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 ? (reference height is the median diversity) is equipped in an excellent Cox design additionally the concomitant Akaike Information Criterion (AIC) well worth are computed. The pair out of cut-issues that reduces AIC opinions means optimal reduce-items. More over, opting for reduce-items because of the Bayesian recommendations standards (BIC) gets the same performance as AIC (Even more document 1: Tables S1, S2 and you can S3).
Execution within the 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 < 0.05.
This new simulator studies
Good Monte Carlo simulator study was utilized to check the latest abilities of optimal equal-Hour strategy or other discretization tips such as the median split up (Median), the upper minimizing quartiles opinions (Q1Q3), plus the minimal journal-review sample p-value method (minP). To investigate new overall performance ones methods, the new predictive efficiency regarding Cox patterns suitable with different discretized details is reviewed.
Design of the newest simulator analysis
U(0, 1), ? was the size and style factor out-of Weibull shipment, v was the form factor away from Weibull shipping, x was an ongoing covariate regarding a standard typical delivery, and you may s(x) is the fresh offered reason for appeal. So you’re able to replicate U-molded relationship anywhere between x and journal(?), the type of s(x) is actually set-to be
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 Bu web sitesine gidin 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.