For debate: substituting placebo controls in longterm Alzheimer's prevention trials
 René Spiegel^{1}Email author,
 Manfred Berres^{2},
 André R Miserez^{1, 3},
 Andreas U Monsch^{1} and
 the Alzheimer's Disease Neuroimaging Initiative
DOI: 10.1186/alzrt68
© Spiegel et al.; licensee BioMed Central Ltd. 2011
Received: 30 August 2010
Accepted: 21 March 2011
Published: 21 March 2011
Abstract
Introduction
Novel compounds with potential to attenuate or stop the progression of Alzheimer's disease (AD) from its presymptomatic stage to dementia are being tested in man. The study design commonly used is the longterm randomized, placebocontrolled trial (RPCT), meaning that many patients will receive placebo for 18 months or longer. It is ethically problematic to expose presymptomatic AD patients, who by definition are at risk of developing dementia, to prolonged placebo treatment. As an alternative to longterm RPCTs we propose a novel clinical study design, termed the placebo group simulation approach (PGSA), using mathematical models to forecast outcomes of presymptomatic AD patients from their own baseline data. Forecasted outcomes are compared with outcomes observed on candidate drugs, thus replacing a concomitant placebo group.
Methods
First models were constructed using mild cognitive impairment (MCI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. One outcome is the Alzheimer Disease Assessment Scale  cognitive subscale (ADAScog) score after 24 months, predicted in a linear regression model; the other is the trajectory over 36 months of a composite neuropsychological test score (NeuroPsychological Battery (NPBatt)), using a mixed model. Demographics and clinical, biological and neuropsychological baseline values were tested as potential predictors in both models.
Results
ADAScog scores after 24 months are predicted from gender, obesity, Functional Assessment Questionnaire (FAQ) and baseline scores of MiniMental State Examination, ADAScog and NPBatt with an R^{2} of 0.63 and a residual standard deviation of 0.67, allowing reasonably precise estimates of sample means. The model of the NPBatt trajectory has random intercepts and slopes and fixed effects for body mass index, time, apolipoprotein E4, age, FAQ, baseline scores of ADAScog and NPBatt, and four interaction terms. Estimates of the residual standard deviation range from 0.3 to 0.5 on a standard normal scale. If novel drug candidates are expected to diminish the negative slope of scores with time, a change of 0.04 per year could be detected in samples of 400 with a power of about 80%.
Conclusions
First PGSA models derived from ADNI MCI data allow prediction of cognitive endpoints and trajectories that correspond well with real observed values. Corroboration of these models with data from other observational studies is ongoing. It is suggested that the PGSA may complement RPCT designs in forthcoming longterm drug studies with presymptomatic AD individuals.
Introduction
A number of compounds with potential to attenuate the progression of Alzheimer's disease (AD) from a presymptomatic stage to dementia  that is, drugs intended for secondary prevention of dementia due to AD  are currently undergoing testing in man [1, 2]. The study design routinely applied in advanced stages of clinical development (late phase 2, phase 3) of central nervous system active compounds is that of the randomized, placebocontrolled clinical trial (RPCT), a procedure implying that a high proportion of patients, up to 50% of the total sample, will receive inactive drug throughout. Given that meaningful study of experimental treatment intended for secondary prevention of dementia due to AD will take 18 months or more for each individual, it is problematic, from an ethical standpoint, to expose patients with mild cognitive impairment (MCI) and similar conditions, who by definition run a high risk of developing dementia, to prolonged exposure to placebo [3]. In addition, the external validity (representativity) of longterm RPCTs may be questioned, as many potential trial participants will decline inclusion in a study that intentionally exposes them to the risk of prolonged inactive treatment.
Although several groups of investigators have discussed more focused and/or more timeeconomical approaches to testing potential AD coursealtering treatments [4–8], including variations of the conventional parallelgroup clinical study design, little or no attention has been paid to the two fundamental problems of longterm RPCTs in highrisk individuals: the ethical issue of extended placebo exposure, and the problem of the trials' external validity. Here we propose a novel clinical strategy  the placebo group simulation approach (PGSA)  thought to be a viable alternative to longterm RPCTs and able to overcome a serious ethical and scientific dilemma of current clinical research in AD and similar conditions. Making use of anamnestic, biological, neuropsychological and other subject data routinely established at study baseline, the PGSA comprises mathematical modeling and forecasting of typical AD disease trajectories from its presymptomatic to symptomatic stages. Based on such forecasts, the endpoints and trajectories of patients undergoing experimental treatment intended for secondary prevention of dementia due to AD can be compared with their own modeled disease course; that is, with their predicted endpoints and trajectories had they not been treated. Based on these comparisons between observed and modelbased outcomes, the efficacy of putative AD coursealtering drugs can be delineated.
The present article describes the development of the predictive models, based on a large, multidimensional dataset collected from individuals characterized as MCI subjects  that is, presymptomatic individuals with a high risk to develop dementia within a few years. As will be noted, the PGSA differs from traditional historical control and observation studies with regard to three critical aspects. First, this approach uses mathematical modeling to identify and quantify those measures at baseline that allow one to forecast cognitive and/or other clinically relevant outcomes after a predetermined time period. Second, based on a variety of measures established at baseline, the PGSA provides a quantified prediction as to the expected time course of the outcomes selected by the investigators. Third, the predictive models of the PGSA are based on large sets of uniformly collected longitudinal observational data of properly defined patient samples. Furthermore, although the PGSA could be expanded to other areas of medicine where longitudinal data allowing computation of respective models are available, the focus of the present paper is on AD.
Materials and methods
Subjects and procedures
Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database [9]. The ADNI was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies and nonprofit organizations. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and the progression of mild cognitive impairment and early Alzheimer's disease. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians in developing new treatments and monitoring their effectiveness, as well as to lessen the time and cost of clinical trials.
The Principal Investigator of this initiative is Michael W Weiner, MD (VA Medical Center and University of California  San Francisco, CA, USA). The ADNI is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the United States and Canada. The initial goal of the ADNI was to recruit 800 adults, ages 55 to 90, to participate in the research  approximately 200 cognitively normal older individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years and 200 people with early AD to be followed for 2 years. Uptodate information is available online [9].
For the present analyses, we focus on the MCI subjects in the ADNI database. Participants were classified as MCI in the ADNI project when they had a MiniMental State Examination (MMSE) [10] score between 24 and 30, a memory complaint, a memory loss measured by educationadjusted scores on the Wechsler Memory Scale  Logical Memory II, a Clinical Dementia Rating [11] of 0.5, an absence of significant levels of impairment in other cognitive domains, essentially preserved activities of daily living, and an absence of dementia [12].
Demographic and baseline data for the mild cognitive impairment group
Characteristic  Value 

