The continuous predictor X is discretized into a categorical covariate X ? with low range (X < X1k), median range (X1k < X < Xdosk), and high range (X > X2k) according to each pair of candidate cut-points.
Then categorical covariate X ? (reference peak is the median range) is equipped in good Cox design while the concomitant Akaike Guidance Criterion (AIC) worth is determined. The two off slash-items that minimizes AIC opinions is described as maximum clipped-facts. Also, opting for reduce-issues of the Bayesian guidance standard (BIC) has got the exact same performance due to the fact AIC (More document step 1: Dining tables S1, S2 and you may S3).
Execution in 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.
The newest simulator analysis
A beneficial Monte Carlo simulator analysis was applied to check the brand new results of optimum equal-Hours means or other discretization procedures such as the median separated (Median), top of the and lower quartiles beliefs (Q1Q3), and also the minimum record-review try p-really worth strategy (minP). To investigate the abilities of those methods, the brand new predictive abilities away from Cox models fitted with various discretized parameters was reviewed.
Model of the latest simulator analysis
U(0, 1), ? are the shape parameter of Weibull distribution, v is actually the design factor from Weibull distribution, x was an ongoing covariate out-of an elementary regular distribution, and you will s(x) is this new provided aim of appeal. To help you replicate You-designed matchmaking anywhere between x and you will diary(?), the form of s(x) try 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 http://datingranking.net/tr/bronymate-inceleme 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.