We generated best-censored endurance data with known You-formed coverage-effect matchmaking

We generated best-censored endurance data with known You-formed coverage-effect matchmaking

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 ? (source peak ‘s the average assortment) is fitted from inside the a beneficial Cox model and the concomitant Akaike Pointers Traditional (AIC) well worth is calculated. The pair off slashed-points that reduces AIC beliefs means max slashed-products. Furthermore, choosing slashed-activities by the Bayesian suggestions expectations (BIC) gets the exact same overall performance because the AIC (Extra document 1: Dining tables S1, S2 and you can S3).

Execution from inside 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) www.datingranking.net/tr/dating4disabled-inceleme. 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.

Brand new simulator study

A Monte Carlo simulator analysis was applied to test the fresh new efficiency of the maximum equal-Time approach or any other discretization procedures including the median separated (Median), the top of minimizing quartiles viewpoints (Q1Q3), and the minimal journal-review take to p-value approach (minP). To analyze the newest abilities of them actions, the newest predictive abilities from Cox activities installing with various discretized details try analyzed.

Type of the newest simulator studies

U(0, 1), ? are the dimensions parameter of Weibull shipments, v was the proper execution parameter of Weibull shipping, x was an ongoing covariate out-of a basic normal shipping, and you will s(x) is the fresh new considering intent behind attract. So you’re able to simulate You-formed relationships ranging from x and you will log(?), the type of s(x) is actually 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 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.

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