(n = 397)  
Age (years)  74.2 (7.4) 
Women  141 (36%) 
Apolipoprotein E4  
0 E4 alleles  185 (47%) 
1 E4 allele  165 (42%) 
2 E4 alleles  47 (12%) 
Education (years)  15. 7 (3.0) 
Body mass index (kg/m^{2})  26.1 (4.0) 
Hachinski modified (0 to 12)  1 (0 to 4) 
FAQ (0 (normal) to 50)  2 (0 to 21) 
MMSE (0 to 30 (best))  27.0 (1.8) 
ADAScog, modified (0 (best) to 85)  18.6 (6.3) 
NPBatt (z score)  1.02 (0.66) 
Medication  240 (60%) 
(n = 199)  
βamyloid_{142}  146.9 (48.3 to 298.8) 
Total tau protein  85.6 (28.5 to 463.2) 
βamyloid_{142}/total tau protein  1.564 (0.233 to 7.61) 
Baseline data and neuropsychological assessments
On every visit, the following neuropsychological tests were administered: MMSE, Alzheimer Disease Assessment Scale  cognitive subscale (ADAScog) [13], Wechsler Memory Scale  Revised Logical Memory I and II, Auditory Verbal Learning Test, Boston Naming Test, Trail Making Test A and B, Digit Symbol Test (forward and backward), Clock Drawing Test and Category Fluency (animals and vegetables) [14]. For what we term univariate (endpointrelated) analysis, we used the ADAScog modified total score  that is, the traditional ADAScog total score plus Delayed Word Recall and Digit Cancellation with a maximum score of 85 (Table 1). For what is termed multivariate (trajectoryrelated) analysis, we computed a neuropsychological composite score (NeuroPsychological Battery (NPBatt)) as defined by Cronk and colleagues [15]. This is the average of nine z scores determined by standardization with means and standard deviations (SDs) of the normal controls of the ADNI database. The nine subtests are: Logical Memory II, Digit Span Forward, Digit Span Backward, category fluency animals, category fluency vegetables, Trail Making B, Boston Naming Test, Auditory Verbal Learning Test, and Digit Symbol Test.
Response variables for our models were ADAScog at 24 months and NPBatt scores at all followup visits. We used baseline evaluations of these scores, plus demographics (age, gender, years of education, body mass index, number of apolipoprotein E4 alleles), the Hachinski modified score, the total score of the FAQ [16] and the MMSE score as potential predictors in our models. The body mass index is divided into three classes: <25 kg/m^{2}, ≥25 to <30 kg/m^{2}, ≥30 kg/m^{2}. In supplementary analyses with a smaller subject sample we also included the ratio of the cerebrospinal fluid biomarkers βamyloid_{142} (Aβ42) over total tau protein (Ttau) as a potential predictor.
Statistical analysis
In the univariate analysis we forecast the ADAScog after 24 months from demographic variables, apolipoprotein E4, body mass index, modified Hachinski score, and baseline values of FAQ, MMSE, ADAScog and NPBatt scores in a linear regression model. Twentyfour months were chosen because this is a time span in which significant cognitive decline may be expected in MCI subjects and because there were many missing values in the database at later times. As the distribution of ADAScog scores is heavily skewed to the right, a squareroot transformation was applied. Variable selection is based on the Akaike Information Criterion [17] and, after the main effects model is found, all pairwise interactions are tested for inclusion in this model. Simulated control group data are generated by randomly generating a parameter vector from the multivariate normal distribution of the estimated parameter vector, by applying this parameter vector to the covariate values of the individual considered (that is, forming the linear combination), and by adding random noise with the variance of the residuals. Power calculation is based on the assumption that treated individuals would have a lower ADAScog score at 24 months (that is, less deterioration) than untreated controls. The standard power formula for comparison of two means is applied. Since a randomized treatment is uncorrelated with other covariates, this formula drops from the more general procedure for parameters in regression models.
The multivariate model is a mixed model with random intercepts and random slopes versus time [18]. It is computed for the NPBatt score at all followup visits. We include the same predictors as those in the univariate analysis as well as the interactions of these predictors with time in a starting model, and eliminate effects in a stepwise manner based on their P values. We then test whether pairwise interactions of main effects that stayed in the model should be included. Time is coded as the visit number in units of 6 months; that is, the visit at month 36 has the visit number 6. All slopes on time are changes per visit. Simulated control group data are generated in a similar manner as in the univariate model with additional simulation of individual random effects. The power is determined by simulation: we simulated 1,000 treatment datasets (with the covariates of the ADNI MCI) with slope 0.74 and compared each of them with the given control group, which had a slope of 0.94.
For both types of models we display effects in profile plots. We compare the simulated response variables with their observed counterparts. The validity of the model was checked by fivefold crossvalidation; that is, the dataset was randomly split into five equally sized subsets, responses of each subset were predicted by a model estimated from the union of the other subsets and the overall prediction error was determined by comparing all predictions with the actual observed responses. As colinearity might be a concern, we computed the variance inflation factor of each quantitative predictor: they were all below 1.70 (that is, a fairly low value).
Results
Preliminary analysis with the number of apolipoprotein E4 alleles placed into three categories (0, 1, 2) showed that two alleles approximately duplicated the effect of one allele. We therefore put the number of E4 alleles as a numerical predictor in our models.
Univariate analysis
Results of the univariate regression model for the ADAScog total at month 24
Estimate  Standard error  t value  P value  

