Abstract：
Multiplicity issues are increasingly common in vaccine clinical studies. Common causes include multi-valent combinations/co-administrations requiring separate evaluation of each antigen; numerous efficacy endpoints; requests from regulatory authorities for inclusion of specific powered endpoints into registration studies; interim analyses to support early decision-making. In a Phase III study to evaluate safety and immunogenicity of the 4-component Neisseria meningitidis serogroup B vaccine (4CMenB) when co-administered with 13-valent pneumococcal conjugate vaccine (PCV13) to healthy infants, a total of 49 statistical hypotheses were identified for the primary objectives as requested by the health authority. We designed a sequential testing strategy with visualization using a graphical gatekeeping procedure.
The 49 immunogenicity objectives related to evaluation of the sufficiency of the 4CMenB immune response; and demonstration of non-inferiority of PCV13 and 4CMenB when co-administered versus administration alone. We used a graphical shortcut display for closed families assuming that the multiple testing procedure is consonant and hypotheses that are rejected by a closed testing procedure are also rejected within the graphical short-cut. The 49 hypotheses were grouped into 10 families and distributed over 4 sequential steps following the clinical and statistical logical relationships agreed with the clinical team. Test decisions within the first 8 families will be made based on p-values with alpha propagation to subsequent families according to the tree structure.
This tailored strategy allowed evaluation of all 49 statistical hypotheses individually, and more efficiently. The method avoided a rigid all-or-nothing approach whereby all endpoints fail if one or more null hypotheses cannot be rejected. Clinical input and agreement are critical for designing an efficient and fit-for-purpose strategy. Our experience could encourage more application of such strategies in increasingly complex clinical trials.
The 49 immunogenicity objectives related to evaluation of the sufficiency of the 4CMenB immune response; and demonstration of non-inferiority of PCV13 and 4CMenB when co-administered versus administration alone. We used a graphical shortcut display for closed families assuming that the multiple testing procedure is consonant and hypotheses that are rejected by a closed testing procedure are also rejected within the graphical short-cut. The 49 hypotheses were grouped into 10 families and distributed over 4 sequential steps following the clinical and statistical logical relationships agreed with the clinical team. Test decisions within the first 8 families will be made based on p-values with alpha propagation to subsequent families according to the tree structure.
This tailored strategy allowed evaluation of all 49 statistical hypotheses individually, and more efficiently. The method avoided a rigid all-or-nothing approach whereby all endpoints fail if one or more null hypotheses cannot be rejected. Clinical input and agreement are critical for designing an efficient and fit-for-purpose strategy. Our experience could encourage more application of such strategies in increasingly complex clinical trials.
摘要：
多重性问题在疫苗临床研究中越来越常见。常见原因包括多价组合/共同给药，需要对每种抗原进行单独评估；许多功效终点；监管机构要求将特定的动力终点纳入注册研究；中期分析以支持早期决策。在一项III期研究中，评估4组分脑膜炎奈瑟菌血清群B疫苗（4CMenB）与13价肺炎球菌结合疫苗（PCV13）共同给予健康婴儿时的安全性和免疫原性，按照卫生当局的要求,针对主要目标共确定了49项统计假设.我们使用图形化的看门过程设计了具有可视化功能的顺序测试策略。
49个免疫原性目标涉及4CMenB免疫应答的充分性的评估;以及当共同施用与单独施用时PCV13和4CMenB的非劣效性的证明。我们为封闭家庭使用了图形快捷方式显示，假设多个测试程序是辅音的，并且在图形快捷方式中也拒绝了封闭测试程序拒绝的假设。根据与临床团队达成的临床和统计逻辑关系，将49个假设分为10个家庭，并按4个顺序步骤分布。前8个家族中的测试决定将根据树结构基于p值进行，并将alpha传播到后续家族。
这种量身定制的策略允许单独评估所有49个统计假设，更有效率。该方法避免了如果不能拒绝一个或多个零假设，则所有端点都将失败的严格方法。临床投入和协议对于设计有效且适合目的的策略至关重要。我们的经验可以鼓励在日益复杂的临床试验中更多地应用此类策略。
49个免疫原性目标涉及4CMenB免疫应答的充分性的评估;以及当共同施用与单独施用时PCV13和4CMenB的非劣效性的证明。我们为封闭家庭使用了图形快捷方式显示，假设多个测试程序是辅音的，并且在图形快捷方式中也拒绝了封闭测试程序拒绝的假设。根据与临床团队达成的临床和统计逻辑关系，将49个假设分为10个家庭，并按4个顺序步骤分布。前8个家族中的测试决定将根据树结构基于p值进行，并将alpha传播到后续家族。
这种量身定制的策略允许单独评估所有49个统计假设，更有效率。该方法避免了如果不能拒绝一个或多个零假设，则所有端点都将失败的严格方法。临床投入和协议对于设计有效且适合目的的策略至关重要。我们的经验可以鼓励在日益复杂的临床试验中更多地应用此类策略。
