Comparison of Population-Based Observational Studies With Randomized Trials in Oncology
Comparison of Population-Based Observational Studies With Randomized Trials in Oncology
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DOI：
10.1200/jco.18.01074
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发表时间：
2019-05-10
影响因子：
45.3
通讯作者：
Spratt, Daniel E.
中科院分区：
文献类型：
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作者：
Soni, Payal D.;Hartman, Holly E.;Spratt, Daniel E.
使用人群登记进行的比较疗效研究可能会受到显著偏倚的影响。有一个客观的数据证明因素，可以充分减少偏差，并提供准确的results.METHODS检索MEDLINE从2000年1月至2016年10月的观察性研究比较两种治疗方案的任何癌症的诊断，使用SEER，SEER-医疗保险，或国家癌症数据库。使用STROBE标准的组成部分评估报告质量和统计方法。确定了比较相同治疗方案的随机试验。主要结局是观察性研究和随机试验提供的生存风险比（HR）估计值之间的相关性。次要结果包括匹配对之间的协议和predictors of agreement.Results的3，657研究回顾，350治疗比较符合资格标准，并匹配到121随机试验。观察性研究和随机试验报告的HR估计值之间无显著相关性（一致性相关系数，0.083; 95% CI，-0.068至0.230）。40%的匹配研究在治疗效果方面一致（kappa，0.037; 95% CI，-0.027至0.1），62%的观察性研究HR在随机试验的95% CI内。癌症类型、数据来源、报告质量、年龄、分期或合并症的调整、倾向加权的使用、工具变量或敏感性分析以及匹配良好的研究人群都不能预测一致性。结论我们无法确定存在任何可改变的因素在基于人群的观察性研究中提高了与随机试验的一致性。无论观察性研究的报告质量或统计学严谨性如何，都没有超出预期的偶然一致性。未来的工作是需要确定可靠的方法进行人口为基础的比较疗效研究。(C)2019年美国临床肿瘤学会
PURPOSE Comparative efficacy research performed using population registries can be subject to significant bias. There is an absence of objective data demonstrating factors that can sufficiently reduce bias and provide accurate results.METHODS MEDLINE was searched from January 2000 to October 2016 for observational studies comparing two treatment regimens for any diagnosis of cancer, using SEER, SEER-Medicare, or the National Cancer Database. Reporting quality and statistical methods were assessed using components of the STROBE criteria. Randomized trials comparing the same treatment regimens were identified. Primary outcome was correlation between survival hazard ratio (HR) estimates provided by the observational studies and randomized trials. Secondary outcomes included agreement between matched pairs and predictors of agreement.RESULTS Of 3,657 studies reviewed, 350 treatment comparisons met eligibility criteria and were matched to 121 randomized trials. There was no significant correlation between the HR estimates reported by observational studies and randomized trials (concordance correlation coefficient, 0.083; 95% CI, -0.068 to 0.230). Forty percent of matched studies were in agreement regarding treatment effects (kappa, 0.037; 95% CI, -0.027 to 0.1), and 62% of the observational study HRs fell within the 95% CIs of the randomized trials. Cancer type, data source, reporting quality, adjustment for age, stage, or comorbidities, use of propensity weighting, instrumental variable or sensitivity analysis, and well-matched study population did not predict agreement.CONCLUSION We were unable to identify any modifiable factor present in population-based observational studies that improved agreement with randomized trials. There was no agreement beyond what is expected by chance, regardless of reporting quality or statistical rigor of the observational study. Future work is needed to identify reliable methods for conducting population-based comparative efficacy research. (C) 2019 by American Society of Clinical Oncology