Abstract
Designing a trial to determine whether or not an intervention has modified the underlying course of the disease is straightforward for certain conditions, such as cancer, in which it is possible to directly measure the disease course. For many other diseases, the disease course is latent, and one must rely on indirect measures such as clinical symptoms to quantify the effects of interventions. In this case, it is difficult with conventional trial designs to determine the extent to which the treatment is modifying the disease course as opposed to merely alleviating the symptoms of the disease. This distinction has become critically important in the study of treatments for neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease, but it applies to many other diseases as well.
This chapter discusses proposed strategies for trial design to attempt to distinguish between the disease-modifying and symptomatic effects of a treatment in diseases with a latent disease course. Two-period designs, such as the withdrawal design and the delayed start design, are being used for this purpose, most commonly in neurodegenerative disease. In these designs, the first period involves a standard randomization of participants to active and placebo treatments. In the second period, those in the active treatment group are switched to placebo (withdrawal design), or those in the placebo group are switched to active treatment (delayed start design). These designs are reviewed in detail in terms of their underlying assumptions, limitations, and strategies for statistical analysis.
References
Ahlskog JE, Uitti RJ (2010) Rasagiline, Parkinson neuroprotection, and delayed-start trials: still no satisfaction? Neurology 74:1143–1148
Athauda D, Foltynie T (2016) Challenges in detecting disease modification in Parkinson’s disease clinical trials. Parkinsonism Relat Disord 32:1–11
Bhattaram VA, Siddiqui O, Kapcala LP, Gobburu JV (2009) Endpoints and analyses to discern disease-modifying drug effects in early Parkinson’s disease. AAPS J 11:456–464
Carpenter JR, Roger JH, Kenward MG (2013) Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation. J Biopharm Stat 23:1352–1371
Clarke CE (2004) A “cure” for Parkinson’s disease: can neuroprotection be proven with current trial designs? Mov Disord 19:491–498
Clarke CE (2008) Are delayed-start design trials to show neuroprotection in Parkinson’s disease fundamentally flawed? Mov Disord 23:784–789
Cummings JL (2009) Defining and labeling disease-modifying treatments for Alzheimer’s disease. Alzheimers Dement 5:406–418
Cummings J (2017) Disease modification and neuroprotection in neurodegenerative disorders. Transl Neurodegener 6:25. https://doi.org/10.1186/s40035-017-0096-2
D’Agostino RB Jr (1998) Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 17:2265–2281
D’Agostino RB Sr (2009) The delayed-start study design. N Engl J Med 361:1304–1306
Dorsey ER, Bloem BR (2018) The Parkinson pandemic – a call to action. JAMA Neurol 75:9–10
Emery P, Breedveld FC, Hall S, Durez P, Chang DJ, Robertson D, Singh A, Pedersen RD, Koenig AS, Freundlich B (2008) Comparison of methotrexate monotherapy with a combination of methotrexate and etanercept in active, early, moderate to severe rheumatoid arthritis (COMET): a randomised, double-blind, parallel treatment trial. Lancet 372:375–382
Guimaraes P, Kieburtz K, Goetz CG, Elm JJ, Palesch YY, Huang P, Ravina B, Tanner CM, Tilley BC (2005) Non-linearity of Parkinson’s disease progression: implications for sample size calculations in clinical trials. Clin Trials 2:509–518
Holford N (2015) Clinical pharmacology = disease progression + drug action. Br J Clin Pharmacol 79:18–27
Holford NHG, Nutt JG (2011) Interpreting the results of Parkinson’s disease clinical trials: time for a change. Mov Disord 26:569–577
International Conference on Harmonization (2017) ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials: Step 2b, 16 June 2017
Kaye JA (2000) Methods for discerning disease-modifying effects in Alzheimer disease treatment trials. Arch Neurol 57:312–314
Kieburtz K (2006) Issues in neuroprotection clinical trials in Parkinson’s disease. Neurology 66(Suppl 4):S50–S57
Leber P (1996) Observations and suggestions on antidementia drug development. Alzheimer Dis Assoc Disord 10(Suppl 1):31–35
Li JD, Barlas S (2017) Divergence effect analysis in disease-modifying trials. Statist Biopharm Res 9:390–398
Little RJA, Rubin DB (2002) Statistical analysis with missing data. John Wiley and Sons, Hoboken
Little R, Yau L (1996) Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 52:1324–1333
Liu GF, Pang L (2017) Control-based imputation and delta-adjustment stress test for missing data analysis in longitudinal clinical trials. Statist Biopharm Res 9:186–194
Liu-Seifert H, Andersen SW, Lipkovich I, Holdridge KC, Siemers E (2015) A novel approach to delayed-start analyses for demonstrating disease-modifying effects in Alzheimer’s disease. PLoS One 10(3):e0119632. https://doi.org/10.1371/journal.pone.0119632
