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Multiple imputation for competing risks survival data via pseudo-observations
Communications for Statistical Applications and Methods 2018;25:385-396
Published online July 31, 2018
© 2018 Korean Statistical Society.

Seungbong Han1,a, Adin-Cristian Andreib, and Kam-Wah Tsuic

aDepartment of Applied Statistics, Gachon University, Korea, bDepartment of Preventive Medicine, Northwestern University, USA, cDepartment of Statistics, University of Wisconsin-Madison, USA
Correspondence to: 1Department of Applied Statistics, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do, 13120, Korea. E-mail: hanseungbong@gachon.ac.kr
Received March 8, 2018; Revised April 5, 2018; Accepted May 16, 2018.

Competing risks are commonly encountered in biomedical research. Regression models for competing risks data can be developed based on data routinely collected in hospitals or general practices. However, these data sets usually contain the covariate missing values. To overcome this problem, multiple imputation is often used to fit regression models under a MAR assumption. Here, we introduce a multivariate imputation in a chained equations algorithm to deal with competing risks survival data. Using pseudo-observations, we make use of the available outcome information by accommodating the competing risk structure. Lastly, we illustrate the practical advantages of our approach using simulations and two data examples from a coronary artery disease data and hepatocellular carcinoma data.

Keywords : competing risks, missing data, multiple imputation, pseudo-observations, random forest
1. Introduction

In a competing risks setting, an individual can undergo failure from any of several event types but we observe the first occurring event. A schematic for a single competing risks setting is represented in Figure 1.

In the beginning, an individual is in the initial state 0, but may then experience an event of interest (state 1) or a competing event (state 2). For regression models, several researchers proposed a cumulative incidence function modeling approach assuming independence between subjects (Fine and Gray, 1999; Klein and Andersen, 2005). Logan et al. (2011) use marginal models for clustered event times with competing risks based on pseudo-observations (POs). Moreno-Betancur and Latouche (2013) propose a regression model of the cumulative incidence function (CIF) with missing causes of failures using POs. Nicolaie et al. (2013) propose dynamic POs method constructed from the prediction probabilities at different landmark times. Kim and Kim (2016) also use POs for the regression modeling of interval censored data and Do and Kim (2017) extend their approach to interval censored data with missing causes of failures. Moreno-Betancur and Latouche (2013) propose a method of the missing cause of failure problem for right-censored data using POs. Ahn and Mendolia (2014) also used POs to compare median survivals for dependent data. POs-based regression is easy to implement using existing software after the POs have been obtained. Therefore it has potential for flexible regression modeling. Logan et al. (2011) point out several properties of the POs using influence functions by Graw et al. (2009). They show thr expected value of POs when the covariate value converges to the cumulative incidence function using an inverse probability of censoring weighting formulation for the cumulative incidence estimate.

We collect registry data sets in hospitals/general practices but these data sets usually contain missing values of the covariates. It is not unusual for more than half the data to be missing for important predictors (Ambler et al., 2005). A naive approach to deal with missing/incomplete covariates is to conduct a complete case analysis (Ambler et al., 2007). In this case, patients with missing values are excluded from the analysis. However, this approach typically results in substantial bias for the regression coefficient estimates and less reliable predictions. Multiple imputation by chained equations (MICE) is another widely used practical approach to deal with missing values (van Buuren et al., 1999). MICE, also known as a full conditional specification, has been used successfully to generate imputed values in survival data analyses (Shah et al., 2014). Shah et al. (2014) propose a MICE algorithm using a random forest (RF) method (Breiman, 2001) as the conditional model for imputation. The RF method automatically includes any interaction effect, as well as highly correlated covariates. In addition, the RF method uses bootstrap aggregation of multiple regression trees to reduce the risk of overfitting. Therefore, using an RF has an advantage over MICE methods based on classification and regression trees (CART), as proposed by Burgette and Reiter (2010). For competing risks survival data, we extend the RF-based MICE method using POs. Mogensen and Gerds (2013) also propose a random forest method for competing risks using POs. They first construct the POs and use them as an outcome variable in the random forest model. They compare the predictive performance of their proposed pseudo random forests to that of Cox regression model. Because the model interpretation is essential as well as the prediction error, we use POs as outcome variables in the Fine-Gray model and examine regression coefficient estimates under the scenario of covariate missing, which was not studied before. We compare a standard implementation using a MICE approach based on CART or an RF, and then propose a new version of MICE that uses POs for the competing risks survival data.

