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Recruitment and retention of participant and study partner dyads in two multinational Alzheimer's disease registration trials.

  • Author(s): Bernstein, Olivia M
  • Grill, Joshua D
  • Gillen, Daniel L
  • et al.
Abstract

Background

Early study exit is detrimental to statistical power and increases the risk for bias in Alzheimer's disease clinical trials. Previous analyses in early phase academic trials demonstrated associations between rates of trial incompletion and participants' study partner type, with participants enrolling with non-spouse study partners being at greater risk.

Methods

We conducted secondary analyses of two multinational phase III trials of semagacestat, an oral gamma secretase inhibitor, for mild-to-moderate AD dementia. Cox's proportional hazards regression model was used to estimate the relationship between study partner type and the risk of early exit from the trial after adjustment for a priori identified potential confounding factors. Additionally, we used a random forest model to identify top predictors of dropout.

Results

Among participants with spousal, adult child, and other study partners, respectively, 35%, 38%, and 36% dropped out or died prior to protocol-defined study completion, respectively. In unadjusted models, the risk of trial incompletion differed by study partner type (unadjusted p value = 0.027 for test of differences by partner type), but in models adjusting for potential confounding factors, the differences were not statistically significant (p value = 0.928). In exploratory modeling, participant age was identified as the primary characteristic to explain the relationship between study partner type and the risk of failing to complete the trial. Participant age was also the strongest predictor of trial incompletion in the random forest model.

Conclusions

After adjustment for age, no differences in the risk of incompletion were observed when comparing participants with different study partner types in these trials. Differences between our findings and the findings of previous studies may be explained by differences in trial phase, size, geographic regions, or the composition of academic and non-academic sites.

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