Advertisement

Minimizing the Sample Sizes of Clinical Trials on Preclinical and Early Symptomatic Stage of Alzheimer Disease

  • J. Luo
  • H. Weng
  • J. C. Morris
  • Chengjie Xiong
Brief Report

Abstract

Background

Clinical trials of investigational drugs for Alzheimer disease (AD) increasingly focus on the prodromal (symptomatic) stage of the illness and now its preclinical (asymptomatic) stage. Sensitive and specific cognitive and functional endpoints are needed to track subtle cognitive and functional changes in the early and preclinical stages to minimize sample sizes in these trials.

Objectives

To identify informative items in a standard clinical assessment protocol and a psychometric battery that are predictive of onset of dementia symptom.

Design

Longitudinal retrospective study.

Setting

Washington University (WU) Knight Alzheimer Disease Research Center (ADRC).

Participants

A total of 735 individuals at least 65 years old and cognitively normal at baseline from a longitudinal clinical cohort at the WU Knight ADRC.

Measurements

The annual clinical assessment included a wide spectrum of functional and cognitive domains; a comprehensive psychometric battery was completed about 2 weeks after the clinical evaluation. Psychometricians are blinded to the results of the clinical evaluation and to the prior performance of the participants on the psychometric tests.

Results

The mean age at baseline of the 735 participants was 74.30 and 62.31% were female. 240 individuals developed prodromal dementia symptoms (consistent with mild cognitive impairment due to AD and with very mild AD dementia) during longitudinal follow-up (mean follow-up=6.79 years). Among a total of 562 items in the clinical and cognitive assessments under analysis, 292 (52%) were identified as informative because their longitudinal changes were predictive of symptomatic onset. When these items were used to form the functional and cognitive composites, the longitudinal rates of changes were free of a learning effect and captured subtle longitudinal progression prior to symptomatic onset. The rates of change were much greater right after the symptomatic onset than those from the functional and cognitive composites formed using non-informative items. Although the sample sizes for prevention trials (prior to symptomatic onset) using the informative items still yield large numbers, the sample sizes for early treatment trial (after symptomatic onset) was much smaller than those derived from all the items or from the noninformative items alone.

Conclusions

The antecedent longitudinal changes in nearly half of the items in a clinical assessment protocol and a comprehensive cognitive battery did not show statistically significant ability to predict the dementia symptom onset, and hence may be non-informative to track the preclinical functional and cognitive progression of AD. The remaining items, on the other hand, captured some of the preclinical changes prior to the symptom onset, but performed much better right after the symptom onset. Currently ongoing prevention trials on preclinical AD of elderly individuals may need to re-assess the sample sizes and statistical power.

