title: On developing a pragmatic strategy for clinical trials: A case study of hepatocellular carcinoma.
abstract: Randomised controlled trials (RCTs) with sufficiently high statistical power are not always feasible for patients when the administration of the treatment is burdensome. Nevertheless, useful information concerning the relative effectiveness of the Test and Standard therapies, may be gleaned from under powered trials, non-randomised comparative studies and/or clinician's beliefs: the latter possibly additionally providing some suggestion of the strength of evidence required in order to adopt the Test therapy into clinical practice. In such circumstances, a Bayesian synthesis may be useful in quantifying the evidence of treatment effectiveness. In this article, we aim to present a Bayesian approach for synthesizing the cumulative evidence of the use of adjuvant hepatic intra-arterial iodine-131-lipiodol (I131L) following curative resection in hepatocellular carcinoma (HCC) patients. We constructed a posterior distribution using the information from two small RCTs, three non-randomised comparative studies, three single arm studies and the views of investigators on the use of I131L. This distribution enables calculation of the probability that the Test therapy is more effective than the Standard by a pre-stipulated amount. If this is very high, then for example, one may conclude the Test may replace the Standard therapy. If it is not, then the Standard would be retained for clinical use. Despite a strong early indication of the effectiveness of I131L, the evolving evidence over a 10-year period became more sceptical of its value. Although highly recommended, difficulties of implementing a Bayesian approach in this context are highlighted.
authors.affiliation: Department of Biostatistics, Singapore Clinical Research Institute, Singapore; Centre of Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore; Department of International Health, School of Medicine, University of Tampere, Tampere, Finland. Electronic address: mihir.gandhi@scri.edu.sg.
authors.forename: Mihir
authors.initials: M
authors.lastname: Gandhi
authors.affiliation: Centre of Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore. Electronic address: tan_say_beng@moh.gov.sg.
authors.forename: Say Beng
authors.initials: SB
authors.lastname: Tan
authors.affiliation: Department of General Surgery, Singapore General Hospital, Singapore. Electronic address: alexander.chung.y.f@sgh.com.sg.
authors.forename: Alexander
authors.initials: A
authors.lastname: Chung Yaw Fui
authors.affiliation: Cancer Studies, University of Leicester and Medical Statistics Group, School of Health and Related Studies, University of Sheffield, UK. Electronic address: dm113@leicester.ac.uk.
authors.forename: David
authors.initials: D
authors.lastname: Machin
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001499:Bayes Theorem; D006528:Carcinoma, Hepatocellular / Q000188:drug therapy* / Q000601:surgery; D017024:Chemotherapy, Adjuvant; D018849:Controlled Clinical Trials as Topic; D018572:Disease-Free Survival; D004998:Ethiodized Oil / Q000627:therapeutic use*; D006801:Humans; D007457:Iodine Radioisotopes / Q000627:therapeutic use*; D008113:Liver Neoplasms / Q000188:drug therapy* / Q000601:surgery; D009364:Neoplasm Recurrence, Local; D012107:Research Design
chemical_list: D000970:Antineoplastic Agents; D007457:Iodine Radioisotopes; D004998:Ethiodized Oil
keywords: Bayes posterior; Elicitation; Iodine-131-lipiodol; Prior