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Estimating and testing an index of bias attributable to composite outcomes in comparative studies

2020; Elsevier BV; Volume: 132; Linguagem: Inglês

10.1016/j.jclinepi.2020.12.003

1878-5921

Fredi Alexander Díaz-Quijano,

Advanced Causal Inference Techniques

Objectives This study aimed to develop an index to evaluate the bias attributable to composite outcomes (BACOs) in comparative clinical studies. Study Design and Setting The author defined the BACO index as the ratio of the logarithm of the association measure (e.g., relative risk) of the composite outcome to that of its most relevant component endpoint (e.g., mortality). Methods to calculate the confidence intervals and test the null hypotheses (BACO index = 1) were described and applied in systematically selected clinical trials. Two other preselected trials were included as “positive controls” for being examples of primary composite outcomes disregarded because of inconsistency with the treatment effect on mortality. Results The BACO index values different from 1 were classified according to whether the use of composite outcomes overestimated (BACO index >1), underestimated (BACO index between 0 and <1), or inverted (BACO index <0) the association between exposure and prognosis. In 3 of 23 systematically selected trials and the two positive controls, the BACO indices were significantly lower than 1 (P < 0.005). Conclusion BACO index can warn that the composite outcome association is stronger, weaker, or even opposite than that of its most critical component.