Intercept  3.84232  0.76473  5.02  <0.0001 
Gender  0.28474  0.08387  3.39  0.0008 
Obesity  0.27526  0.11680  2.36  0.019 
FAQ at baseline  0.07902  0.01787  4.42  <0.0001 
MMSE at baseline  0.06994  0.02534  2.76  0.0062 
ADAScog at baseline  0.09250  0.00768  12.04  < 0.0001 
NPBatt at baseline  0.50529  0.08820  5.73  <0.0001 
FAQ × NPBatt at baseline  0.03762  0.01341  2.81  0.0054 
Fivefold crossvalidation with 20% of the responses predicted in each run achieved a mean prediction error of 0.671, very close to the residual SD of 0.669. This confirms the stability of the model.
An effective AD treatment is expected to attenuate the increase of the ADAScog total score at month 24 in treated compared with untreated MCI subjects. We assume an effect of 0.16 on the squareroot scale of ADAScog, corresponding to an improvement of 1 point at an ADAScog score of 10, or of 2.5 points at an ADAScog score of 60. The analytically derived power with sample sizes of 286 per group and based on the estimated SD of the residuals (0.669) in each group is 82%.
Multivariate analysis
Fixed effects of the mixed model for the NeuroPsychological Battery
Fixed effects  Value  Standard error  Degrees of freedom  t value  P value 

