MedicGo
Robust estimation of the effect of an exposure on the change in a continuous outcome.
Metadata
Journalbmc medical research methodology3.031Date
2020 Jun 06
5 months ago
Type
Journal Article
Volume
2020-Jun-06 / 20 : 145
Author
Ning Y 1, 2, Støer NC 3, Ho PJ 4, 5, Kao SL 6, 7, Ngiam KY 2, 4, 8, 9, Khoo EYH 6, 7, Lee SC 10, 11, Tai ES 6, 7, Hartman M 1, 2, 4, Reilly M 12, Tan CS 13
Affiliation
  • 2. Yong Loo Lin School of Medicine, Department of Surgery, National University of Singapore and National University Health System, 10 Medical Dr, Singapore, 117597, Singapore.
  • 3. Norwegian National Advisory Unit on Women's Health, Oslo University Hospital, PO box 4950, Nydalen, 0424, Oslo, Norway.
  • 4. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Tahir Foundation Building, Singapore, 117549, Singapore.
  • 5. Genome Institute of Singapore, 60 Biopolis St, Singapore, 138672, Singapore.
  • 6. Yong Loo Lin School of Medicine, Department of Medicine, National University of Singapore and National University Health System, 10 Medical Dr, Singapore, 117597, Singapore.
  • 7. University Medicine Cluster, Division of Endocrinology, National University Health System, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
  • 8. University Surgical Cluster, Division of General Surgery (Thyroid and Endocrine Surgery), National University Health System, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
  • 9. National University Health System Corporate Office, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
  • 10. Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore.
  • 11. Department of Haematology-Oncology, National University Health System, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
  • 12. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, SE-171 77, Stockholm, Sweden.
  • 13. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, Tahir Foundation Building, Singapore, 117549, Singapore. [email protected]
Doi
PMIDMESH
Abstract
BACKGROUND: The change in two measurements of a continuous outcome can be modelled directly with a linear regression model, or indirectly with a random effects model (REM) of the individual measurements. These methods are susceptible to model misspecifications, which are commonly addressed by applying monotonic transformations (e.g., Box-Cox transformation) to the outcomes. However, transforming the outcomes complicates the data analysis, especially when variable selection is involved. We propose a robust alternative through a novel application of the conditional probit (cprobit) model.
METHODS: The cprobit model analyzes the ordered outcomes within each subject, making the estimate invariant to monotonic transformation on the outcome. By scaling the estimate from the cprobit model, we obtain the exposure effect on the change in the observed or Box-Cox transformed outcome, pending the adequacy of the normality assumption on the raw or transformed scale.
RESULTS: Using simulated data, we demonstrated a similar good performance of the cprobit model and REM with and without transformation, except for some bias from both methods when the Box-Cox transformation was applied to scenarios with small sample size and strong effects. Only the cprobit model was robust to skewed subject-specific intercept terms when a Box-Cox transformation was used. Using two real datasets from the breast cancer and inpatient glycemic variability studies which utilize electronic medical records, we illustrated the application of our proposed robust approach as a seamless three-step workflow that facilitates the use of Box-Cox transformation to address non-normality with a common underlying model.
CONCLUSIONS: The cprobit model provides a seamless and robust inference on the change in continuous outcomes, and its three-step workflow is implemented in an R package for easy accessibility.
Keywords: Box-Cox transformation Conditional probit model Normal errors Random effects model
Fav
Like
Download
Share
Export
Cite
3.0
BMC Med Res Methodolbmc medical research methodology
Metadata
LocationEngland
FromBMC

No Data

© 2017 - 2020 Medicgo
Powered by some medical students