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. 2020 May 4:18:100567.
doi: 10.1016/j.conctc.2020.100567. eCollection 2020 Jun.

Incomplete data analysis of non-inferiority clinical trials: Difference between binomial proportions case

Yulia Sidi  1 , Ofer Harel  1
Affiliations

Incomplete data analysis of non-inferiority clinical trials: Difference between binomial proportions case

Yulia Sidi et al. Contemp Clin Trials Commun. .

Erratum in

Abstract

Background: Incomplete data analysis continues to be a major issue for non-inferiority clinical trials. Due to the steadily increasing use of non-inferiority study design, we believe this topic deserves an immediate attention.

Methods: We evaluated the performance of various strategies, including complete case analysis and various imputations techniques for handling incomplete non-inferiority clinical trials when outcome of interest is difference between binomial proportions. Non-inferiority of a new treatment was determined using a fixed margin approach with 95-95% confidence interval method. The methods used to construct the confidence intervals were compared as well and included: Wald, Farrington-Manning and Newcombe methods.

Results: We found that worst-case and best-case scenario imputation methods should not be used for analysis of incomplete data in non-inferiority trial design, since such methods seriously inflate type-I error rates and produce biased estimates. In addition, we report conditions under which complete case analysis is an acceptable strategy for missing at random missingness mechanism. Importantly, we show how two-stage multiple imputation could be successfully applied for incomplete data that follow missing not at random patterns, and thus result in controlled type-I error rates and unbiased estimates.

Conclusion: This thorough simulation study provides a road map for the analysis of incomplete data in non-inferiority clinical trials for different types of missingness. We believe that the results reported in this paper could serve practitioners who encounter missing data problems in their non-inferiority clinical trials.

Keywords: Binary outcome; Incomplete data analysis; Multiple imputation; Non-inferiority design.

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Figures

Fig. 1
Fig. 1
Empirical type-I error CCA strategy for MAR: drop-out rates are balanced between the treatment groups.
Fig. 2
Fig. 2
Empirical type-I errors, CCA strategy for MNAR due to lack of efficacy in T.
Fig. 3
Fig. 3
Empirical type-I errors, two-stage MI strategy via MICE for MNAR due to lack of efficacy T.
Fig. 4
Fig. 4
Choice of different distribution parameters for kT. Empirical type-I error, two-stage MI strategy via MICE for scenario with pC=0.85,Δ=0.1 for MNAR due to lack of efficacy in T.
See this image and copyright information in PMC

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