Article

Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: Baseline adjustment

Dementia Research Centre, Institute of Neurology, UCL, London WC1N 3BG, UK.
Neurobiology of aging (Impact Factor: 4.85). 08/2010; 31(8):1452-62, 1462.e1-2. DOI: 10.1016/j.neurobiolaging.2010.04.011
Source: PubMed

ABSTRACT Cerebral atrophy rate is increasingly used as an outcome measure for Alzheimer's disease (AD) trials. We used the Alzheimer's disease Neuroimaging initiative (ADNI) dataset to assess if adjusting for baseline characteristics can reduce sample sizes. Controls (n = 199), patients with mild cognitive impairment (MCI) (n = 334) and AD (n = 144) had two MRI scans, 1-year apart; approximately 55% had baseline CSF tau, p-tau, and Abeta1-42. Whole brain (KN-BSI) and hippocampal (HMAPS-HBSI) atrophy rate, and ventricular expansion (VBSI) were calculated for each group; numbers required to power a placebo-controlled trial were estimated. Sample sizes per arm (80% power, 25% absolute rate reduction) for AD were (95% CI): brain atrophy = 81 (64,109), hippocampal atrophy = 88 (68,119), ventricular expansion = 118 (92,157); and for MCI: brain atrophy = 149 (122,188), hippocampal atrophy = 201 (160,262), ventricular expansion = 234 (191,295). To detect a 25% reduction relative to normal aging required increased sample sizes approximately 3-fold (AD), and approximately 5-fold (MCI). Disease severity and Abeta1-42 contributed significantly to atrophy rate variability. Adjusting for 11 predefined covariates reduced sample sizes by up to 30%. Treatment trials in AD should consider the effects of normal aging; adjusting for baseline characteristics can significantly reduce required sample sizes.

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    • "Beyond the standardization of methods and data sets, MRI studies carried out with the ADNI cohort have impacted clinical trials in a number of ways. Fox and coworkers developed improved methods for measuring the rate of atrophy across multiple sites and for reducing required sample sizes [39] [40] [41], and also developed automated methods to measure brain and hippocampal volume and rates of atrophy [39] [42] [43]. These have been incorporated into large commercial clinical trials and submitted to the European Medicines Agency, leading to guidance on hippocampal volume measurement in trials [24]. "
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    ABSTRACT: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. We searched for ADNI publications using established methods. ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials. Copyright © 2015 The Alzheimer's Association. All rights reserved.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 07/2015; 11(7):865-84. DOI:10.1016/j.jalz.2015.04.005 · 17.47 Impact Factor
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    • "Using these biomarkers as outcome measures in trials would also have the potential to show a disease modifying effect on fewer subjects than standard cognitive tests, with proper enrichment strategies making these useful for predementia trials. (Grill et al., 2013; Schott et al., 2010). "
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    ABSTRACT: Brain atrophy measured using structural MRI has been widely used as an imaging biomarker for disease diagnosis and tracking of pathological progression in neurodegenerative diseases. In this work, we present a generalised and extended formulation of the Boundary Shift Integral (gBSI) using probabilistic segmentations in order to estimate anatomical changes between 2 time points. This method adaptively estimates a non-binary XOR region-of-interest from probabilistic brain segmentations of the baseline and repeat scans, in order to better localise and capture the brain atrophy. We evaluate the proposed method by comparing the sample size requirements for a hypothetical clinical trial of Alzheimer’s disease to that needed for the current implementation of BSI as well as a fuzzy implementation of BSI. The gBSI method results in a modest, but reduced sample size, providing increased sensitivity to disease changes through the use of the probabilistic XOR region.
    Neurobiology of Aging 08/2014; 36. DOI:10.1016/j.neurobiolaging.2014.04.035 · 4.85 Impact Factor
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    • "Researchers may be unsure or sceptical as to how much of an increase in power is likely to occur in practice. Few articles have examined this issue using real data, and most have been limited to datasets in traumatic brain injury [2,5,8] or Alzheimer’s disease [20]. Further research to assess the potential increase in power through adjustment for known prognostic factors, and the decrease in power through adjustment for nonprognostic factors, would allow researchers to make more informed decisions as to whether covariate adjustment is likely to be worthwhile in their own trial. "
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    ABSTRACT: Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice. We used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of therapeutic areas. We assessed (1) how large an increase in power can be expected in practice; and (2) the impact of adjustment for covariates that are not prognostic. Adjustment for known prognostic covariates led to large increases in power for most outcomes. When power was set to 80% based on an unadjusted analysis, covariate adjustment led to a median increase in power to 92.6% across the 12 outcomes (range 80.6 to 99.4%). Power was increased to over 85% for 8 of 12 outcomes, and to over 95% for 5 of 12 outcomes. Conversely, the largest decrease in power from adjustment for covariates that were not prognostic was from 80% to 78.5%. Adjustment for known prognostic covariates can lead to substantial increases in power, and should be routinely incorporated into the analysis of randomized trials. The potential benefits of adjusting for a small number of possibly prognostic covariates in trials with moderate or large sample sizes far outweigh the risks of doing so, and so should also be considered.
    Trials 04/2014; 15(1):139. DOI:10.1186/1745-6215-15-139 · 2.12 Impact Factor
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