Medicine before and after David Cox

Predicting prognosis outcomes of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment using lipidomics

Background

Primary central nervous system lymphoma (PCNSL) is a rare extranodal lymphomatous malignancy which is commonly treated with high-dose methotrexate (HD-MTX)-based chemotherapy. However, the prognosis outcome of HD-MTX-based treatment cannot be accurately predicted using the current prognostic scoring systems, such as the Memorial Sloan-Kettering Cancer Center (MSKCC) score.

Methods

We studied 2 cohorts of patients with PCNSL and applied lipidomic analysis to their cerebrospinal fluid (CSF) samples. After removing the batch effects and features engineering, we applied and compared several classic machine-learning models based on lipidomic data of CSF to predict the relapse of PCNSL in patients who were treated with HD-MTX-based chemotherapy.

Results

We managed to remove the batch effects and get the optimum features of each model. Finally, we found that Cox regression had the best prediction performance (AUC = 0.711) on prognosis outcomes.

Conclusions

We developed a Cox regression model based on lipidomic data, which could effectively predict PCNSL patient prognosis before the HD-MTX-based chemotherapy treatments.

Interpreting Randomized Controlled Trials

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP

Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility. Artificial intelligence (AI)/machine learning (ML) models have become an effective solution for early fertility detection. Seven industry-standard ML models are used: support vector machine, random forest (RF), decision tree, logistic regression, naïve bayes, adaboost, and multi-layer perception to detect male fertility. Shapley additive explanations (SHAP) are vital tools that examine the feature's impact on each model's decision making. On these, we perform a comprehensive comparative study to identify good and poor classification models. While dealing with the all-above-mentioned models, the RF model achieves an optimal accuracy and area under curve (AUC) of 90.47% and 99.98%, respectively, by considering five-fold cross-validation (CV) with the balanced dataset. Furthermore, we provide the SHAP explanations of existing models that attain good and poor performance. The findings of this study show that decision making (based on ML models) with SHAP provides thorough explanations for detecting male fertility, as well as a reference for clinicians for further treatment planning.

A Causal Framework for Making Individualized Treatment Decisions in Oncology

Simple Summary

Physicians routinely make individualized treatment decisions by accounting for the joint effects of patient prognostic covariates and treatments on clinical outcomes. Ideally, this is performed using historical randomized clinical trial (RCT) data. Randomization ensures that unbiased estimates of causal treatment effect parameters can be obtained from the historical RCT data and used to predict each new patient’s outcome based on the joint effect of their baseline covariates and each treatment being considered. However, this process becomes problematic if a patient seen in the clinic is very different from the patients who were enrolled in the RCT. That is, if a new patient does not satisfy the entry criteria of the RCT, then the patient does not belong to the population represented by the patients who were studied in the RCT. In such settings, it still may be possible to utilize the RCT data to help choose a new patient’s treatment. This may be achieved by combining the RCT data with data from other clinical trials, or possibly preclinical experiments, and using the combined dataset to predict the patient’s expected outcome for each treatment being considered. In such settings, combining data from multiple sources in a way that is statistically reliable is not entirely straightforward, and correctly identifying and estimating the effects of treatments and patient covariates on clinical outcomes can be complex. Causal diagrams provide a rational basis to guide this process. The first step is to construct a causal diagram that reflects the plausible relationships between treatment variables, patient covariates, and clinical outcomes. If the diagram is correct, it can be used to determine what additional data may be needed, how to combine data from multiple sources, how to formulate a statistical model for clinical outcomes as a function of treatment and covariates, and how to compute an unbiased treatment effect estimate for each new patient. We use adjuvant therapy of renal cell carcinoma to illustrate how causal diagrams may be used to guide these steps.

Abstract

We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

Smartphone-Based and Data-Driven Superstructure State Prediction Method for Highway Bridges in Service

Survival analysis is a data-driven approach that is widely used in various fields of biomedical prognostic research, and it is highly reliable in the processing of time-event data. This study developed a method for evaluating the service performance of bridge superstructures using the built-in acceleration sensor of smartphones and the prediction of survival analysis theory. It will be used to assist in the daily maintenance and repair of small and medium bridges. The effects of the upper load-bearing structure, upper general structure, bearings, deck paving, expansion joints, and frequency ratio on the deterioration of the bridge superstructure were investigated. The results show that the first-order vibration frequency of the bridge can be effectively detected by the built-in acceleration sensor of the mobile phone, but its low sensitivity and high output noise make it impossible to accurately detect the higher-order frequencies of the bridge. The upper load-bearing members, the upper general structure, the bearing, the deck pavement, and the frequency ratio are all related to the changing trend of the technical condition level of the bridge superstructure.