Parametric assumptions equate to hidden observations: comparing the efficiency of nonparametric and parametric models for estimating time to AIDS or death in a cohort of HIV-positive women

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Creator
  • Rudolph, Jacqueline E
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Cole, Stephen R
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Edwards, Jessie K
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
Abstract
  • Background When conducting a survival analysis, researchers might consider two broad classes of models: nonparametric models and parametric models. While nonparametric models are more flexible because they make few assumptions regarding the shape of the data distribution, parametric models are more efficient. Here we sought to make concrete the difference in efficiency between these two model types using effective sample size. Methods We compared cumulative risk of AIDS or death estimated using four survival models – nonparametric, generalized gamma, Weibull, and exponential – and data from 1164 HIV patients who were alive and AIDS-free in 1995. We added pseudo-observations to the sample until the spread of the 95% confidence limits for the nonparametric model became less than that for the parametric models. Results We found the 3-parameter generalized gamma to be a good fit to the nonparametric risk curve, but the 1-parameter exponential both underestimated and overestimated the risk at different times. Using two year-risk as an example, we had to add 354, 593, and 3960 observations for the nonparametric model to be as efficient as the generalized gamma, Weibull, and exponential models, respectively. Conclusions These added observations represent the hidden observations underlying the efficiency gained through parametric model form assumptions. If the model is correctly specified, the efficiency gain may be justified, as appeared to be the case for the generalized gamma model. Otherwise, precision will be improved, but at the cost of specification bias, as was the case for the exponential model.
Date of publication
DOI
Identifier
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • The Author(s).
Journal title
  • BMC Medical Research Methodology
Journal volume
  • 18
Journal issue
  • 1
Page start
  • 142
Language
  • English
Bibliographic citation
  • BMC Medical Research Methodology. 2018 Nov 19;18(1):142
Publisher
  • BioMed Central

Relations

Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
file details 12874_2018_article_605.pdf 2019-05-06 Public Download
file details 12874_2018_605_moesm1_esm.pdf 2019-05-06 Public Download