Risk Prediction with Imperfect Survival Outcome Information from Electronic Health Records

Abstract

Readily available proxies for time of disease onset such as time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on current status and a large number of unlabeled observations on imperfect proxies.Under a semiparametric transformation model for onset and a highly flexible measurement error models for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initial estimator solely based on the labelled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semi-supervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Partners Biobank Electronic Health Records (EHR).

Implicit Profiling Estimation for Semiparametric Models with Bundled Parameters

“…Further assume θ and λ are bundled in this objective function, i.e., θ and λ cannot be clearly separated. Typical examples of this case include the semiparametric transformation model (Chan et al, 2021) and the semiparametric GARCH-in-mean model (Christensen et al, 2012), which are discussed in details in Section 4 and 5. It is worth noting that, λ can also be a smooth function with infinite dimension.…”

“…In this section, we focus on a semiparametric transformation model (Carroll et al, 1998;Chan et al, 2021) to illustrate the application of implicit profiling method.…”

“…π(•) = exp(•)/(1 + exp(•)) denotes the logit function. To estimate the smooth function λ(•), the kernel estimation method is applied (Chan et al, 2021).…”