Mallinckrodt CH, Lane PW, Schnell D, Peng Y, Mancuso JP (2008) Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials. Drug Inf J 42:303–319
Mallinckrodt CH, Lin Q, Lipkovich I, Molenberghs G (2012) A structured approach to choosing estimands and estimators in longitudinal clinical trials. Pharm Stat 11:456–461
Mallinckrodt C, Molenberghs G, Rathmann S (2017) Choosing estimands in clinical trials with missing data. Pharm Stat 16:29–36
McDermott MP, Hall WJ, Oakes D, Eberly S (2002) Design and analysis of two-period studies of potentially disease-modifying treatments. Control Clin Trials 23:635–649
Mills E, Heels-Ansdell D, Kelly S, Guyatt G (2007) A randomized trial of pegaptanib sodium for age-related macular degeneration used an innovative design to explore disease-modifying effects. J Clin Epidemiol 60:456–460
Molenberghs G, Kenward MG (2007) Missing data in clinical studies. John Wiley and Sons, Chichester
Molenberghs G, Thijs H, Jansen I, Beunckens C, Kenward MG, Mallinckrodt C, Carroll RJ (2004) Analyzing incomplete longitudinal clinical trial data. Biostatistics 5:445–464
National Research Council (2010) The prevention and treatment of missing data in clinical trials. National Academies Press, Washington, DC
O’Kelly M, Ratitch B (2014) Clinical trials with missing data: a guide for practitioners. John Wiley and Sons, Chichester
Offen W, Chuang-Stein C, Dmitrienko A, Littman G, Maca J, Meyerson L, Muirhead R, Stryszak P, Baddy A, Chen K, Copley-Merriman K, Dere W, Givens S, Hall D, Henry D, Jackson JD, Krishen A, Liu T, Ryder S, Sankoh AJ, Wang J, Yeh C-H (2007) Multiple co-primary endpoints: medical and statistical solutions. Drug Inf J 41:31–46
Olanow CW, Hauser RA, Jankovic J, Langston W, Lang A, Poewe W, Tolosa E, Stocchi F, Melamed E, Eyal E, Rascol O (2008) A randomized, double-blind, placebo-controlled, delayed start study to assess rasagiline as a disease modifying therapy in Parkinson’s disease (the ADAGIO study): rationale, design, and baseline characteristics. Mov Disord 15:2194–2201
Olanow CW, Rascol O, Hauser R, Feigin PD, Jankovic J, Lang A, Langston W, Melamed E, Poewe W, Stocchi F, Tolosa E, the ADAGIO Study Investigators (2009) A double-blind, delayed-start trial of rasagiline in Parkinson’s disease. N Engl J Med 361:1268–1278
Ploeger BA, Holford NHG (2009) Washout and delayed start designs for identifying disease modifying effects in slowly progressive diseases using disease progression analysis. Pharm Stat 8:225–238
Sano M, Ernesto C, Thomas RG, Klauber MR, Schafer K, Grundman M, Woodbury P, Growdon J, Cotman CW, Pfeiffer E, Schneider LS, Thal LJ (1997) A controlled trial of selegiline, alpha-tocopherol, or both as treatment for Alzheimer’s disease. N Engl J Med 336:1216–1222
Schafer JL (1997) Analysis of incomplete multivariate data. Chapman and Hall/CRC, Boca Raton
Schapira AHV, Albrecht S, Barone P, Comella CL, McDermott MP, Mizuno Y, Poewe W, Rascol O, Marek K (2010) Rationale for delayed-start study of pramipexole in Parkinson’s disease: the PROUD study. Mov Disord 25:1627–1632
Sormani MP, Bruzzi P (2013) MRI lesions as a surrogate for relapses in multiple sclerosis: a meta-analysis of randomised trials. Lancet Neurol 12:669–676
Tang Y (2017) An efficient multiple imputation algorithm for control-based and delta-adjusted pattern mixture models using SAS. Statist Biopharm Res 9:116–125
The Parkinson Study Group (1989) Effect of deprenyl on the progression of disability in early Parkinson’s disease. N Engl J Med 321:1364–1371
The Parkinson Study Group (1993) Effects of tocopherol and deprenyl on the progression of disability in early Parkinson’s disease. N Engl J Med 328:176–183
The Parkinson Study Group (2004) Levodopa and the progression of Parkinson’s disease. N Engl J Med 351:2498–2508
Vellas B, Andrieu S, Sampaio C, Wilcock G, the European Task Force Group (2007) Disease-modifying trials in Alzheimer’s disease: a European task force consensus. Lancet Neurol 6:56–62
Vellas B, Andrieu S, Sampaio C, Coley N, Wilcock G, the European Task Force Group (2008) Endpoints for trials in Alzheimer’s disease: a European task force consensus. Lancet Neurol 7:436–450
Whitehouse PJ, Kittner B, Roessner M, Rossor M, Sano M, Thal L, Winblad B (1998) Clinical trial designs for demonstrating disease-course-altering effects in dementia. Alzheimer Dis Assoc Disord 12:281–294
Xiong C, Luo J, Gao F, Morris JC (2014) Optimizing parameters in clinical trials with a randomized start or withdrawal design. Comput Statist Data Anal 69:101–113
Zhang RY, Leon AC, Chuang-Stein C, Romano SJ (2011) A new proposal for randomized start design to investigate disease-modifying therapies for Alzheimer disease. Clin Trials 8:5–14
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this entry
Cite this entry
McDermott, M.P. (2021). Designs to Detect Disease Modification. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52677-5_93-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-52677-5_93-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52677-5
Online ISBN: 978-3-319-52677-5
eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering