Decision tree for modeling survival data with competing risks

A review on statistical and machine learning competing risks methods

When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility.

Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study

Multiple attempts to quantify pain objectively using single measures of physiological body responses have been performed in the past, but the variability across participants reduces the usefulness of such methods. Therefore, this study aims to evaluate whether combining multiple autonomic parameters is more appropriate to quantify the perceived pain intensity of healthy subjects (HSs) and chronic back pain patients (CBPPs) during experimental heat pain stimulation. HS and CBPP received different heat pain stimuli adjusted for individual pain tolerance via a CE-certified thermode. Different sensors measured physiological responses. Machine learning models were trained to evaluate performance in distinguishing pain levels and identify key sensors and features for the classification task. The results show that distinguishing between no and severe pain is significantly easier than discriminating lower pain levels. Electrodermal activity is the best marker for distinguishing between low and high pain levels. However, recursive feature elimination showed that an optimal subset of features for all modalities includes characteristics retrieved from several modalities. Moreover, the study's findings indicate that differences in physiological responses to pain in HS and CBPP remain small.

Optimal Tuning of Random Survival Forest Hyperparameter with an Application to Liver Disease

Background

Random Forest (RF) is a technique that optimises predictive accuracy by fitting an ensemble of trees to stabilise model estimates. The RF techniques were adapted into survival analysis to model the survival of patients with liver disease in order to identify biomarkers that are highly influential in patient prognostics.

Methods

The methodology of this study begins by applying the classical Cox proportional hazard (Cox-PH) model and three parametric survival models (exponential, Weibull and lognormal) to the published dataset. The study further applied the supervised learning methods of Tuning Random Survival Forest (TRSF) parameters and the conditional inference Forest (Cforest) to optimally predict patient survival probabilities.

Results

The efficiency of these models was compared using the Akaike information criteria (AIC) and integrated Brier score (IBS). The results revealed that the Cox-PH model (AIC = 185.7233) outperforms the three classical models. We further analysed these data to observe the functional relationships that exist between the patient survival function and the covariates using TRSF.

Conclusion

The IBS result of the TRFS demonstrated satisfactory performance over other methods. Ultimately, it was observed from the TRSF results that some of the covariates contributed positively and negatively to patient survival prognostics.

Decision tree model based prediction of the efficacy of acupuncture in methadone maintenance treatment

Background

Patients with MMT often face difficulties such as sleep disturbance, headaches, and difficulty in complete abstinence from drugs. Research has shown that acupuncture can mitigate side effects while attenuating methadone dependence. It also has a synergistic and attenuated effect on methadone maintenance treatment (MMT). Exploring the predictors of the efficacy of acupuncture intervention in MMT might help clinicians and patients promote acupuncture-assisted participation in MMT, and improve clinical treatment strategies for MMT.

Objective

To describe the effect of potential predictors on MMT after acupuncture intervention by building a decision-tree model of data from A Clinical Study of Acupuncture-assisted MMT.

Design, setting, and participants

In this randomized controlled trial, 135 patients with MMT underwent acupuncture at the Substance Dependence Department of Guangzhou Huiai Hospital in Guangzhou, Guangdong Province, China.

Intervention

A total of 135 patients were 1:1 randomly assigned to either an acupuncture plus routine care group (acupuncture plus methadone) or a routine group (methadone only) for 6 weeks, and followed up for 10 weeks. Sex, age, education level, route of previous opioid use, years of opioid use, and MMT time were recorded before the trial.

Outcome measurements and statistical analysis

All analyses were based on the intention-to-treat (ITT) population. The two decision tree models used the change of methadone dosage and the VAS score for opioid desire as response variables, respectively, and the evaluation criteria were positive effect (decreased by ≥20%) and no effect (decreased by <20%, or increased). We generated the respective feature weights for the decision tree and evaluated the model's accuracy and performance by Precision-Recall.

Results

The overall accuracy of methadone reduction and psychological craving VAS scoring decision trees were 0.63 and 0.74, respectively. The Methadone Dosage Efficacy decision tree identified years of opioid use (weight = 0.348), acupuncture (weight = 0.346), and route of previous opioid use (weight = 0.162) as key features. For the VAS Score decision tree, acupuncture (weight = 0.618), MMT time (weight = 0.235), and age (weight = 0.043) were the important features.

Conclusion

Exploratory decision tree analysis showed that acupuncture, years of opioid use, route of previous opioid use, MMT time, and age were key predictors of the MMT treatment. Thus, acupuncture-assisted MMT strategy should consider the relevant influencing factors mentioned above.

Patient summary

Understanding patient characteristics and the impact of acupuncture regimens on methadone dosage reduction in MMT patients may help physicians determine the best treatment regimen for patients. An analysis of data from our clinical trial showed that acupuncture, years of opioid use, route of previous opioid use, age, and MMT time were key predictors of progressive recovery in patients with MMT. Eligible patients may benefit most from the MMT rehabilitation that reduces consumption and psychological cravings for methadone.

