A random walk model for evaluating clinical trials involving serial observations

John L. Hopper, Graeme P. Young

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

For clinical trials where the variable of interest is ordered and categorical (for example, disease severity, symptom scale), and where measurements are taken at intervals, it might be possible to achieve a greater discrimination between the efficacy of treatments by modelling each patient's progress as a stochastic process. The random walk is a simple, easily interpreted model that can be fitted by maximum likelihood using a maximization routine with inference based on standard likelihood theory. In general the model can allow for randomly censored data, incorporates measured prognostic factors, and inference is conditional on the (possibly non‐random) allocation of patients. Tests of fit and of model assumptions are proposed, and application to two therapeutic trials of gastroenterological disorders are presented. The model gave measures of the rate of, and variability in, improvement for patients under different treatments. A small simulation study suggested that the model is more powerful than considering the difference between initial and final scores, even when applied to data generated by a mechanism other than the random walk model assumed in the analysis. It thus provides a useful additional statistical method for evaluating clinical trials.

Original languageEnglish
Pages (from-to)581-590
Number of pages10
JournalSTATISTICS IN MEDICINE
Volume7
Issue number5
DOIs
Publication statusPublished - May 1988
Externally publishedYes

Keywords

  • Antibiotic‐associated diarrhoea
  • Clinical trials
  • Colitis
  • Gastroenterology
  • Maximum likelihood
  • Oesophagitis
  • Ordinal measures
  • Stochastic processes

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