Incorporating survival data into case-control studies with incident and prevalent cases

10/14/2020
by   Soutrik Mandal, et al.
0

Typically, case-control studies to estimate odds-ratios associating risk factors with disease incidence from logistic regression only include cases with newly diagnosed disease. Recently proposed methods allow incorporating information on prevalent cases, individuals who survived from disease diagnosis to sampling, into cross-sectionally sampled case-control studies under parametric assumptions for the survival time after diagnosis. Here we propose and study methods to additionally use prospectively observed survival times from prevalent and incident cases to adjust logistic models for the time between disease diagnosis and sampling, the backward time, for prevalent cases. This adjustment yields unbiased odds-ratio estimates from case-control studies that include prevalent cases. We propose a computationally simple two-step generalized method-of-moments estimation procedure. First, we estimate the survival distribution based on a semi-parametric Cox model using an expectation-maximization algorithm that yields fully efficient estimates and accommodates left truncation for the prevalent cases and right censoring. Then, we use the estimated survival distribution in an extension of the logistic model to three groups (controls, incident and prevalent cases), to accommodate the survival bias in prevalent cases. In simulations, when the amount of censoring was modest, odds-ratios from the two-step procedure were equally efficient as those estimated by jointly optimizing the logistic and survival data likelihoods under parametric assumptions. Even with 90 were as efficient as estimates obtained using only cross-sectionally available information under parametric assumptions. This indicates that utilizing prospective survival data from the cases lessens model dependency and improves precision of association estimates for case-control studies with prevalent cases.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

03/16/2018

Inference for case-control studies with incident and prevalent cases

We propose and study a fully efficient method to estimate associations o...
05/27/2019

Marginalized Frailty-Based Illness-Death Model: Application to the UK-Biobank Survival Data

The UK Biobank is a large-scale health resource comprising genetic, envi...
09/22/2020

casebase: An Alternative Framework For Survival Analysis and Comparison of Event Rates

In epidemiological studies of time-to-event data, a quantity of interest...
12/03/2018

An improved fully nonparametric estimator of the marginal survival function based on case-control clustered data

A case-control family study is a study where individuals with a disease ...
03/26/2018

Cox Regression Model Under Dependent Truncation

Truncation is a statistical phenomenon that occurs in many time to event...
04/14/2021

Dependent censoring based on copulas

Consider a survival time T that is subject to random right censoring, an...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.