Intercept  0.92879  0.22683  1,065  4.095  < 0.0001 
BMI2 (category 25 to 30 kg/m^{2})  0.06911  0.03812  366  1.813  0.0706 
BMI3 (category >30 kg/m^{2})  0.04498  0.05337  366  0.843  0.3999 
Visit number  0.01241  0.02184  1,065  0.568  0.5701 
Apolipoprotein E4  0.03383  0.02608  366  1.298  0.1953 
Age  0.00887  0.00292  366  3.040  0.0025 
FAQ at baseline  0.11273  0.03835  366  2.939  0.0035 
ADAScog at baseline  0.00299  0.00339  366  0.882  0.3782 
NPBatt at baseline  0.97529  0.03019  366  32.303  <0.0001 
(BMI2) × Visit number  0.02130  0.01366  1,065  1.559  0.1192 
(BMI3) × Visit number  0.05513  0.02003  1,065  2.752  0.0060 
Visit number × apolipoprotein E  0.03558  0.00932  1,065  3.818  0.0001 
Visit number × ADAScog  0.00557  0.00104  1,065  5.374  <0.0001 
Age × FAQ  0.00140  0.00051  366  2.723  0.0068 
Random effects of the mixed model for the NeuroPsychological Battery
Random effects  Lower limit  Value  Upper limit 

SD(intercept)  0.1424  0.1856  0.2419 
SD(slope)  0.0537  0.0677  0.0852 
Cor(intercept; slope)  0.2694  0.6544  0.9510 
Withingroup SD  0.2377  0.2500  0.2629 
Descriptive statistics of observed and simulated NPBatt scores for MCI at months 6, 12, 18 and 24
Minimum  First quartile  Median  Mean  Third quartile  Maximum  Standard deviation  

Observed  3.95  1.50  0.97  1.01  0.47  1.74  0.83 
Simulated  4.12  1.59  1.00  1.04  0.45  1.85  0.85 
NPBatt scores of patients without an E4 allele decreased with an average slope of 0.094. We hypothesize that an effective treatment for secondary prevention of dementia due to AD might improve this slope by 0.02 to an average slope of 0.074 per visit. Simulating treatment data with this (alternative) hypothesis and comparing them with the observed ADNI data as placebo control achieves a power of about 80% (1,000 simulations).
Supplementary analysis including cerebrospinal fluid biomarkers
Fixed effects of covariates including Aβ42/Ttau in the mixed model for NPBatt (189 patients)
Fixed effects  Value  Standard error  Degrees of freedom  t value  P value 

Intercept  0.19832  0.09375  564  2.115  0.0348 
Visit number  0.07163  0.03346  564  2.141  0.0327 
Aβ42/Ttau  0.00328  0.01535  184  0.213  0.8312 
FAQ at baseline  0.01015  0.00508  184  1.999  0.0472 
ADAScog at baseline  0.00058  0.00463  184  0.125  0.9007 
NPBatt at baseline  0.99973  0.04373  184  22.861  <0.0001 
Visit number × (Aβ42/Ttau)  0.02229  0.00548  564  4.065  0.0001 
Visit number × ADAScog  0.00398  0.00146  564  2.719  0.0067 
Random effects of the mixed model for NPBatt including Aβ42/Ttau as covariate (189 patients)
Lower limit  Value  Upper limit  

