - PDAC

KW - post resection

UR - https://www.ncbi.nlm.nih.gov/pubmed/31439598

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DO - 10.1136/bmjopen-2018-027192

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JO - BMJ Open

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SN - 2044-6055

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