arXiv subjects

Shuji Ogino

Publications and source records attributed to Shuji Ogino.

3 recordsLinked to original sources

arXiv2022

The Subtype-Free Average Causal Effect for Heterogeneous Disease Etiology

Studies have shown that the effect an exposure may have on a disease can vary for different subtypes of the same disease. However, existing approaches to estimate and compare these effects largely overlook causality. In this paper, we study the effect smoking may have on having colorectal cancer subtypes defined by a trait known as microsatellite instability (MSI). We use principal stratification to propose an alternative causal estimand, the Subtype-Free Average Causal Effect (SF-ACE). The SF-ACE is the causal effect of the exposure among those who would be free from other disease subtypes under any exposure level. We study non-parametric identification of the SF-ACE, and discuss different monotonicity assumptions, which are more nuanced than in the standard setting. As is often the case with principal stratum effects, the assumptions underlying the identification of the SF-ACE from the data are untestable and can be too strong. Therefore, we also develop sensitivity analysis methods that relax these assumptions. We present three different estimators, including a doubly-robust estimator, for the SF-ACE. We implement our methodology for data from two large cohorts to study the heterogeneity in the causal effect of smoking on colorectal cancer with respect to MSI subtypes.

stat.ME↗

arXiv2018

A novel calibration framework for survival analysis when a binary covariate is measured at sparse time points

The goals in clinical and cohort studies often include evaluation of the association of a time-dependent binary treatment or exposure with a survival outcome. Recently, several impactful studies targeted the association between aspirin-taking and survival following colorectal cancer diagnosis. Due to surgery, aspirin-taking value is zero at baseline and may change its value to one at some time point. Estimating this association is complicated by having only intermittent measurements on aspirin-taking. Naive, commonly-used, methods can lead to substantial bias. We present a class of calibration models for the distribution of the time of status change of the binary covariate. Estimates obtained from these models are then incorporated into the proportional hazard partial likelihood in a natural way. We develop nonparametric, semiparametric and parametric calibration models, and derive asymptotic theory for the methods that we implement in the aspirin and colorectal cancer study. Our methodology allows to include additional baseline variables in the calibration models for the status change time of the binary covariate. We further develop a risk-set calibration approach that is more useful in settings in which the association between the binary covariate and survival is strong.

stat.AP↗

arXiv2016

The competing risks Cox model with and without auxiliary case covariates under weaker or no missing-at-random cause of failure

In the analysis of time-to-event data with multiple causes using a competing risks Cox model, often the cause of failure is unknown for some of the cases. The probability of a missing cause is typically assumed to be independent of the cause given the time of the event and covariates measured before the event occurred. In practice, however, the underlying missing-at-random assumption does not necessarily hold. Motivated by colorectal cancer subtype analysis, we develop semiparametric methods to conduct valid analysis, first when additional auxiliary variables are available for cases only. We consider a weaker missing-at-random assumption, with missing pattern depending on the observed quantities, which include the auxiliary covariates. Overlooking these covariates will potentially result in biased estimates. We use an informative likelihood approach that will yield consistent estimates even when the underlying model for missing cause of failure is misspecified. We then consider a method to conduct valid statistical analysis when there are no auxiliary covariates in the not missing-at-random scenario. The superiority of our methods in finite samples is demonstrated by simulation study results. We illustrate the use of our method in an analysis of colorectal cancer data from the Nurses' Health Study cohort, where, apparently, the traditional missing-at-random assumption fails to hold for particular molecular subtypes.

stat.ME↗