Saunders, O 1, Asseburg, C 2, O Reilly, K 3, Sly, I 1, Lee, D 1
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1 Modelling Alzheimer s disease progression using a multivariate model for the associated outcomes of cognition, behaviour and functioning: Data from the ICTUS Study Saunders, O 1, Asseburg, C, O Reilly, K 3, Sly, I 1, Lee, D 1 1 BresMed, Sheffield, UK; Swedish Institute for Health Economics, Otsuka Pharmaceutical Companies Europe and ICTUS/DSA group ISPOR nd Annual, International Meeting, May, 17, Boston, MA, USA. 1
2 Acknowledgements The ICTUS study was partially supported by a grant from the European Commission within the 5th framework programme (QLK6-CT--645) and partially from an unrestricted equal grant from each of Eisai, Janssen, Lundbeck, and Novartis pharmaceutical companies. The pharmaceutical companies had no role in study design, data collection, data analysis or data interpretation. Promotion of the ICTUS study was supported by the University Hospital Centre of Toulouse. The data sharing activity was supported by the Association Monegasque pour la recherche sur la maladie d Alzheimer (AMPA) and the UMR 17 Unit INSERM University of Toulouse III. With thanks to the ICTUS study group: Vellas B., Reynish E., Ousset PJ., Andrieu S. (Toulouse), Burns A. (Manchester),Pasquier F. (Lille), Frisoni G.(Brescia),Salmon E. (Liège), Michel J.P., Zekry D.S. (Geneva), Boada M. (Barcelona), Dartigues J.F. (Bordeaux), Olde-Rikkert M.G.M. (Nijmejen), Rigaud A.S. (Paris), Winblad B. (Huddinge), Malick A., Sinclair A. (Warwick), Frölich L.(Mannheim), Scheltens P. (Amsterdam), Ribera C.(Madrid), Touchon J. (Montpellier), Robert P. (Nice), Salva A.(Barcelona), Waldmar G.(Copenhagen),Bullock R.(Swindon), Costa-Tsolaki M. (Thesaloniki), Rodriguez G. (Genoa), Spiru L. (Bucharest), Jones R.W. (Bath), Stiens G., Stoppe G. (Goettingen), Eriksdotter Jönhagen M. (Stockholm), Cherubini A. (Perugia), Lage P.M., Gomez-Isla T. (Pamplona), Camus V. (Tours), Agüera-Morales E., Lopez F.(Cordoba). DSA Group: Andrieu S., Savy S., Cantet C., Coley N.
3 Contents Objectives Dataset Statistical problem Proposed solution Model Application of the model to the ICTUS data Modelling Results 3
4 Objectives Dependence in Alzheimer s disease (AD) can be characterized as the impact of changes in cognition, function and behaviour that result in increased need for assistance. Although domains measure different aspects of the disease, they have an inter-related and aggregate effect 1 Develop a model for AD progression using markers of cognition, function and behaviour. Required to predict all markers simultaneously while respecting the correlation structure To our knowledge this has not previously been attempted in mild/moderate AD 1 McLaughlin T et al. Alzheimers Dement. 1 Nov;6(6):
5 Dataset Prospectively collected longitudinal data from Europeans with AD. Study Name: The Impact of Treatment with Anticholinesterase Inhibitors (AChEI) on Europeans with AD Acronym: ICTUS (Impact of Cholinergic Treatment Use) Number of patients 1375 patients Study details Inclusion criteria Participating centres Timeframe Analysis set Mild to moderate AD. Defined as a Mini Mental State Examination (MMSE) between 1 and 6 at inclusion. 9 hospital clinics in 1 European countries Patients were recruited at outpatient visits between February 3 and July 5 and followed for up to 36 months Analysis was conducted on 98 patients stable on AChEI (4 months of continuous treatment) 5
6 The ICTUS study provides longitudinal data on markers of AD The Alzheimer s Disease Assessment Scale Cognition (ADAS-Cog) Activities of Daily Living Scale - (ADL) Function Instrumental Activities of Daily Living Scale - (IADL) - Function Neuropsychiatric Inventory - Behaviour (NPI) 6
