The continuous predictor X is discretized into a categorical covariate X ? with low range (X < Xstep onek), median range (X1k < X < Xdosk), and high range (X > X2k) according to each pair of candidate cut-points.
Then your categorical covariate X ? (site height is the median assortment) is equipped in a great Cox design as well as the concomitant Akaike Recommendations Requirement (AIC) value was computed. The two away from slash-issues that decreases AIC viewpoints is understood to be max cut-factors. Moreover, choosing slashed-situations of the Bayesian guidance criterion (BIC) provides the exact same efficiency because AIC (Additional file 1: Dining tables S1, S2 and S3).
Implementation in R
The optimal equal-HR method was implemented in the language R (version 3.3.3). The freely available R package ‘survival’ was used to fit Cox models with P-splines. The R package ‘pec’ was employed for computing the Integrated Brier Score (IBS). The R package ‘maxstat’ was used to implement the minimum p-value method with log-rank statistics. And an R package named ‘CutpointsOEHR’ was developed for the optimal equal-HR method. This package could be installed in R by coding devtools::install_github(“yimi-chen/CutpointsOEHR”). All tests were two-sided and considered statistically significant at P facebook dating bio < 0.05.
The simulation data
A good Monte Carlo simulator study was applied to check the fresh new results of the maximum equivalent-Hours approach and other discretization methods like the median separated (Median), the top minimizing quartiles viewpoints (Q1Q3), plus the lowest log-score attempt p-well worth means (minP). To research the show of them actions, the latest predictive performance regarding Cox patterns installing with various discretized parameters are analyzed.
Type of the new simulation analysis
U(0, 1), ? is the size factor of Weibull distribution, v are the shape parameter out-of Weibull distribution, x is actually a continuous covariate out of a simple regular distribution, and you can s(x) are the fresh offered intent behind focus. In order to simulate U-shaped relationships ranging from x and log(?), the type of s(x) are set-to feel
where parameters k1, k2 and a were used to control the symmetric and asymmetric U-shaped relationships. When -k1 was equal to k2, the relationship was almost symmetric. For each subject, censoring time C was simulated by the uniform distribution with [0, r]. The final observed survival time was T = min(T0, C), and d was a censoring indicator of whether the event happened or not in the observed time T (d = 1 if T0 ? C, else d = 0). The parameter r was used to control the censoring proportion Pc.
One hundred independent datasets were simulated with n = 500 subjects per dataset for various combinations of parameters k1, k2, a, v and Pc. Moreover, the simulation results of different sample sizes were shown in the supplementary file, Additional file 1: Figures S1 and S2. The values of (k1, k2, a) were set to be (? 2, 2, 0), (? 8/3, 8/5, ? 1/2), (? 8/5, 8/3, 1/2), (? 4, 4/3, ? 1), and (? 4/3, 4, 1), which were intuitively presented in Fig. 2. Large absolute values of a meant that the U-shaped relationship was more asymmetric than that with small absolute values of a. Peak asymmetry factor of the above (k1, k2, a) values were 1, 5/3, 3/5, 3, 1/3, respectively. The survival times were Weibull distributed with the decreasing (v = 1/2), constant (v = 1) and increasing (v = 5) hazard rates. The scale parameter of Weibull distribution was set to be 1. The censoring proportion Pc was set to be 0, 20 and 50%. For each scenario, the median method, the Q1Q3 method, the minP method and the optimal equal-HR method were performed to find the optimal cut-points.