Clinical trial registration

http://www.chictr.org.cn/index.aspx, identifier: ChiCTR1900026357.

Online Learning Behavior Feature Mining Method Based on Decision Tree

This research mainly discusses the design of online learning behavior feature mining method based on decision tree. Data collection is the real-time collection of online learning behavior data from distance learning websites. OWC (Office Web Component) technology is used to draw real-time charts on the page. Online learning students are selected as the research object, and the student's system log data and questionnaire data are selected. When combining the pre-pruning method and the post-pruning method to make decisions after the tree is pruned, the same source data is used to adjust, test and evaluate the decision tree model. The evaluation process to generate a complete decision tree is completed by the c4.5tree algorithm in C4.5, which can be named with a suffix of .names The type definition file is used to record the type of each attribute item or the range of possible values. In the study, the prediction accuracy rate of predicting learning effect based on "online learning behavior" reached more than 66%.

Duration of Perioperative Chemotherapy in Locally Advanced Gastric Cancer: A "Less Is More" Question When ypN0 Is Achieved

Backgrounds

Perioperative chemotherapy (PEC) and neoadjuvant chemotherapy (NAC) have become a vital part of locally advanced gastric cancer (LAGC) treatment, but the optimal duration of PEC has not been studied. The aim of this study was to demonstrate the possibility of duration reduction in PEC in the adjuvant chemotherapy (AC) phase for ypN0 patients.

Methods

We included LAGC patients who achieved ypN0 after NAC in our institution from 2005 to 2018. The risk/benefit of AC and other covariates were majorly measured by overall survival (OS) and progression-free survival (PFS). We developed a survival-tree-based model to determine the optimal PEC duration for ypN0 patients in different classes.

Results

A total of 267 R0 resection patients were included. There were 55 patients who did not receive AC. The 5-year OS was 74.34% in the non-AC group and 83.64% in the AC group with a significant difference (p = 0.012). Multivariate Cox regression revealed that both AC (AC vs. non-AC: HR, 0.49; 95%CI, 0.27–0.88; p = 0.018) and ypT stages (ypT3-4 vs. ypT0-2: HR, 2.00; 95%CI, 1.11–3.59; p = 0.021) were significant protective/risk factors on patients OS and PFS. A decision tree model for OS indicated an optimal four to six cycles of PEC, which was recommended for ypT0-2N0 patients, while a minimum of five PEC cycles was recommended for ypT3-4N0 patients.

Conclusion

AC treatment is still necessary for ypN0. The duration reduction could be applied for the ypT0-2N0 stage patients but may not be suitable for higher ypT stages and beyond. A multicenter-based study is required.

Minimum Message Length Inference of the Exponential Distribution with Type I Censoring

Data with censoring is common in many areas of science and the associated statistical models are generally estimated with the method of maximum likelihood combined with a model selection criterion such as Akaike’s information criterion. This manuscript demonstrates how the information theoretic minimum message length principle can be used to estimate statistical models in the presence of type I random and fixed censoring data. The exponential distribution with fixed and random censoring is used as an example to demonstrate the process where we observe that the minimum message length estimate of mean survival time has some advantages over the standard maximum likelihood estimate.

Predicting Long-Term Outcomes After Poor-Grade Aneurysmal Subarachnoid Hemorrhage Using Decision Tree Modeling

BACKGROUND

Despite advances in the treatment of poor-grade aneurysmal subarachnoid hemorrhage (aSAH), predicting the long-term outcome of aSAH remains challenging, although essential.

OBJECTIVE

To predict long-term outcomes after poor-grade aSAH using decision tree modeling.

METHODS

This was a retrospective analysis of a prospective multicenter observational registry of patients with poor-grade aSAH with a World Federation of Neurosurgical Societies (WFNS) grade IV or V. Outcome was assessed by the modified Rankin Scale (mRS) at 12 mo, and an unfavorable outcome was defined as an mRS of 4 or 5 or death. Long-term prognostic models were developed using multivariate logistic regression and decision tree algorithms. An additional independent testing dataset was collected for external validation. Overall accuracy, sensitivity, specificity, and area under receiver operating characteristic curves (AUC) were used to assess model performance.

RESULTS

Of the 266 patients, 139 (52.3%) had an unfavorable outcome. Older age, absence of pupillary reactivity, lower Glasgow coma score (GCS), and higher modified Fisher grade were independent predictors of unfavorable outcome. Modified Fisher grade, pupillary reactivity, GCS, and age were used in the decision tree model, which achieved an overall accuracy of 0.833, sensitivity of 0.821, specificity of 0.846, and AUC of 0.88 in the internal test. There was similar predictive performance between the logistic regression and decision tree models. Both models achieved a high overall accuracy of 0.895 in the external test.

CONCLUSION

Decision tree model is a simple tool for predicting long-term outcomes after poor-grade aSAH and may be considered for treatment decision-making.