The remainder of this paper is organized as follows. We present a PO-based RF MICE (PORF) strategy in Section 2 in detail. Then, in Section 3, we use extensive simulation results to illustrate our method’s performance for moderate sample sizes. In Sections 4 and 5, we apply our method to coronary artery disease data and HCC data. Lastly, Section 6 concludes the paper with a discussion of the results.

2. Missing data imputation method for competing risks survival data

Let Ti and Ci be the event time and the censoring time for subject i. Suppose the data consists of n observations and the observation for subject i is (Xi, δi, εi, Zi), where Xi is the observation time as (Ti Λ Ci) and δi is the event indicator as I(TiCi). We denote εi the event type which can be observed if a certain event has occurred before Ci. We also denote Zi the p-dimensional covariate vector. We assume the censoring time Ci is independent of the event time Ti. For the n observations, Z is the n × p covariate matrix. We arrange the columns of Z so that Z = (ZC, ZP), where ZC is a completely observed covariate matrix, and ZP is a partially observed covariate matrix. To conduct the MICE approach, we introduce PO matrix Y, where Y is composed of q columns of POs. We obtain the POs at q different time points by using the jackknife method to substitute Xi, δi and εi (Klein and Andersen, 2005). Next, we describe how POs are obtained for the competing risks data. Assume that there are K distinct event types, indexed by εi ∈ {1, 2, … , K}. It is possible to experience experience failure from any of the K event types; however, we only observe the event time for the first occurring event (or the last follow up time if no failure has occurred). We assume no measurement error and non-informative censoring. For event k, the cause-specific hazard function hk(t) and overall hazard function h·(t) are defined as:




The CIF for event k at time t is obtained by


where S(t)=exp{-0th.(u)du} is the overall survival function. Finally, the PO for the ith subject at time t is defined as


where k(t) and F^k(i)(t) are the estimated CIFs based on the full sample and a sample of size n − 1, respectively. In the latter case, we delete the ith observation. To select q (≥ 2) distinct time points 0 < t1 < ··· < tq < ∞ for the PO vector νi(t) = (νi(t1), … , νi(tq)), we may use percentile values based on the estimated CIF. Andersen and Perme (2010) discuss the choice of the number of time points, q. We select nine time points based on the estimated CIF. These nine points correspond to the tq values as:


where p = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9. Originally, the POs were used for regression modeling by Klein and Andersen (2005). We extend their approach for the imputation of missing data using MICE for competing risks survival data. To implement MICE, we rearrange covariate matrix Z such that the complete observation proportion is decreasing from left to right. The MICE procedure is as follows. Let φ1, φ2, … , φp+q constitute the rearranged matrix Ψ. The first variable with the smallest missing variable, say φ1, is regressed on the other variables, φ2, … , φp+q. Missing values in φ1 are replaced by random draws from a predictive distribution of φ1. The next variable with missing values, say φ2 is regressed with all other variables, φ1, φ3, … , φp+q. Then, missing values in φ2 are replaced by random draws from a predictive distribution of φ2. This process is repeated for all other variables with missing values, and this cycle constitutes one imputed data set. It is common to use a generalized linear model for the predictive regression model. However, we use the RF algorithm for the regression estimation. The RF algorithm uses bootstrapping sampling such that records with missing values in the dependent variable are imputed by random draws from a normal distribution centered on a conditional mean of the predicted value. We generate several imputed data sets to represent the uncertainty about the missing value. Then we combine the estimates from the multiple imputed data sets using Rubin’s rule (Rubin, 1987). When there are B imputed data sets, the combined estimate θ̂ is given by


where θ̂b is an estimate of a quantity of interest, such as a regression coefficient. Suppose Vb is the estimated variance of θ̂b, and V is the average of Vb across the B imputed data sets. Then, the following combined variance accommodates both the within-imputation and the between-imputation variability:


where W=1/(B-1)b=1B(θ^b-θ^)2. There are several regression models for competing risks data (Aalen et al., 2008). The Fine-Gray method directly models the CIFs (Fine and Gray, 1999). For completeness, we describe the model here. Assume βk and Zi are a 1 × p vector of regression coefficients for event k and a p × 1 vector of covariates for subject i, respectively. Then, the Fine-Gray model is given by


