Ng Ryan, Kornas Kathy, Sutradhar Rinku, Wodchis Walter P, Rosella Laura C
1Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON M5T 3M7 Canada.
2Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, ON M4N 3M5 Canada.
Diagn Progn Res. 2018 Feb 7;2:4. doi: 10.1186/s41512-018-0026-5. eCollection 2018.
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 , 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.
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.
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.
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.
The protocol was registered with Open Science Framework (https://osf.io/r3232/).
纳入生存分析的预后模型可预测在特定时间段内发生未来事件的风险（即概率）。2002年，罗伊斯顿和帕尔马在[文献名称未给出]中描述了一种灵活的参数生存模型，即罗伊斯顿 - 帕尔马模型，该模型拟合受限立方样条以在比例风险尺度上灵活地对基线对数累积风险进行建模。这一特性使得能够在所有时间点估计效应的绝对度量（例如风险率），这在使用该模型时是一个重要特性。罗伊斯顿 - 帕尔马模型还可以纳入时间依存效应，并可用于不同尺度（例如比例优势、概率单位）。这些特性使得罗伊斯顿 - 帕尔马模型在预测方面具有吸引力，但其目前在预后建模中的应用情况尚不清楚。因此，本研究的目的是对罗伊斯顿 - 帕尔马模型在健康研究的预后模型中的应用方式进行范围综述，以提高对该模型的认识，识别当前报告中的差距，并为其他研究人员提供模型构建方面的考虑因素和报告建议。
检索了2001年至2016年期间五个电子数据库以及网络来源中的灰色文献，以确定纳入范围综述的文章。两名评审员独立筛选了1429篇文章，经过两步筛选过程应用排除标准后，提取了12项研究的数据。
自2001年以来，仅确定了12项研究在某种程度上使用罗伊斯顿 - 帕尔马模型进行预后建模，其中10项研究将该模型作为其预后模型的基础。不同研究中受限立方样条的内部节点数量各不相同（范围为1至6），只有三项研究报告了节点位置。三项研究提供了关于基线函数的详细信息，两项研究使用了图表，第三项研究提供了系数。然而，没有研究提供关于其受限立方样条的足够信息，以使其他人能够验证或完全使用该模型。
尽管罗伊斯顿 - 帕尔马模型在预后模型方面具有优势，但它们在健康研究中并未得到广泛应用。需要更好地报告关于受限立方样条的详细信息，以便其他人能够使用和验证该预后模型。
该方案已在开放科学框架（https://osf.io/r3232/）注册。
