An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study, and the validity under different missing mechanisms is unknown. In this research, we propose supervised and unsupervised imputations and examine four machine learning-based imputation strategies. We conducted a simulation study under various scenarios with several parameters, such as sample size, missing rate, and different missing mechanisms. The results revealed the type-I errors according to different imputation techniques in the survival data. The simulation results show that the non-parametric “missForest” based on the unsupervised imputation is the only robust method without inflated type-I errors under all missing mechanisms. In contrast, other methods are not valid to test when the missing pattern is informative. Statistical analysis, which is improperly conducted, with missing data may lead to erroneous conclusions. This research provides a clear guideline for a valid survival analysis using the Cox proportional hazard model with machine learning-based imputations.

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Liu Jun Zi Tang (LJZT) has been used to treat functional dyspepsia and depression, suggesting its effects on gastrointestinal and neurological functions. LJZT is currently used as a complementary therapy to attenuate cisplatin-induced side effects, such as dyspepsia. However, its effect on chemotherapy-induced neuropathic pain or neurotoxicity has rarely been studied. Thus, we explored potential mechanisms underlying LJZT protection against cisplatin-induced neurotoxicity. We observed that LJZT attenuated cisplatin-induced thermal hyperalgesia in mice and apoptosis in human neuroblastoma SH-SY5Y cells. Furthermore, it also attenuated cisplatin-induced cytosolic and mitochondrial free radical formation, reversed the cisplatin-induced decrease in mitochondrial membrane potential, and increased the release of mitochondrial pro-apoptotic factors. LJZT not only activated the peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) promoter region, but also attenuated the cisplatin-induced reduction of PGC-1α expression. Silencing of the PGC-1α gene counteracted the protection of LJZT. Taken together, LJZT mediated, through anti-oxidative effect and mitochondrial function regulation, to prevent cisplatin-induced neurotoxicity.

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