A u t o M e t a

Sensitivity analysis in clinical trials: three criteria for a valid sensitivity analysis

author: Sameer Parpia, Tim P Morris, Mark R Phillips, et al.
country:Canada
Publication time: 2022-05
  • Title(CN):    Sensitivity analysis in clinical trials: three criteria for a valid sensitivity analysis
  • Publication of journals:    Eye (Lond).
  • Year/Volume/Issue/Page:    2022/36/11/2073-2074
  • language:    English
  • theme:    统计分析
  • Study type:    Meta分析,系统评价

abstract:

Randomized clinical trials are a tool to generate high-quality evidence of efficacy and safety for new interventions. The statistical analysis plan (SAP) of a trial is generally pre-specified and documented prior to seeing outcome data, and it is encouraged that researchers follow the pre-specified analysis plan. The process of pre-specification of the primary analysis involves making assumptions about methods, models, and data that may not be supported by the final trial data. Sensitivity analysis examines the robustness of the result by conducting the analyses under a range of plausible assumptions about the methods, models, or data that differ from the assumptions used in the pre-specified primary analysis. If the results of the sensitivity analyses are consistent with the primary results, researchers can be confident that the assumptions made for the primary analysis have had little impact on the results, giving strength to the trial findings. Recent guidance documents for statistical principles have emphasized the importance of sensitivity analysis in clinical trials to ensure a robust assessment of the observed results.

abstract(CN):

Randomized clinical trials are a tool to generate high-quality evidence of efficacy and safety for new interventions. The statistical analysis plan (SAP) of a trial is generally pre-specified and documented prior to seeing outcome data, and it is encouraged that researchers follow the pre-specified analysis plan. The process of pre-specification of the primary analysis involves making assumptions about methods, models, and data that may not be supported by the final trial data. Sensitivity analysis examines the robustness of the result by conducting the analyses under a range of plausible assumptions about the methods, models, or data that differ from the assumptions used in the pre-specified primary analysis. If the results of the sensitivity analyses are consistent with the primary results, researchers can be confident that the assumptions made for the primary analysis have had little impact on the results, giving strength to the trial findings. Recent guidance documents for statistical principles have emphasized the importance of sensitivity analysis in clinical trials to ensure a robust assessment of the observed results.

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