SD(intercept)  0.1236  0.1816  0.2669 
SD(slope)  0.0506  0.0698  0.0962 
Cor(intercept, slope)  0.5034  0.5598  0.9487 
Withingroup SD  0.2342  0.2512  0.2693 
Fivefold crossvalidation again confirmed the stability of the model including Aβ42/Ttau. The mean prediction error coincides with the residual standard deviation  both are 0.2187, close to the prediction error achieved from the full sample.
Discussion
The main goal of the current study is to develop mathematical models of typical disease trajectories of AD, from its presymptomatic to symptomatic stages  that is, to develop algorithms for use to quantitatively compare patients undergoing experimental treatment for secondary prevention of dementia due to AD with their own anticipated untreated disease course. We used the data for 397 MCI subjects from the ADNI database as available in October 2009. The examples presented here concern a univariate (endpointrelated) approach  that is, an algorithm that predicts the MCI subject group's performance scores on the ADAScog 24 months after their baseline examination  and a multivariate (trajectoryrelated) approach  that is, an algorithm that forecasts the decline of performance during 36 months, from baseline to the last examination after 3 years  on the composite score of a neuropsychological battery as described previously (NPBatt) [15]. Both outcomes, a cognitive performance score after 24 months and the trajectory of cognitive change over 36 months, could be of use in studies with experimental drugs for secondary prevention of dementia due to AD. A total of 11 demographic, neuropsychological and biological measures established at baseline, plus their interactions, were included as potential predictors in the univariate and multivariate analyses.
In the univariate model, the strongest predictors of the ADAScog scores as measured after 24 months were the ADAScog scores established at baseline. Other significant predictors were (in decreasing order of importance) the composite scores on the NPBatt, the MMSE scores, gender and obesity. There was also a significant interaction of the FAQ with the relationship between the NPBatt at baseline and the ADAScog scores at 24 months, suggesting that higher scores on the FAQ (indicating some functional restriction at baseline) mainly affected 24 months ADAScog scores of MCI subjects with normal NPBatt scores at baseline, but not those with lower baseline performance.
In the multivariate analysis, the strongest predictors of the NPBatt trajectory over 36 months were the NPBatt scores established at baseline. Other significant, albeit weaker, predictors are age, the FAQ scores at baseline and obesity. Of particular interest is the interaction between the number of apolipoprotein E4 alleles and time (Figure 3), indicating that the negative impact of this genetic marker upon cognitive performance was not significant at baseline but developed over time.
Taken one by one, none of these findings is novel. For the baseline cognitive measures (ADAScog, MMSE, NPBatt) predicting cognitive performance after 24 months as well as the trajectory of cognitive decline over 36 months, numerous studies show that cognitive performance established at some point in time (memory function and other cognitive domains) is a strong predictor of subsequent cognitive decline and of dementia. This was found, at different levels of performance, for older subjects who were cognitively healthy at baseline [20, 21], for MCI subjects [22] and for AD patients [6, 23]. The concept of cognitive reserve [24, 25] captures these observations in a more general hypothetical construct.
The effect of gender was significant in the univariate approach, but not in the multivariate model. While it is known from epidemiological studies that AD occurs more frequently in aged women than in men [26], this general finding does not explain the difference in the two models.
Obesity was found to have a protective effect against cognitive decline in both the univariate model and the multivariate model. Cronk and colleagues, who also worked with the ADNI database, have already reported a favorable impact of higher body mass index baseline values on the development of MMSE, ADAScog and NPBatt scores as early as 1 year after baseline [15]. Other authors also found a protective effect of obesity upon cognitive performance in persons of older age [27, 28]. Although overweight in middle age was identified as a risk factor for dementia several decades later [29, 30], this relationship appears to be reversed in persons beyond 70 years of age (the obesity paradox [28]). To what extent this paradox and the apparent protective effect of obesity are due to an underlying selection factor  for example, higher mortality in overweight people lacking a hypothetical protective factor that also supports cognitive maintenance  cannot be deducted from the current data.
