Yorghos Tripodis

Given their improvements in bias reduction and efficiency, joint models (JMs) for longitudinal and time‐to‐event data offer great potential to clinical trials. However, for JM to become more widely used, there is a need for additional development of design considerations. To this end, Chen et al previously developed two closed‐form sample size formulas in the JM setting. In this current work, we expand upon this framework by utilizing the time‐dependent slopes parameterization, where the change in the longitudinal outcome influences the hazard, in addition to the current value of the longitudinal process. Our extended formula for the required number of events can be used when testing significance of the association between the longitudinal and time‐to‐event outcomes. We find that if the data indeed are generated such that not only the current value, but also the slope of the longitudinal outcome influence the hazard of the time‐to‐event process, it is advisable to use the current formula developed utilizing the time‐dependent slopes parameterization. In this setting, our proposed formula will provide a more accurate estimate of power compared to the method by Chen et al. To illustrate our proposed method, we present power calculations of a biomarker qualification study for Hutchinson‐Gilford progeria syndrome, an ultra‐rare premature aging disease.

Blood-based biomarkers such as tau phosphorylated at threonine 181 (phosphorylated-tau181) represent an accessible, cost-effective and scalable approach for the in vivo detection of Alzheimer’s disease pathophysiology. Plasma-pathological correlation studies are needed to validate plasma phosphorylated-tau181 as an accurate and reliable biomarker of Alzheimer’s disease neuropathological changes. This plasma-to-autopsy correlation study included participants from the Boston University Alzheimer’s Disease Research Center who had a plasma sample analysed for phosphorylated-tau181 between 2008 and 2018 and donated their brain for neuropathological examination. Plasma phosphorelated-tau181 was measured with single molecule array technology. Of 103 participants, 62 (60.2%) had autopsy-confirmed Alzheimer’s disease. Average time between blood draw and death was 5.6 years (standard deviation = 3.1 years). Multivariable analyses showed higher plasma phosphorylated-tau181 concentrations were associated with increased odds for having autopsy-confirmed Alzheimer’s disease [AUC = 0.82, OR = 1.07, 95% CI = 1.03–1.11, P < 0.01; phosphorylated-tau standardized (z-transformed): OR = 2.98, 95% CI = 1.50–5.93, P < 0.01]. Higher plasma phosphorylated-tau181 levels were associated with increased odds for having a higher Braak stage (OR = 1.06, 95% CI = 1.02–1.09, P < 0.01) and more severe phosphorylated-tau across six cortical and subcortical brain regions (ORs = 1.03–1.06, P < 0.05). The association between plasma phosphorylated-tau181 and Alzheimer’s disease was strongest in those who were demented at time of blood draw (OR = 1.25, 95%CI = 1.02–1.53), but an effect existed among the non-demented (OR = 1.05, 95% CI = 1.01–1.10). There was higher discrimination accuracy for Alzheimer’s disease when blood draw occurred in years closer to death; however, higher plasma phosphorylated-tau181 levels were associated with Alzheimer’s disease even when blood draw occurred >5 years from death. Ante-mortem plasma phosphorylated-tau181 concentrations were associated with Alzheimer’s disease neuropathology and accurately differentiated brain donors with and without autopsy-confirmed Alzheimer’s disease. These findings support plasma phosphorylated-tau181 as a scalable biomarker for the detection of Alzheimer’s disease.