Correction to: 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

  • Jacqueline E. RudolphEmail author
  • Stephen R. Cole
  • Jessie K. Edwards
Open Access

Correction to: BMC Medical Research Methodology (2018) 18:142

In the original publication of this article [1], the numbers of the root mean square errors in the second paragraph of the result section are wrong. Other results and interpretation remain unchanged. The numbers are revised in the paragraph below:

These results were particularly evident at two years (Fig. 1b). The two-year risk of AIDS or death in the 1164 WIHS participants estimated using the nonparametric model was 0.22 (95% CL difference: 0.048; RMSE: 0.012). For the generalized gamma model, the risk was 0.21 (95% CL difference: 0.042; RMSE: 0.014), and for the exponential model, the risk was 0.15 (95% CL difference: 0.023; RMSE: 0.067). For the Weibull model, the risk was 0.20 (95% CL difference: 0.039; RMSE: 0.021). Thus, the generalized gamma approximated the nonparametric risk well but was more precise; the exponential model was highly precise but biased. The Weibull model sat between these two extremes in terms of bias and precision.


  1. 1.
    Rudolph, et al. BMC Med Res Methodol. 2018;18(142).

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Jacqueline E. Rudolph
    • 1
    Email author
  • Stephen R. Cole
    • 1
  • Jessie K. Edwards
    • 1
  1. 1.Department of EpidemiologyUniversity of North Carolina at Chapel HillChapel HillUSA

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