There was a significant interaction between the FAQ scores and the NPBatt scores as predictors of the performance on the ADAScog at 24 months. FAQ scores at baseline were a significant determinant of cognitive decline in the multivariate analysis when NPBatt scores were still normal (that is, z = 0). As mentioned earlier, the ADNI MCI sample cannot be considered a pure selection of MCI subjects since some subjects had restrictions in activities of daily life and instrumental activities of daily life, as indicated by baseline FAQ scores >0 (Table 1), and 60% of the subjects were taking antiAD drugs (cholinesterase inhibitors and/or memantine) at some point in the study. With regard to impaired activities of daily life/instrumental activities of daily life in some subjects, it is of interest that the baseline FAQ scores turned out to be a significant and, at least in the multivariate model, independent predictor of cognitive decline over the ensuing 36 months. According to Pérès and colleagues, some decline in instrumental activities of daily life is seen in aged subjects as early as 10 years before a clinical diagnosis of dementia is made, and may thus constitute a very early marker of dementia [31]. Dickerson and colleagues found that, in mildly impaired aged individuals who did not meet strict MCI criteria as implemented in clinical trials, the degree of cognitive impairment in daily life and performance on neuropsychological testing impacted the likelihood of an AD diagnosis within 5 years [32].
As for the possible effect of antiAD medication, separate analyses for MCI subjects with and without use of cholinesterase inhibitors and/or memantine at any time provided very similar predictive models. Although the patients taking any of these drugs at some time showed somewhat inferior cognitive performance at baseline  that is, higher average ADAScog scores and lower scores on the NPBatt than subjects not taking any of these drugs  the major predictors for the ADAScog scores after 24 months and the trajectory of the NPBatt over 36 months were the same for both subgroups (data not shown).
While the number of apolipoprotein E4 alleles was not a significant predictor of the ADAScog scores at 24 months in the univariate analysis, this genetic risk factor of AD showed a significant interaction with time and, consequently, a strong impact upon the trajectory of the NPBatt scores in the longitudinal analysis (Figure 3). Interestingly, the number of apolipoprotein E4 alleles did no longer significantly affect cognitive performance when the Aβ42/Ttau quotient was introduced as a potential predictor; note, however, that the analysis including the cerebrospinal fluid markers was performed with a sample only onehalf as large as that for the other calculations. The Aβ42/Ttau quotient is a known early marker of AD [19, 33] and was recently reported to be a predictor of functional decline in the ADNI MCI sample [34]. Aβ42 and subsequently tau in the cerebrospinal fluid are considered early markers in the AD pathological cascade [35].
In summary, although none of the individually significant predictors identified in our models was unexpected, the specific and weighted combination of predictors is novel in the models presented: the univariate approach predicted cognitive performance (ADAScog scores) 2 years after baseline, and the multivariate approach forecasted the decline of cognitive performance  as measured by means of the NPBatt  over 36 months. The univariate model for the square root of ADAScog explains 63% of the variance, and the prediction error of a single outcome in crossvalidation is 0.67. This is obviously not a precise estimate for single values, as the 95% interval for an estimate of ADAScog score of 20 would range roughly from 10 to 34 for an individual patient. In the application of this model to clinical studies, however, the relevant measure will be the mean of, say, 200 outcomes. The standard error of this mean would be 0.047 on the squareroot scale, and the 95% confidence interval for a mean of 20 on the original scale would be as narrow as 19.2 to 20.8. In the multivariate model for NPBatt, which has a standard normal distribution in a healthy population, the standard deviation of the conditional residuals is 0.2175. Taking the betweenpatient variability into account, the standard deviation of residuals in the population is between 0.32 and 0.51, depending on the time of observation (0 to 36 months). For a sample of 200 subjects these standard deviations are reduced to 0.023 and 0.035, respectively. Estimated means from such samples appear to be sufficiently precise for group comparisons in clinical trials.
Descriptive statistics of observed and simulated NPBatt scores for Alzheimer's disease patients at months 6, 12 and 24
Minimum  First quartile  Median  Mean  Third quartile  Maximum  Standard deviation  

