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 ? (site level is the average variety) is fitted in a Cox design while the concomitant Akaike Information Traditional (AIC) well worth is actually calculated. The pair of clipped-items that decrease AIC values is described as optimum reduce-activities. Moreover, choosing clipped-activities of the Bayesian recommendations expectations (BIC) provides the exact same abilities as the AIC (Even more file step one: Tables S1, S2 and you may S3).
Execution inside 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.
New simulator analysis
A beneficial Monte Carlo simulator study was used to evaluate the newest performance of the optimal equal-Hour means or other discretization tips including the average split (Median), the top minimizing quartiles opinions (Q1Q3), additionally the minimal journal-score sample p-well worth method (minP). To investigate this new results of these methods, new predictive results regarding Cox habits fitted with different discretized details are reviewed.
Type of brand new simulation studies
U(0, 1), ? is actually the size factor out of Weibull delivery, v are the shape parameter of Weibull shipments, x is actually an ongoing covariate out of an elementary normal shipments, and s(x) was the newest considering reason for interest. In order to imitate U-shaped matchmaking anywhere between x and you can journal(?), the form of s(x) are set to getting
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 https://datingranking.net/tr/antichat-inceleme/ 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.