where i = 1, … , n, and Λ0(t) is an unspecified non-decreasing baseline. Note that a positive βk indicates that the CIF increases with Zi. Furthermore, suppose that C is the censoring time and G(t) is the survival function for C, Pr(Ct). The following score function is proposed to estimate the regression coefficients for the event of interest k:


where Ñi(t) = I(Tit, XTi = k), i(t) = 1 − Ñi(t−), ri = I(Ci ≥ min(Ti, t)), and wi(t) = ri(t){Ĝ(t)/Ĝ (min(t, Ti,Ci))}. We can see that wj(t) j(t) approximates the sub-distribution risk set. Without the competing risks, the weighted score function reduces to the standard score function for the Cox model. In the following simulation section, the PO-based multiple imputation method is compared to the methods of Burgette and Reiter (2010) (CART-based MICE; CRT) and Shah et al. (2014) (RF-based MICE; SHAH). CRT is a nonparametric approach to implement multiple imputation via chained equations using sequential regression trees as the conditional models. This method uses the CART imputation engine that can fit interactions, nonlinear relations without data transformations. However, the SHAH method uses the random forest imputation for the multivariate imputation by chained equations.

3. Simulation studies

To evaluate the performance of the proposed method for the competing risk survival data, we compare it against the CRT method and the SHAH method. Here, the R packages tree and mice are used for the simulation comparison (Ripley, 2014; van Buuren and Groothuis-Oudshoorn, 2011). Variables were artificially made under the missing-at-random (MAR) assumption. Three different simulation scenarios are considered, according to the competing event and censoring proportions. Competing risks survival data under the proportional hazards model are generated as follows. For the event of interest in state 1 and the competing event in state 2, we assume cause-specific hazards for subject i, given by:




where the regression parameter β = (1, 1, 1, 1, 0.1, 0.1, 0.1), and γ = (1.2, 0.2, 1.2) for scenario 1, γ = (1.2, 0.3, 0.8) for scenario 2, or γ = (1, 1, 1) for scenario 3. The covariate matrix for state 1 is composed of seven dimensional vectors, Zi=(z1,z2,z3,z4,z42,z1z2,z1z4), where z1 is drawn from a normal distribution N(−4, 0.5), z2 is drawn from a binomial distribution Bin(0.5), z3 is drawn from a uniform distribution U(−4, −2), and z4 is drawn from N(−3, 0.5). However, the covariate matrix for state 2 is composed of three dimensional vectors, Wi=(w1,w2,w3), where w1 is drawn from a normal distribution N(−6, 1), w2 is drawn from a Weibull distribution Wei(1, 0.5), and w3 is drawn from a uniform distribution U(−6, −4). First, we simulate the failure time with the all-cause hazard h·(t|Zi,Wi) = h1(t|Zi) + h2(t|Wi). We then run a binomial experiment for a simulated failure time that decides on the event of interest with probability h1(t|Zi)/h·(t|Zi,Wi) (Beyersmann et al., 2012). In this way, we generate competing risks data and random right-censoring times C. We simulate 1,000 subjects from each design, and delete observations from z1 through w3 via a MAR mechanism that depends on z3, which is completely observed. This leads to around 10% missing values for every variable, except z3. On average, 60% of the covariate matrix is complete. The γ values change the competing risks proportions so that around 3%, 6%, and 10% of competing events are generated in scenarios 1, 2, and 3. To make the time points for νi(t) fixed across simulation-runs, we generate 1,000,000 subjects, and select 9 points based on percentiles of the cumulative incidence estimate. We perform multiple imputation using the randomForest package, with default settings (Liaw and Wiener, 2002). After the all the covariates are imputed, we fit the competing risks model based on the crr function from the cmprsk package in R (Gray, 2014). Using B = 20 or 40 may produce more accurate results in some situations; however, in our case, B = 10 produces good results (Graham et al., 2007).