7 Statistical problem Model must be able to robustly predict disease progression using these outcomes and utilizing baseline (e.g. sex, age at baseline) and time dependent covariates (e.g. concurrent donepezil, galantamine treatment.) The correlation structure of these outcomes must be respected; it is anticipated that the evolution of these variables over time is not independent Patients with deteriorations of cognition are also expected to have simultaneous deteriorations in function and behaviour Ignoring this correlation is likely to misrepresent disease evolution in AD patients The repeated measures nature of these data must also be properly accounted for; multiple observations from the same marker from the same person are correlated 7
8 Proposed solution Multivariate linear growth models were proposed as a method of constructing predictive equations linking cognition, function and behaviour These models have previously been described by Gao et al 1 and Thiebaut et al and can be implemented in R/SAS Similar models have previously been applied to the study of AD progression, Harvey et al 3 Models are a hybrid of MANOVA and random effects models Models provide a more flexible and methodologically robust method to model the joint evolution of decline in cognition, behaviour and functioning than has previously been used in mild/moderate AD 1. Gao, F., et al. SUGI San Francisco.. Thiebaut, R., et al. Computer Methods Programs Biomed ; 69: Harvey J.D., et al. J Alzheimer s Dis 3;5:
9 Model (bivariate case) y i,j, IADL y i,j, ADL = β, IADL + β 1,IADL. time i,j + β,iadl. Sex i,j β, ADL + β 1,ADL. time i,j + β,adl. Sex i,j + b i,,iadl + b i,1iadl. time i,j + b i,,adl + b i,1,adl. time i,j Random Effects Component b i,,iadl b i,1,iadl b i,1,adl b i,1,adl ~ MVN 4, σ 1 σ 1 σ 1 σ σ 13 σ 3 σ 14 σ 34 σ 13 σ 14 σ 3 σ 4 σ 3 σ 34 σ 34 σ 4 Residual Error ε i,j,iadl ~N(, σ IADL ) ) ε i,j,adl ~N(, σ ADL ε i,j,iadl + ε ൰ i,j,adl 9
10 Application of the model to the ICTUS data Model fitted simultaneously to the four markers; ADAS-Cog, ADL, IADL and NPI. Covariates with low explanatory power were dropped (simultaneously for all three outcomes) using the value of Akaike s Information Criterion (AIC) Covariates were selected based upon their prognostic ability and the desire to make predictions for different patients populations within subsequent economic modelling: Time since baseline visit (years) AChEI treatment Sex Number of years of formal education Age Duration since diagnosis at baseline visit (years) Family history of AD (=none, 1=yes) Marital status (=not married, 1=married) Region (Source vs West vs Northern) Baseline MMSE score Baseline NPI score 1
11 Modelling Exploratory modelling revealed implausible covariate estimates and unsatisfactory residual diagnostics for NPI; Residuals diagnostics showed heavy tails with errors increasing with predicted values (heteroscedasticity) NPI was therefore removed as an outcome and included as a baseline covariate Based on AIC, the following variables increased the explanatory power of the model; number of years since baseline, sex, number of years of formal education, age, region, Baseline MMSE and baseline NPI Due to missing covariate information, the final model included 958 (98%) of the total population; patients stable on AChEI 11
12 Results Fixed effect coefficient values (S.E/p-value) ADAS-Cog ADL IADL Intercept (.697 : <.1 ) 6. (.34 : <.1 ) 6.1 (.7 : <.1 ) Number of years since baseline 4.59 (.198 : <.1 ) -.5 (.5 : <.1 ) -.88 (.4 : <.1 ) Sex - Male.47 (.469 :.319 ). (.57 :.945 ) -.59 (.149 : <.1 ) Age.1 (.9 :.83 ) -. (.4 : <.1 ) -.6 (.8 : <.1 ) Number of years of formal education -.3 (.53 :.61 ).1 (.6 :.354 ).1 (.14 :.477 ) Southern Europe.6 (.763 :.3 ) -.9 (.91 :.1 ).6 (.191 :.18 ) Western Europe -.86 (.736 : <.1 ) -. (.88 :.833 ).43 (.186 :.1 ) Baseline NPI.4 (.18 :.1 ) -.1 (. : <.1 ) -.4 (.5 : <.1 ) Baseline MMSE -1.4 (.54 : <.1 ).6 (.7 : <.1 ).16 (.14 : <.1 ) 1