Key words

Age of symptom onset Alzheimer disease prevention trials treatment trials informative items power 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Price JL, Morris JC: Tangles and plaques in nondemented aging and «preclinical» Alzheimer’s disease. Ann Neurol 1999, 45(3):358–368.CrossRefPubMedGoogle Scholar
  2. 2.
    Sperling RA, Aisen PS, Beckett LA et al: Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011, 7(3):280–292.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Sheehan B: Assessment scales in dementia. Ther Adv Neurol Disord 2012, 5(6):349–358.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Twamley EW, Ropacki SA, Bondi MW: Neuropsychological and neuroimaging changes in preclinical Alzheimer’s disease. J Int Neuropsychol Soc 2006, 12(5):707–735.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Morris JC: The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993, 43(11):2412–2414.CrossRefPubMedGoogle Scholar
  6. 6.
    JA S Ja Y: Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontology: A Guide to Assessment and Intervention. In. New York: The Haworth Press; 1986: 165–173.Google Scholar
  7. 7.
    Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL: A new clinical scale for the staging of dementia. Br J Psychiatry 1982, 140:566–572.CrossRefPubMedGoogle Scholar
  8. 8.
    Pfeiffer E: A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. J Am Geriatr Soc 1975, 23(10):433–441.CrossRefPubMedGoogle Scholar
  9. 9.
    Folstein MF, Folstein SE, McHugh PR: «Mini-mental state». A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975, 12(3):189–198.CrossRefPubMedGoogle Scholar
  10. 10.
    Katzman R, Brown T, Fuld P et al: Validation of a short Orientation-Memory-Concentration Test of cognitive impairment. Am J Psychiatry 1983, 140(6):734–739.CrossRefPubMedGoogle Scholar
  11. 11.
    Goodglass HaK, E: The Assessment of Aphasia and Related Disorders, 2nd Edition. Philadelphia: Lea & Febiger; 1972.Google Scholar
  12. 12.
    Cummings JL: The Neuropsychiatric Inventory: assessing psychopathology in dementia patients. Neurology 1997, 48(5 Suppl 6):S10–16.CrossRefPubMedGoogle Scholar
  13. 13.
    Pfeffer RI, Kurosaki TT, Harrah CH, Jr., Chance JM, Filos S: Measurement of functional activities in older adults in the community. J Gerontol 1982, 37(3):323–329.CrossRefPubMedGoogle Scholar
  14. 14.
    Ferman TJ, Smith GE, Boeve BF et al: DLB fluctuations - Specific features that reliably differentiate DLB from AD and normal aging. Neurology 2004, 62(2):181–187.CrossRefPubMedGoogle Scholar
  15. 15.
    Blessed G, Tomlinson, B.E., and Roth, M..: The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects. British Journal of Psychiatry 1968, 1114:797–811.CrossRefGoogle Scholar
  16. 16.
    Wechsler D: Wechsler Memory Scale-Revised. In. San Antonio, Texas: Psychological Corporation; 1987.Google Scholar
  17. 17.
    Wechsler DaS, C.P: Wechsler Memory Scale. In. New York: Psychological Corporation; 1973.Google Scholar
  18. 18.
    Benton AL: The Revised Visual Retention Test: Clinical and experimental applications. In. New York: Psychological Corporation; 1963.Google Scholar
  19. 19.
    Goodglass HaK, E.,: Boston Diagnostic Aphasia Examination Booklet. In., 3rd ed. edn. Philadelphia: Lea & Febiger; 1983.Google Scholar
  20. 20.
    Wechsler D: Wechsler Adult Intelligence Scale. In. New York: Psychological Corporation; 1955.Google Scholar
  21. 21.
    Grober E, Buschke H, Crystal H, Bang S, Dresner R: Screening for dementia by memory testing. Neurology 1988, 38(6):900–903.CrossRefPubMedGoogle Scholar
  22. 22.
    Morris JC, Weintraub S, Chui HC et al: The uniform data set (UDS): Clinical and cognitive variables and descriptive data from Alzheimer disease centers. Alz Dis Assoc Dis 2006, 20(4):210–216.CrossRefGoogle Scholar
  23. 23.
    American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 4 edn. Washington DC: American Psychiatric Association; 2000.Google Scholar
  24. 24.
    Bogaerts K, Lesaffre E: Estimating local and global measures of association for bivariate interval censored data with a smooth estimate of the density. Statistics in Medicine 2008, 27(28):5941–5955.CrossRefPubMedGoogle Scholar
  25. 25.
    Sperling RA, Rentz DM, Johnson KA et al: The A4 Study: Stopping AD Before Symptoms Begin? Science Translational Medicine 2014, 6(228).Google Scholar
  26. 26.
    Mills SM, Mallmann J, Santacruz AM et al: Preclinical trials in autosomal dominant AD: Implementation of the DIAN-TU trial. Rev Neurol-France 2013, 169(10):737–743.CrossRefGoogle Scholar
  27. 27.
    Ayutyanont N, Langbaum JBS, Hendrix SB et al: The Alzheimer’s Prevention Initiative Composite Cognitive Test Score: Sample Size Estimates for the Evaluation of Preclinical Alzheimer’s Disease Treatments in Presenilin 1 E280A Mutation Carriers. J Clin Psychiat 2014, 75(6):652–660.CrossRefGoogle Scholar
  28. 28.
    Xiong C, Zhu, K, Yu, K, Miller, JP: Statistical Modeling in Biomedical Research: Longitudinal Data Analysis. In: Handbook of Statistics. Volume 27, edn. Edited by Rao CR MJ, Rao DC. London: Elsevier; 2007: 429–463.Google Scholar
  29. 29.
    Ringman JM, Grill J, Rodriguez-Agudelo Y, Chavez M, Xiong C: Commentary on «a roadmap for the prevention of dementia II: Leon Thal Symposium 2008.» Prevention trials in persons at risk for dominantly inherited Alzheimer’s disease: opportunities and challenges. Alzheimers Dement 2009, 5(2):166–171.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Mohs RC, Knopman D, Petersen RC et al: Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord 1997, 11 Suppl 2:S13–21.CrossRefPubMedGoogle Scholar

Copyright information

© Serdi and Springer Nature Switzerland AG 2018

Authors and Affiliations

  • J. Luo
    • 1
    • 2
    • 3
  • H. Weng
    • 3
  • J. C. Morris
    • 4
    • 5
    • 6
  • Chengjie Xiong
    • 3
    • 4
    • 7
  1. 1.Division of Public Health Sciences, Department of SurgeryWashington University School of MedicineSt. LouisUSA
  2. 2.Siteman Cancer Center Biostatistics CoreWashington University School of MedicineSt. LouisUSA
  3. 3.Division of BiostatisticsWashington University School of MedicineSt. LouisUSA
  4. 4.Knight Alzheimer Disease Research CenterWashington University School of MedicineSt. LouisUSA
  5. 5.Departments of Pathology and ImmunologyWashington University School of MedicineSt. LouisUSA
  6. 6.Department of NeurologyWashington University School of MedicineSt. LouisUSA
  7. 7.Division of BiostatisticsSt. LouisUSA

Personalised recommendations