Observed  4.48  2.66  1.95  2.05  1.40  0.14  0.87 
Simulated  4.85  2.65  2.00  2.08  1.45  0.10  0.89 
Assuming that our models are supported by analyses of further independent datasets, what would principally argue against their use in trials with experimental compounds aimed at secondary prevention of dementia due to AD? Evidently, the 50year tradition of placebocontrolled study designs in clinical neuropsychopharmacology argues against a new approach like the one suggested here  although several limitations of the conventional designs have repeatedly been pointed out [4, 7, 8].
To illustrate one particularly important limitation of RPCTs and its consequences, let us for a moment assume the perspective of an older person who has just learned that he or she shows the characteristics of presymptomatic AD or MCI, implying that he or she is likely to become demented within a few years, and who is offered participation in a longterm phase 3 RPCT with a promising experimental diseasecourse altering drug. Would one not expect that this individual would be uncertain as to how he or she should decide: agree to participate in a placebocontrolled  that is, a Russian roulette type of trial  or reject participation and hope for a better alternative?
In recent years, clinical investigators  notably in the United States  have reported increasing difficulties recruiting patients into AD clinical trials [36]. One cannot rule out that some of these difficulties are due to patients' unwillingness to enter trials that entail a high risk for participants of being treated with placebo for months or even years. Thus, apart from the ethical issue of exposing highrisk individuals to admittedly ineffective treatment (placebo), one should also consider that only a selfselected fraction of all trial candidates will eventually enter RPCTs, a fact that seriously jeopardizes the external validity of such trials. In spite of these concerns, current regulatory guidelines [37] and specialized task forces [38, 39] keep recommending or even demand RPCTs as proof of efficacy for drugs intended for use in AD, including compounds aimed at secondary prevention of dementia due to AD that require very long studies to prove efficacy. This insistence is surprising, given that placebocontrolled designs were originally developed for clinical studies of analgesics, antidepressants and anxiolytics  that is, for trials in mostly selflimiting, unstable and partly subjective central nervous system indications that differ in important aspects from slowly developing, irreversible, degenerative disorders such as AD.
If supported by further evidence, where in the clinical development process of a new compound aimed at secondary prevention of dementia due to AD could be the place for the proposed PGSA? No doubt some of the earlier (phase 1 and phase 2) trials, which are often performed on healthy subjects and subsequently on AD patients at different levels of deterioration, do require placebo control, notably in order to detect and characterize any relevant safety issue of the new compound. As these earlier studies last only a few months for each patient, and since little is known early in development about a new drug's potentially useful effect in man, there is no ethical concern to use placebo at this stage. Once the proofofprinciple and placebocontrolled safety studies are completed, however, and presumably effective and safe doses of the novel drug need to be tested for longterm efficacy in the target population  that is, in subjects in presymptomatic stages of AD  then a placebofree approach such as the PGSA should be seriously considered. In addition to its ethical and scientific merits, it also has the potential to save patients, time and money. The next years will show whether the AD research community [3, 7, 40, 41] and drug regulatory bodies are ready and willing to deemphasize a traditional study paradigm that has serious shortcomings, and are willing to consider a design that has the potential both to benefit the patients and facilitate antiAD drug development.
Conclusions
First predictive univariate (endpointrelated) and multivariate (trajectory related) models based on anamnestic, clinical, biological and neuropsychological data from the ADNI database show high correspondence of predicted and real observed values. Corroboration of these models with data from other studies is ongoing. It is hoped that the PGSA, which comprises comparisons between real, observed data of patients on experimental treatment with their own, modelbased forecasted trajectories, will be considered for late phase 2 or phase 3 longterm trials with drugs intended for secondary prevention of dementia due to AD.
Abbreviations
 Aβ42:

amyloid β42
 AD:

Alzheimer's disease
 ADAScog:

Alzheimer Disease Assessment Scale  cognitive subscale
 ADNI:

Alzheimer Disease Neuroimaging Initiative
 FAQ:

Functional Assessment Questionnaire
 MCI:

mild cognitive impairment
 MMSE:

MiniMental Status Examination
 NPBatt:

NeuroPsychological Battery
 PGSA:

placebo group simulation approach
 RPCT:

randomized placebocontrolled trial
 SD:

standard deviation
 Ttau:

total tau protein.
Declarations
Acknowledgements
Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, BristolMyers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. HoffmanLa Roche, ScheringPlough, Synarc, Inc., as well as nonprofit partners the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation, with participation from the US Food and Drug Administration. Privatesector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.
Lon Schneider MD (University of Southern California University Hospital) and Ivo Spiegel PhD (Harvard University Medical Center) provided useful critical comments to the PGSA and earlier versions of the manuscript.
Data used in preparation of this article were obtained from the ADNI database. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators is available online [42].
Authors’ Affiliations
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