13 ADAS-Cog score ADL/IADL score Results Predicted mean for an average patient over -year observation period 7 ADAS-Cog score 1 ADL/IADL score Time in months - Time in months ADAS-Cog mean ADAS-Cog upper bound ADAS-Cog lower bound ADL mean ADL upper bound IADL lower bound ADL lower bound IADL mean IADL upper bound 13
14 Results Joint evolution of deterioration The model showed a strong multivariate relationship between the randomly distributed disturbance in the rate of change for each patient for the three markers of AD; ADAS- Cog, ADL, IADL There was a strong positive correlation in the randomly distributed disturbance in the rate of decline of ADL and IADL (rho=.65) This is expected, both are markers of functioning and are expected to decline together Strong negative correlation in the randomly distributed disturbance in the rate of decline of ADAS-cog and ADL and ADAS-cog and IADL (rho = -.7 and -.55) ADAS-Cog increases as cognition deteriorates, ADL and IADL decrease as functioning deteriorates 14
15 Incorporating results into a cost effectiveness model Results from this multivariate linear growth model were incorporated into a discrete event simulation cost effectiveness model by addition of each of the following components; 1. Fixed effect components were derived by multiplying simulated covariate values by covariate estimates. Random effect disturbances were randomly drawn (for each patient) from the multivariate normal distribution: b i,,iadl b i,1,iadl b i,1,adl b i,1,adl ~ MVN 4, σ 1 σ 1 σ 1 σ σ 13 σ 3 σ 14 σ Residual error disturbances for each patient at each timepoint were randomly drawn from the univariate distribution specific to each of the endpoints: ε i,j,iadl ~N(, σ IADL ) ) ε i,j,adl ~N(, σ ADL σ 13 σ 14 σ 3 σ 4 σ 3 σ 34 σ 34 σ 4 15
16 Conclusions Multivariate linear growth models provide an intuitive and easy to implement methodology to model the joint evolution of decline in correlated endpoints This study demonstrates the strength of correlation between deterioration in cognition and functioning; providing an example of how to account for this within prediction models Whilst behaviour, as measured using NPI is also considered a conceptually important marker for AD progression; within this study it was not feasible to model all four markers simultaneously 16
17 References McLaughlin et al. Dependence as a unifying construct in defining Alzheimer s disease severity. Alzheimers Dement. 1 Nov;6(6): Gao, F., et al. Analyzing Multivariate Longitudinal Data Using SAS. in SUGI San Francisco. Harvey, D.J., L.A. Beckett, and D.M. Mungas, Multivariate modeling of two associated cognitive outcomes in a longitudinal study. J Alzheimers Dis, 3. 5(5): p Thiebaut, R., et al., Bivariate linear mixed models using SAS proc MIXED. Computer Methods and Programs in Biomedicine,. 69: p
18 Back-up 18
19 Results Other interesting observations Interpretation of random intercepts: Moderate positive correlation between random intercepts for ADL and IADL (corr =.6) Patients with high ADL at baseline also tend to have IADL at baseline There is limited correlation between the intercepts for the other markers Interpretation of correlation between random intercepts and slopes: Only weak correlation observed between random intercepts and slopes No relationship observed between values at baseline and deterioration rate 19
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