In:
Pharmaceutical Statistics, Wiley, Vol. 21, No. 3 ( 2022-05), p. 514-524
Abstract:
The problem of associating a continuous covariate, or biomarker, against a time‐to‐event outcome, is that it often requires categorisation of the covariate. This can lead to bias, loss of information and a poor representation of any underlying relationship. Here, two methods are proposed for estimating the effects of a continuous covariate on a time‐to‐event endpoint using weighted kernel estimators. The first method aims to estimate a density function for a time‐to‐event endpoint conditional on some covariate value whilst the second uses a joint density estimator. The results are visualisations in the form of surface plots that show the effects of a covariate without any need for categorisation. Both methods can aid interpretation and analysis of covariates against a time‐to‐event endpoint.
Type of Medium:
Online Resource
ISSN:
1539-1604
,
1539-1612
Language:
English
Publisher:
Wiley
Publication Date:
2022
detail.hit.zdb_id:
2083706-9
detail.hit.zdb_id:
2163550-X
SSG:
15,3
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