Mason J
Centre for Health Economics, University of York, England.
Pharmacoeconomics. 1997 Jun;11(6):503-14. doi: 10.2165/00019053-199711060-00001.
Authors of pharmacoeconomic analyses understandably want their findings to apply as broadly as possible. Also, decision-makers may have to interpret the results of analyses conducted in healthcare settings other than their own. The validity of transferring or generalising results from one setting to another raises important issues for health-economic evaluation. Pharmacoeconomic analyses attempt to model the costs and benefits of alternative treatments in normal clinical practice. Usually, no single clinical study directly provides all the required information, and a variety of data sources is generally included in each analysis. Different data sources present different problems in terms of their relevance to decision-makers. At one extreme, an analysis based purely on trial outcomes and resource use may be precise, but not reflect normal practice; at the other extreme, an analysis using practice data may appear relevant, but be exposed to biases and confounding. Reviews of published studies suggest that general standards have been inadequate in the past. Reapplying such analyses in different localities may simply replicate inadequate findings. The 'perfect' should not become the enemy of the merely 'good'. Models can be helpful in decision-making, provided that they accurately communicate uncertainties in modelling and data. Even so, there will be limits to the generalisability of pharmacoeconomic models, since the required analysis differs between jurisdictions, and because of variations in normal clinical practice. The transferability of research findings re-opens the issue of credibility in pharmacoeconomics. Methodological standardisation, reporting standards and researcher independence are recognised as important factors for enhancing credibility. Where possible, pharmacoeconomic analyses should reflect the findings of systematic reviews of health outcomes to avoid the risk of biased selection of the evidence. In addition, the application of findings to individual healthcare settings must be considered, since cost effectiveness may vary markedly by setting and perspective.
药物经济学分析的作者希望他们的研究结果能尽可能广泛地适用，这是可以理解的。此外，决策者可能不得不解读在其所在医疗环境之外进行的分析结果。将结果从一种环境转移或推广到另一种环境的有效性，给卫生经济评估带来了重要问题。药物经济学分析试图对正常临床实践中替代治疗的成本和效益进行建模。通常，没有单一的临床研究能直接提供所有所需信息，每项分析一般都包含多种数据来源。不同的数据来源在与决策者的相关性方面存在不同问题。在一个极端情况下，纯粹基于试验结果和资源使用的分析可能很精确，但不能反映正常实践；在另一个极端情况下，使用实践数据的分析可能看起来相关，但容易受到偏差和混杂因素的影响。对已发表研究的综述表明，过去通用标准并不充分。在不同地区重新应用此类分析可能只会重复不充分的研究结果。“完美”不应成为“良好”的敌人。模型有助于决策，前提是它们能准确传达建模和数据中的不确定性。即便如此，药物经济学模型的可推广性仍会有局限，因为不同司法管辖区所需的分析不同，且正常临床实践也存在差异。研究结果的可转移性再次引发了药物经济学中可信度的问题。方法标准化、报告标准和研究者独立性被认为是提高可信度的重要因素。药物经济学分析应尽可能反映健康结果系统评价的结果，以避免有偏向性地选择证据的风险。此外，必须考虑将研究结果应用于个体医疗环境，因为成本效益可能因环境和视角的不同而有显著差异。
