Dataset Information

The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review.

ABSTRACT:

Background

Prognostic models incorporating survival analysis predict the risk (i.e., probability) of experiencing a future event over a specific time period. In 2002, Royston and Parmar described a type of flexible parametric survival model called the Royston-Parmar model in Statistics in Medicine, a model which fits a restricted cubic spline to flexibly model the baseline log cumulative hazard on the proportional hazards scale. This feature permits absolute measures of effect (e.g., hazard rates) to be estimated at all time points, an important feature when using the model. The Royston-Parmar model can also incorporate time-dependent effects and be used on different scales (e.g., proportional odds, probit). These features make the Royston-Parmar model attractive for prediction, yet their current uptake for prognostic modeling is unknown. Thus, the objectives were to conduct a scoping review of how the Royston-Parmar model has been applied to prognostic models in health research, to raise awareness of the model, to identify gaps in current reporting, and to offer model building considerations and reporting suggestions for other researchers.

Methods

Five electronic databases and gray literature indexed in web sources from 2001 to 2016 were searched to identify articles for inclusion in the scoping review. Two reviewers independently screened 1429 articles, and after applying exclusion criteria through a two-step screening process, data from 12 studies were abstracted.

Results

Since 2001, only 12 studies were identified that used the Royston-Parmar model in some capacity for prognostic modeling, 10 of which used the model as the basis for their prognostic model. The restricted cubic spline varied across studies in the number of interior knots (range 1 to 6), and only three studies reported knot placement. Three studies provided details about the baseline function, with two studies using a figure and the third providing coefficients. However, no studies provided adequate information on their restricted cubic spline to permit others to validate or completely use the model.

Conclusions

Despite the advantages of the Royston-Parmar model for prognostic models, they are not widely used in health research. Better reporting of details about the restricted cubic spline is needed, so the prognostic model can be used and validated by others.

Registration

The protocol was registered with Open Science Framework (https://osf.io/r3232/).

SUBMITTER: Ng R

PROVIDER: S-EPMC6460777 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review.

Ng Ryan R Kornas Kathy K Sutradhar Rinku R Wodchis Walter P WP Rosella Laura C LC

Diagnostic and prognostic research 20180207

<h4>Background</h4>Prognostic models incorporating survival analysis predict the risk (i.e., probability) of experiencing a future event over a specific time period. In 2002, Royston and Parmar described a type of flexible parametric survival model called the Royston-Parmar model in <i>Statistics in Medicine</i>, a model which fits a restricted cubic spline to flexibly model the baseline log cumulative hazard on the proportional hazards scale. This feature permits absolute measures of effect (e. ...[more]

PMID: 31093554

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Dataset Information

The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review.

Background

Methods

Results

Conclusions

Registration

Publications

The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review.

Similar Datasets

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