Skip to main content
padlock icon - secure page this page is secure

Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal

Buy Article:

$43.00 plus tax (Refund Policy)

Summary.

The problem of analysing longitudinal data that are complicated by possibly informative drop-out has received considerable attention in the statistical literature. Most researchers have concentrated on either methodology or application, but we begin this paper by arguing that more attention could be given to study objectives and to the relevant targets for inference. Next we summarize a variety of approaches that have been suggested for dealing with drop-out. A long-standing concern in this subject area is that all methods require untestable assumptions. We discuss circumstances in which we are willing to make such assumptions and we propose a new and computationally efficient modelling and analysis procedure for these situations. We assume a dynamic linear model for the expected increments of a constructed variable, under which subject-specific random effects follow a martingale process in the absence of drop-out. Informal diagnostic procedures to assess the tenability of the assumption are proposed. The paper is completed by simulations and a comparison of our method and several alternatives in the analysis of data from a trial into the treatment of schizophrenia, in which approximately 50% of recruited subjects dropped out before the final scheduled measurement time.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Additive intensity model; Counterfactuals; Joint modelling; Martingales; Missing data

Document Type: Research Article

Affiliations: 1: Lancaster University, UK, and Johns Hopkins University School of Public Health, Baltimore, USA 2: Cardiff University, UK and 3: University of Newcastle, UK

Publication date: 01 November 2007

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more