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A new approach to the analysis of analgesic drug trials, illustrated with bromfenac data
Author(s) -
Sheiner Lewis B
Publication year - 1994
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.1994.142
Subject(s) - categorical variable , analgesic , placebo , clinical pharmacology , clinical trial , inference , pain relief , medicine , analgesic agents , anesthesia , computer science , pharmacology , alternative medicine , artificial intelligence , machine learning , pathology
A clinical trial of an analgesic agent compares pain relief scores (ordered categorical responses) over time among groups of patients, each subject to a painful procedure and given various doses of active agent (including zero, i.e., placebo) on demand. Patients may elect to remedicate with an active agent if their pain relief is insufficient, so the sample of patients at any given time is biased toward those with better relief. Standard analyses usually (1) fill in the missing data but make no correction for so doing and (2) treat the ordered categorical variable as continuous. Both of these create problems in interpretation and inference, but the former is more serious than the latter. An alternative analysis has been recently proposed that deals with these problems. This article presents that method for a nonstatistical audience and illustrates its use on some data from the analgesic bromfenac. Clinical Pharmacology and Therapeutics (1994) 56, 309–322; doi: 10.1038/clpt.1994.142