More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study.

Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the Cancer Genome Atlas (TCGA) cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 3.1% when cumulative incidence is of 10% to 29.1% when the cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of adjuvant trials in HCC, we find that the number of patients screened for eligibility can be divided by 2.4 when broadening eligibility criteria. Last, we find that the Cox-Snell [Formula: see text] is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers. Code and results are available at https://github.com/owkin/CovadjustSim .

External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning.

BACKGROUND: An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient. METHODS: We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score matching, Inverse Probability of Treatment Weighting (IPTW), G-computation, and DDML. The replication study relies on five type 2 diabetes randomized clinical trials granted by the Yale University Open Data Access (YODA) project. From the pool of five trials, observational experiments are artificially built by replacing a control arm from one trial by an arm originating from another trial and containing similarly-treated patients. RESULTS: Among the different statistical approaches, numerical simulations show that DDML has the smallest bias followed by G-computation. In terms of mean squared error, G-computation usually minimizes mean squared error. Compared to other methods, DDML has varying Mean Squared Error performances that improves with increasing sample sizes. For hypothesis testing, all methods control type I error and DDML is the most conservative. G-computation is the best method in terms of statistical power, and DDML has comparable power at [Formula: see text] but inferior ones for smaller sample sizes. The replication procedure also indicates that G-computation minimizes mean squared error whereas DDML has intermediate performances in between G-computation and propensity score approaches. The confidence intervals of G-computation are the narrowest whereas confidence intervals obtained with DDML are the widest for small sample sizes, which confirms its conservative nature. CONCLUSIONS: For external control arm analyses, methods based on outcome prediction models can reduce estimation error and increase statistical power compared to propensity score approaches.

Can machine learning bring cardiovascular risk assessment to the next level? A methodological study using FOURIER trial data.

Aims: Through this proof of concept, we studied the potential added value of machine learning (ML) methods in building cardiovascular risk scores from structured data and the conditions under which they outperform linear statistical models. Methods and results: Relying on extensive cardiovascular clinical data from FOURIER, a randomized clinical trial to test for evolocumab efficacy, we compared linear models, neural networks, random forest, and gradient boosting machines for predicting the risk of major adverse cardiovascular events. To study the relative strengths of each method, we extended the comparison to restricted subsets of the full FOURIER dataset, limiting either the number of available patients or the number of their characteristics. When using all the 428 covariates available in the dataset, ML methods significantly (c-index 0.67, P-value 2e-5) outperformed linear models built from the same variables (c-index 0.62), as well as a reference cardiovascular risk score based on only 10 variables (c-index 0.60). We showed that gradient boosting-the best performing model in our setting-requires fewer patients and significantly outperforms linear models when using large numbers of variables. On the other hand, we illustrate how linear models suffer from being trained on too many variables, thus requiring a more careful prior selection. These ML methods proved to consistently improve risk assessment, to be interpretable despite their complexity and to help identify the minimal set of covariates necessary to achieve top performance. Conclusion: In the field of secondary cardiovascular events prevention, given the increased availability of extensive electronic health records, ML methods could open the door to more powerful tools for patient risk stratification and treatment allocation strategies.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.