Zhang and Kattan ( 13) describe how to create nomograms for a logistic regression model for categorical outcome and for a Cox regression model for survival outcome using R. Several methods have been suggested to address the issue and a nomogram is one of the most widely used methods to visualize the model equations that present the behaviors of covariates in scale. However, in practice, it is often very hard to interpret the relationship between covariates and outcomes of interest and the effect of covariates intuitively. The clinical prediction model describes the relationship between covariates and outcomes of interest in forms of mathematical equations. For example, an additive hazards model ( 7- 9) or accelerated failure time model ( 10- 12) can be used as alternatives to the proportional hazards model. When the proportional hazards assumption does not hold for some covariates, other regression models may be used to estimate the effects of covariates on the hazard function for an event of interest. The most commonly used approaches for checking the assumption are to use a test based on the Schoenfeld residuals ( 3), and draw the Schoenfeld residuals plot ( 4) and the log Ĭurves for each of the levels of a covariate are parallel, it indicates that the proportional hazards assumption holds for the covariate. Thus, it is necessary to assess the proportionality assumption since a violation of the assumption may affect statistical inference. In practice, the proportional hazards assumption may not be plausible for some applications. However, the limitation of the article is that the authors did not describe about checking the proportional hazards assumption. The article also introduces R code to compute the concordance index (i.e., C-index) and draw a calibration plot to evaluate the discriminatory and calibration accuracy of a Cox regression model. The article introduces the Cox proportional hazards model ( 2) as a clinical prediction model for time to event data, which is the most widely used statistical model in studies of time to event data to estimate the effects of covariates on the hazard for an event of interest, and describes visualizing the survival probability for a patient with specific values of covariates using nomogram with R. ( 1) provides a nice overview on building a clinical prediction model on time to event data and assessing the prediction accuracy of a clinical prediction model, describing the model construction and model validation using R functions ( 1). The main goal for the analysis of time to event data is to investigate the effects of covariates on the hazard for an event of interest and estimate the survival probability. Time to event data are common in medical research, where outcomes are the time until the occurrence of an event of interest that are subject to censoring. In-depth mining of clinical data: the construction of clinical prediction model with R. Email: This is an article invited by the Editorial Office of Annals of Translational Medicine.Ĭomment on: Zhou ZR, Wang WW, Li Y, et al. Department of Statistics and Data Science Convergence Research Center, Hallym University, Chuncheon, Gangwon 24252, South Korea. Policy of Dealing with Allegations of Research MisconductĬorrespondence to: Junhee Han, PhD.Policy of Screening for Plagiarism Process.
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