We compared the proposed method (PORF) with the CRT algorithm (Burgette and Reiter, 2010) and the SHAH algorithm (Shah et al., 2014). In addition, we included the complete case analysis results (CC) for the comparison. CRT first sorts the columns of (Zi,Wi) to have increasing numbers of missing values. Then it impute missing values in (Zi,Wi) with predictive values from the CART model fit using the R function tree. In order to yield 10 imputed sets, CRT repeats this step 10 times. However, SHAH uses the predictive values to replace missing values from the random forest fit using the R function mice. The mice function has wide capabilities so that there are broad imputation engines such as a Bayesian linear regression, a polytomous logistic regression, a linear discriminant analysis and the random forest missing imputation. To implement the standard random forest, we set ‘rf’ for the method options. Tables 13 show the simulation results for the three scenarios, respectively. The simulation results are summarized across 1,000 Monte Carlo replicates.

Table 1 displays the averages of the estimated regression coefficient and the standard errors. For the main effect terms, the CRT and the SHAH methods significantly underestimate the true value of β. In contrast, the proposed PORF method produces regression coefficients with less bias than the CRT and SHAH methods. Note that the estimates of β3 are relatively similar compared to other coefficients in Table 1. We think this is due to its significance. When the competing risk proportion increases, the PORF performs better than others (Table 2 and Table 3). Other factors such as the competing risk proportion and the covariate significance might affect the coefficient estimation. The bias in the CRT and SHAH algorithms does not decrease when the sample size increases to 400 and 500. In addition, for the quadratic and interaction terms, the results using the proposed method are less biased than those of the CRT and SHAH methods. The reason for the heavy bias in CART and SHAH might come from the fact that these methods are originally designed for standard survival. We also note that the bias of CC is comparable to the PORF method. However, the average of standard errors has almost doubled compared to the PORF. Dropping the incomplete cases in the analysis translates directly to a loss in sample size to estimate the regression coefficients. Therefore, the complete case analysis produces a loss of efficiency.

4. The coronary artery disease data example

Coronary artery disease (CAD) is the leading cause of death in the US and Europe. It is one of the most common types of heart disease and occurs when the arteries become narrowed. The build-up of cholesterol and other materials such as plaque on inner walls is the main cause. Asan Medical Center has constructed a revascularization registry to study left main or multivessel CAD (Seung et al., 2008). They have compared the treatment effect of a percutaneous coronary intervention (PCI) with coronary-artery bypass grafting (CABG) regarding long-term outcomes. Long-term adverse outcomes were measured in terms of death, myocardial infarction (MI), and stroke or target-vessel revascularization. A combined endpoint of death, MI, stroke, and the revascularization was considered a primary endpoint in the previous study; however, it is also possible to investigate the effect of the coronary stents along with other clinical, angiographic or procedural variables for cardiac-specific death. We included 5,775 patients in the analysis and several clinical (age, comorbidity, hemodynamic status, clinical presentations, and prior history of PCI or CABG) and angiographic factors (coronary anatomy, disease extents, and procedural complexities) were considered as possible factors for cardiac-specific death. The covariate variables are age (years), gender, body mass index (BMI), hypertension (yes/no), diabetes (yes/no), current smoker (yes/no), hyperlipidemia (yes/no), PCI intervention (yes/no), prior MI (PMI; yes/no), prior PCI (PPCI; yes/no), prior congestive heart failure (PCHF; yes/no), chronic lung disease (CLD; yes/no), previous stroke (PST; yes/no), peripheral vascular disease (PVD; yes/no), renal dysfunction (RD; yes/no), electrocardiographic finding (ELF; sinus rhythm vs atrial fibrillation + others), acute coronary syndrome (ACS; yes/no), ejection fraction (EF; %), left main (LM) disease (yes/no), proximal left anterior descending artery (PLAD) disease (yes/no), right CAD (yes/no), bifurcation lesion (BL; yes/no), restenotic lesion (RL; yes/no), total occlusion (TO; ≥ 1 vs < 1), EuroSCORE, and SYNTAX Score. After the surgery, 446 patients and 294 patients died of cardiac related and non-cardiac related causes, respectively during the follow-up.

