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Impact of potential modifications to Alzheimer's disease clinical trials in response to disruption by COVID-19: a simulation study.
Schneider, Lon S; Qiu, Yuqi; Thomas, Ronald G; Evans, Carol; Jacobs, Diane M; Jin, Shelia; Kaye, Jeffrey A; LaCroix, Andrea Z; Messer, Karen; Salmon, David P; Sano, Mary; Schafer, Kimberly; Feldman, Howard H.
Afiliação
  • Schneider LS; Keck School of Medicine of the University of Southern California, Los Angeles, USA. lschneid@usc.edu.
  • Qiu Y; University of California, San Diego, USA.
  • Thomas RG; University of California, San Diego, USA.
  • Evans C; University of California, San Diego, USA.
  • Jacobs DM; University of California, San Diego, USA.
  • Jin S; University of California, San Diego, USA.
  • Kaye JA; Oregon Health Sciences University, Portland, USA.
  • LaCroix AZ; University of California, San Diego, USA.
  • Messer K; University of California, San Diego, USA.
  • Salmon DP; University of California, San Diego, USA.
  • Sano M; Icahn School of Medicine at Mount Sinai, New York, USA.
  • Schafer K; University of California, San Diego, USA.
  • Feldman HH; University of California, San Diego, USA.
Alzheimers Res Ther ; 13(1): 201, 2021 12 20.
Article em En | MEDLINE | ID: mdl-34930444
ABSTRACT

BACKGROUND:

The COVID-19 pandemic disrupted Alzheimer disease randomized clinical trials (RCTs), forcing investigators to make changes in the conduct of such trials while endeavoring to maintain their validity. Changing ongoing RCTs carries risks for biases and threats to validity. To understand the impact of exigent modifications due to COVID-19, we examined several scenarios in symptomatic and disease modification trials that could be made.

METHODS:

We identified both symptomatic and disease modification Alzheimer disease RCTs as exemplars of those that would be affected by the pandemic and considered the types of changes that sponsors could make to each. We modeled three scenarios for each of the types of trials using existing datasets, adjusting enrollment, follow-ups, and dropouts to examine the potential effects COVID-19-related changes. Simulations were performed that accounted for completion and dropout patterns using linear mixed effects models, modeling time as continuous and categorical. The statistical power of the scenarios was determined.

RESULTS:

Truncating both symptomatic and disease modification trials led to underpowered trials. By contrast, adapting the trials by extending the treatment period, temporarily stopping treatment, delaying outcomes assessments, and performing remote assessment allowed for increased statistical power nearly to the level originally planned.

DISCUSSION:

These analyses support the idea that disrupted trials under common scenarios are better continued and extended even in the face of dropouts, treatment disruptions, missing outcomes, and other exigencies and that adaptations can be made that maintain the trials' validity. We suggest some adaptive methods to do this noting that some changes become under-powered to detect the original effect sizes and expected outcomes. These analyses provide insight to better plan trials that are resilient to unexpected changes to the medical, social, and political milieu.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article