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Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols.

ABSTRACT: In confirmatory clinical trials, the prespecification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type I error. Consequently, both the ICH E9 guideline and the European Medicines Agency (EMA) guideline on missing data in confirmatory clinical trials require that the primary analysis model is defined unambiguously. This requirement applies to mixed models for longitudinal data handling missing data implicitly. To evaluate the compliance with the EMA guideline, we evaluated the model specifications in those clinical study protocols from development phases II and III submitted between 2015 and 2018 to the Ethics Committee at Hannover Medical School under the German Medicinal Products Act, which planned to use a mixed model for longitudinal data in the confirmatory testing strategy. Overall, 39 trials from different types of sponsors and a wide range of therapeutic areas were evaluated. While nearly all protocols specify the fixed and random effects of the analysis model (95%), only 77% give the structure of the covariance matrix used for modeling the repeated measurements. Moreover, the testing method (36%), the estimation method (28%), the computation method (3%), and the fallback strategy (18%) are given by less than half the study protocols. Subgroup analyses indicate that these findings are universal and not specific to clinical trial phases or size of company. Altogether, our results show that guideline compliance is to various degrees poor and consequently, strict type I error rate control at the intended level is not guaranteed.

SUBMITTER: Hackl S

PROVIDER: S-EPMC6899721 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols.

Häckl Sebastian S Koch Armin A Lasch Florian F

Pharmaceutical statistics 20190703 6

In confirmatory clinical trials, the prespecification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type I error. Consequently, both the ICH E9 guideline and the European Medicines Agency (EMA) guideline on missing data in confirmatory clinical trials require that the primary analysis model is defined unambiguously. This requirement applies to mixed models for longitudinal data handling missing data implicitly. To evaluate the complia ...[more]

PMID: 31267673

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Project description:BackgroundFacilitation is emerging as an important strategy in the uptake of evidence. However, it is not entirely clear from a practical perspective how facilitation occurs to help move research evidence into nursing practice. The Canadian Partnership Against Cancer, also known as the 'Partnership,' is a Pan-Canadian initiative supporting knowledge translation activity for improved care through guideline use. In this case-series study, five self-identified groups volunteered to use a systematic methodology to adapt existing clinical practice guidelines for Canadian use. With 'Partnership' support, local and external facilitators provided assistance for groups to begin the process by adapting the guidelines and planning for implementation.MethodsTo gain a more comprehensive understanding of the nature of facilitation, we conducted a mixed-methods study. Specifically, we examined the role and skills of individuals actively engaged in facilitation as well as the actual facilitation activities occurring within the 'Partnership.' The study was driven by and builds upon a focused literature review published in 2010 that examined facilitation as a role and process in achieving evidence-based practice in nursing. An audit tool outlining 46 discrete facilitation activities based on results of this review was used to examine the facilitation noted in the documents (emails, meeting minutes, field notes) of three nursing-related cases participating in the 'Partnership' case-series study. To further examine the concept, six facilitators were interviewed about their practical experiences. The case-audit data were analyzed through a simple content analysis and triangulated with participant responses from the focus group interview to understand what occurred as these cases undertook guideline adaptation.ResultsThe analysis of the three cases revealed that almost all of the 46 discrete, practical facilitation activities from the literature were evidenced. Additionally, case documents exposed five other facilitation-related activities, and a combination of external and local facilitation was apparent. Individuals who were involved in the case or group adapting the guideline(s) also performed facilitation activities, both formally and informally, in conjunction with or in addition to appointed external and local facilitators.ConclusionsFacilitation of evidence-based practice is a multifaceted process and a team effort. Communication and relationship-building are key components. The practical aspects of facilitation explicated in this study validate what has been previously noted in the literature and expand what is known about facilitation process and activity.

Project description:BackgroundA number of strategies have been proposed to handle missing binary outcome data (MOD) in systematic reviews. However, none of these have been evaluated empirically in a series of published systematic reviews.MethodsUsing published systematic reviews with network meta-analysis (NMA) from a wide range of health-related fields, we evaluated comparatively the most frequently described Bayesian modelling strategies for MOD in terms of log odds ratio (log OR), between-trial variance, inconsistency factor (i.e. difference between direct and indirect estimates for a comparison), surface under the cumulative ranking (SUCRA) and rankings. We extended the Bayesian random-effects NMA model to incorporate the informative missingness odds ratio (IMOR) parameter, and applied the node-splitting approach to investigate inconsistency locally. We considered both pattern-mixture and selection models, different structures for prior distribution of log IMOR, and different scenarios for MOD. To illustrate level of agreement between different strategies and scenarios, we used Bland-Altman plots.ResultsAddressing MOD using extreme scenarios and ignoring the uncertainty about the scenarios led to systematically different and more precise log ORs compared to modelling MOD under the missing at random (MAR) assumption. Hierarchical structure of log IMORs led to lower between-trial variance, especially in the case of substantial MOD. Assuming common-within-network or trial-specific log IMORs yielded similar posterior results for all NMA estimates, whereas intervention-specific structure systematically inflated uncertainty around log ORs and SUCRAs. Pattern-mixture model agreed with selection model, particularly under the trial-specific structure; however, selection model systematically reduced precision around log IMORs. Overall, different strategies and scenarios mostly had good agreement in the case of low MOD.ConclusionsAddressing MOD using extreme scenarios and/or ignoring the uncertainty about the scenarios may negatively affect NMA estimates. Modelling MOD via the IMOR parameter can ensure bias-adjusted estimates and offer valuable insights into missingness mechanisms. The researcher should seek an expert opinion in order to decide on the structure of log IMOR that best aligns to the condition and interventions studied and to define a proper prior distribution for log IMOR. Our findings also apply to pairwise meta-analyses.

Dataset Information

Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols.

Publications

Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols.

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