Figure 2 shows the cumulative incidence rate of the cardiac death. The cumulative incidence funtion is estimated using the R function cuminc from the cmprsk package. It seems the risk increases gradually to 10 years and starts to ascend more steeply. Among the covariate the following variables have missing values : BMI(1.2%), RF(14.6%), ACS(12.2%), EuroSCORE (13.1%), SYNTAX score(39.6%), PLAD(13.9%), BL(10.4%), and TO(8.6%). Only 3442 patients have complete data on all covariates. We apply PORF, CRT, and SHAH to the data to deal with the missing values. Then we fit both univariate and multivariate Fine-Gray regression models. To construct the POs, we use nine time points based on the CIF of the cardiac death. Supplementary Table 1 shows the results of the univariate analysis. Several factors such as age, diabetes, prior MI, prior congestive heart failure, previous stroke, peripheral vascular disease, renal dysfunction, atrial fibrillation, and bifurcation lesion seem to increase the cardiac mortality significantly. However, factors such as large BMI, the PCI and the large ejection fraction level tend to decrease the cardiac mortality risk. In the multivariate regression (Table 4), the final model based on the PORF method is similar with the final model using CRT because the missing proportion is low. Note that the SYNTAX score is not statistically significant in the analysis result based on the SHAH method.

5. The hepatocellular carcinoma data example

We apply the PORF method to a retrospective study of hepatocellular carcinoma data as the second example. The data comprise 20 covariate variables measured on 525 patients who experienced curative hepatectomies at the Asan Medical Center in Korea from 2000 to 2006. The primary study endpoint is HCC-specific death after hepatic resection surgery, but patients can also die of non-cancer-related causes. Thus, our study uses regression modeling to predict post-hepatectomy outcomes. All patients underwent magnetic resonance imaging (MRI), computed tomography (CT), chest CT, and bone scintigraphy, along with serum hepatitis markers. Therefore, we could collect several covariates with regard to histological and surgical information, as well as clinical information (Shim et al., 2012). Of the 20 covariates, 11 covariates have missing values between 5% and 38% of their values. The acquired covariate variables are: age (years), gender, body mass index (BMI; < 23 vs ≥ 23), Child-Pugh class (A/B), etiology (hepatitis B virus vs hepatitis C virus + others), indocyanine green retention rate at 15 minutes (ICG R15; < 14% vs ≥ 14%), serum aspartate aminotransferase (AST; IU/L), serum alanine aminotransferase (ALT; IU/L), liver cirrhosis (yes/no), tumor size (cm), alpha-fetoprotein (AFP; ng/mL), number of tumors, microvascular invasion (yes/no), capsular invasion (yes/no), microsatellite lesion (yes/no), Edmonson grade (I or II vs III or IV), American Joint Committee on Cancer stage (AJCC; I vs II + IIIA), resection type (major/minor), resection margin width (< 10 mm vs ≥ 10 mm), and red cell transfusion (yes/no). After the surgery, 9 patients and 142 patients died of non-cancer-related causes and the HCC-related causes, respectively, whereas 374 patients were censored before experiencing HCC-specific death. Our analysis explores possible risk factors related to HCC-specific death. Supplementary Figure 1 shows the cumulative incidence rate of HCC-specific death. After about 3 years, the cumulative incidence rate increases beyond 10%, and increases to 40% at around 10 years.

We fit both univariate and multivariate Fine-Gray regression models. With regard to the covariate variables, the continuous variables of AFP, tumor size, AST, and ALT are severely skewed. Thus, we use a log transformation for these variables to obtain more stable parameter estimates. Many variables have complete records, but 11 have missing values between 5% and 38%. The missing rates are mostly modest; however, they are scattered among the variables so that only 132 patients have complete data on all variables. Assuming a MAR mechanism, we create B = 10 complete data sets using the PORF method. We order variables according to the number of values they are missing, from smallest to largest, as discussed in Section 2. As in the simulation study, to construct the POs, we use nine time points based on the CIF of the HCC-related death. Supplementary Table 2 shows the results of the univariate analysis. The results based on the CRT and the SHAH methods are also included in the table. First, it seems that the log-transformed tumor size, male gender, and microvascular invasion status increase the HCC-specific mortality significantly. However, a large BMI (≥ 23) and the Child- Pugh class (A) tend to decrease HCC-specific mortality risk. While the PORF method produces similar results to those of the CRT and SHAH overall, the AJCC stage is only significant in the PORF method. The final model based on the PORF method is similar to the final model using CRT in the multivariate regression (Table 5). The log-transformed tumor size, low BMI (< 23), Child-Pugh class (B), AJCC stage (II and IIIA), and microvascular invasion are risk factors for HCC-specific mortality. Note that the AJCC stage is not statistically significant in the univariate regression based on the CRT method; however, it is significant in the final multivariate model. Thus, this variable could be omitted when using only the CRT method. However, the SHAH method does not show the significance of the Child-Pugh class.

