Application of statistical decision theory to treatment choices: implications for the design and analysis of clinical trials.
ABSTRACT This paper explores the application of statistical decision theory to treatment choices in cancer which involve difficult value judgements in weighing beneficial and deleterious outcomes of treatment. Strengths and weaknesses of using decision theory are illustrated by considering the problem of selecting chemotherapy in advanced ovarian cancer. The paper includes an assessment of individual preferences in 27 volunteers and a discussion of some problems in utility assessment. An alternative approach, using threshold analysis, is presented in which the results of the decision analysis are expressed as a function of utility parameters. By knowing what sets of utilities favour each treatment, the assessment of patient preferences can then be focused on important differences of treatment options. The implications of these results for the design and analysis of clinical trials are discussed.
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ABSTRACT: Meta-analysis is an important part of assessing cost-effectiveness in that it may help determine which treatments are indeed effective and estimate the level of effectiveness of each. Meta-analysis uses the data from all the relevant trials and is a powerful tool for detecting effects too small to be picked up by individual trials. The assessment of quality of studies in a meta-analysis is critical, with priority needing to be given to high quality randomised studies. A written protocol, literature retrieval system, evaluation and selection criteria, choice of endpoints and ways to evaluate bias must all be pre-defined. Nevertheless, problems can arise when meta-analysis is used for cost-effectiveness analysis, due to variation in study medication protocols, duration of follow-up, and difficulties in interpreting patient subgroups and compliance. Despite being subject to the design flaws of both the trials analysed and the methods used in the analysis itself, meta-analysis provides a more objective and thorough means of evaluating effectiveness and hence the cost-effectiveness of treatments. Based on the meta-analysis evidence, we recommend that the current QALY league tables be split into an implementation table for clearly effective therapies, and a research priority table where the evidence of treatment effectiveness is less clear and more research is needed.PharmacoEconomics 05/1992; 1(4):282-92. · 2.86 Impact Factor
- Journal of Surgical Oncology 04/2001; 76(3):201-23. · 2.64 Impact Factor
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ABSTRACT: Ovarian cancer is the leading cause of death in women with gynaecological cancers. The most common type of ovarian cancer is epithelial ovarian cancer. Referred to as the `silent' killer, this disease is difficult to detect because of the lack of specific symptoms. The majority of women who have ovarian cancer are diagnosed in the advanced stages. While the exact cause of ovarian cancer remains elusive, it is believed that the events relating to incessant ovulatory function play a major role in the development of this disease. Long term prognosis of women with ovarian cancer remains grim. Although ovarian cancer is highly responsive to chemotherapy, most women will develop persistent or recurrent disease after primary treatment. The standard front-line treatment is paclitaxel in combination with a platinum-based agent; however, toxicities associated with paclitaxel must be weighed against the clinical benefit. The economic issues associated with the treatment of ovarian cancer involve costs of chemotherapy agents and management of supportive care. Patient preferences and quality-of-life issues are also of major importance because of the short survival benefit for most patients. Therefore, quality of life must be maximised alongside efforts to prolong survival. More research is necessary to determine what trade-offs (e.g. adverse effects of treatment) patients are willing to make for modest gains in survival.PharmacoEconomics 01/2000; 17(2):133-150. · 2.86 Impact Factor