arXiv subjects

Kit Roes

Publications and source records attributed to Kit Roes.

2 recordsLinked to original sources

arXiv2022

Use of Non-concurrent Common Control in Master Protocols in Oncology Trials: Report of an American Statistical Association Biopharmaceutical Section Open Forum Discussion

This article summarizes the discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum that took place on December 10, 2020 and was organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group, in coordination with the US FDA Oncology Center of Excellence. Diverse stakeholders including experts from international regulatory agencies, academicians, and representatives of the pharmaceutical industry engaged in a discussion on the use of non-concurrent control in Master Protocols for oncology trials. While the use of non-concurrent control with the concurrent control may increase the power of detecting the therapeutic difference between a treatment and the control, the panelists had diverse opinion on the statistical approaches for modeling non-concurrent and concurrent controls. Some were more concerned about the temporality of the non-concurrent control and bias introduced by different confounders related to time, e.g., changes in standard of care, changes in patient population, changes in recruiting strategies, changes in assessment of endpoints. Nevertheless, in some situations such as when the recruitment is extremely challenging for a rare disease, the panelists concluded that the use of a non-concurrent control can be justified.

stat.AP↗

arXiv2022

Correction of overfitting bias in regression models

Regression analysis based on many covariates is becoming increasingly common. However, when the number of covariates $p$ is of the same order as the number of observations $n$, maximum likelihood regression becomes unreliable due to overfitting. This typically leads to systematic estimation biases and increased estimator variances. It is crucial for inference and prediction to quantify these effects correctly. Several methods have been proposed in literature to overcome overfitting bias or adjust estimates. The vast majority of these focus on the regression parameters. But failure to estimate correctly also the nuisance parameters may lead to significant errors in confidence statements and outcome prediction. In this paper we present a jacknife method for deriving a compact set of non-linear equations which describe the statistical properties of the ML estimator in the regime where $p=O(n)$ and under the hypothesis of normally distributed covariates. These equations enable one to compute the overfitting bias of maximum likelihood (ML) estimators in parametric regression models as functions of $\zeta = p/n$. We then use these equations to compute shrinkage factors in order to remove the overfitting bias of maximum likelihood (ML) estimators. This new derivation offers various benefits over the replica approach in terms of increased transparency and reduced assumptions. To illustrate the theory we performed simulation studies for multiple regression models. In all cases we find excellent agreement between theory and simulations.

stat.ME↗