6. Discussion

Missing data for covariates are inevitable when patient data are collected from various aspects. If there is more than one competing event and some of event types are rare, we simply combine them into a composite outcome. However, ignoring competing risks may lead to misleading or even erroneous results that could obstruct the understanding of survival trends. MICE is an increasingly popular method for multiple imputation (Royston and White, 2011). Here, we propose another MICE algorithm using POs for competing risks data. This method uses the RF as an imputation engine so that it can flexibly deal with interactions, nonlinear relations, and complex distributions without parametric assumptions. Using the POs method, we make better use of the available outcome information by accommodating the competing risks structure. The proposed method results in less biased estimates than those of the CRT and SHAH methods in the simulation studies. The results based on real data show that there is marginal difference among different imputation methods. Sample sizes are 5775 and 525 for the CAD study and HCC study, respectively. Overall, estimated standard errors are quite small due to the large sample size for the CAD study. Therefore, all three methods produce similar results. For the HCC study, the missing rates are 19%, 7%, and 8% for the Child-Pugh class, AJCC stage, and MIC invasion variables in the final model. These small missing rates might attenuate differences among the methods. Additional research is needed in future work to deal with missing outcome variables and to extend the proposed method into a multi-state model setting.


This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1C1B 1006717).

Supplementary Material
Fig. 1. A schematic for simple competing risks survival data.
Fig. 2. Cumulative incidence function for cardiac death.

Table 1

Scenario 1: Averages of the estimated regression coefficients and the standard errors across 1,000 simulation runs; true β = (1, 1, 1, 1, 0.1, 0.1, 0.1), competing risk coefficient γ = (1.2, 0.2, 1.2)




PORF = PO-based RF MICE; CRT = CART-based MICE; SHAH = RF-based MICE; CC = complete case analysis results. PO = pseudo-observation; RF = random forest; MICE = multiple imputation by chained equations; CART = classification and regression trees.

Table 2

Scenario 2: Averages of the estimated regression coefficients and the standard errors across 1,000 simulation runs; true β = (1, 1, 1, 1, 0.1, 0.1, 0.1), competing risk coefficient γ = (1.2, 0.3, 0.8)




PORF = PO-based RF MICE; CRT = CART-based MICE; SHAH = RF-based MICE; CC = complete case analysis results. PO = pseudo-observation; RF = random forest; MICE = multiple imputation by chained equations; CART = classification and regression trees.

Table 3

Scenario 3: Averages of the estimated regression coefficients and the standard errors across 1,000 simulation runs; true β = (1, 1, 1, 1, 0.1, 0.1, 0.1), competing risk coefficient γ = (1, 1, 1)




PORF = PO-based RF MICE; CRT = CART-based MICE; SHAH = RF-based MICE; CC = complete case analysis results. PO = pseudo-observation; RF = random forest; MICE = multiple imputation by chained equations; CART = classification and regression trees.

Table 4

Multivariate analysis results for the coronary artery disease data


Age, year0.0310.007<0.0010.0340.007<0.0010.0350.007<0.001
 AF + others0.5520.2000.0060.5660.1980.0040.5160.2010.010
LM disease
EF, %−0.0230.004<0.001−0.0250.004<0.001−0.0250.004<0.001
SYNTAX Score0.0110.0040.0100.0110.0040.007

PORF = PO-based RF MICE; CRT = CART-based MICE; SHAH = RF-based MICE; RD = renal dysfunction; ELF = electrocardiographic finding; EF = ejection fraction; LM = left main; PO = pseudo-observation; RF = random forest; MICE = multiple imputation by chained equations; CART = classification and regression trees.

Table 5

Multivariate analysis results for the HCC data


Log tumor size0.4210.1530.0060.4200.1500.0050.4710.1510.002
 ≥ 23−0.4510.1760.010−0.4510.1760.010−0.4480.1760.011
Child-Pugh class
AJCC stage
 II and IIIA0.6280.2700.0200.6370.2600.0140.6190.2730.023
MIC invasion

PORF = PO-based RF MICE; CRT = CART-based MICE; SHAH = RF-based MICE; BMI = body mass index; AJCC = American Joint Committee on Cancer; MIC = microvascular; PO = pseudo-observation; RF = random forest; MICE = multiple imputation by chained equations; CART = classification and regression trees.

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