Authors

• Rhonda J. Rosychuk
• ‎Mary E. Thompson

Abstract

Medical researchers may be interested in disease processes‎  ‎that are not‎
‎directly observable‎. ‎Imperfect diagnostic‎ ‎tests may be used repeatedly to
monitor the‎ ‎condition of a patient in the absence of a gold standard.‎ ‎We
consider parameter identifiability and estimability‎ ‎in a Markov model for
alternating binary longitudinal ‎responses that may be misclassified.‎ ‎Exactly
two distinct sets of parameter values are shown to‎ ‎generate the distribution
for the data in a common situation and we propose ‎a restriction to
distinguishes the two‎. ‎Even with the restriction‎, ‎parameters‎ ‎may not be
estimable‎. ‎Issues of sampling and correct model specification ‎are discussed.‎

Keywords

• Estimability‎
• ‎hidden Markov models‎
• ‎identifiability‎
• ‎longitudinal data‎
• ‎misclassification