2. Challenges in data acquisition and analysis

#### 2.1. Challenges in recruiting participants for dementia studies

Longitudinal studies are better at establishing causal directions than are cross-sectional studies. However it is not easy to recruit MCI participants, especially for a longitudinal dementia study [5]. Factors affecting eligibility for enrolment include lack of awareness of the trial, lack of benefits to the participant, stringent enrolment criteria which may exclude many people, older age of study volunteers, co-morbidity factors, disability, lack of mobility, requiring the cooperation of a partner or carer, transportation, administration of medication, too many tests, and intensive monitoring of the individual's condition and progress. In general, dementia trials usually take at least 5–6 years to discover whether a drug works or not, due to slow enrolment [6, 7]. Ramifications of this include slow development of potential new treatment, increased costs associated with clinical trials, and impact on the reliability of trial results due to changes which include scanners, investigators, personnel, and economic cycles.

In order to improve internal validity, studies may seek to make recruitment criteria more stringent so as to reduce the heterogeneity typically seen in a memory clinic. Yet in order for studies to be more relevant to clinicians, they also need to be anchored clinically, which means recruitment criteria cannot be too tough for participants to be enrolled. One way to increase the number of volunteers is to simplify recruitment enrolment criteria and screening processes. By being less stringent on suitable subjects for recruitment, more can be eligible for enrolment which helps to encourage referrals from clinicians.

#### 2.2. Leveraging data sets

The support for small studies with less statistical and mathematical rigour to detect or demonstrate a response may be just as important as large randomised controlled trials to validate a response. Justifying resources to be spent on designing and running a study first requires more than just a good idea, but also supporting data from smaller studies, as well as available timeframe and interest. While big studies are often desirable for improving validity, relatively smaller longitudinal studies may be no less significant in exposing a scientific law, if data was collected and analysed the right way. We should remember that the modern science of genetics was founded on cross breeding yellow and green peas and their offsprings, at a time when many competing theories were making headway.

In any diagnostic entity, there is increased heterogeneity the earlier it is addressed, and so mild cognitive impairment (MCI) is a challenging population to study due to the heterogenous phenotypes, etiologies and prognosis, both cross-sectionally and longitudinally. Furthermore, similar symptoms can often be attributed to multiple different causes, each to varying degrees. Although there is a good amount of consistency between MCI studies themselves, increased heterogeneity in the actual early disease states does result in differences in outcome between MCI studies. The new research criteria for MCI due to Alzheimer's disease (AD) is an attempt to eventually move beyond highlighting MCI as a major risk factor for AD to operationalizing

This chapter considers the methodological issues, challenges and assumptions that need to be

Longitudinal studies are better at establishing causal directions than are cross-sectional studies. However it is not easy to recruit MCI participants, especially for a longitudinal dementia study [5]. Factors affecting eligibility for enrolment include lack of awareness of the trial, lack of benefits to the participant, stringent enrolment criteria which may exclude many people, older age of study volunteers, co-morbidity factors, disability, lack of mobility, requiring the cooperation of a partner or carer, transportation, administration of medication, too many tests, and intensive monitoring of the individual's condition and progress. In general, dementia trials usually take at least 5–6 years to discover whether a drug works or not, due to slow enrolment [6, 7]. Ramifications of this include slow development of potential new treatment, increased costs associated with clinical trials, and impact on the reliability of trial results due to

In order to improve internal validity, studies may seek to make recruitment criteria more stringent so as to reduce the heterogeneity typically seen in a memory clinic. Yet in order for studies to be more relevant to clinicians, they also need to be anchored clinically, which means recruitment criteria cannot be too tough for participants to be enrolled. One way to increase the number of volunteers is to simplify recruitment enrolment criteria and screening processes. By being less stringent on suitable subjects for recruitment, more can be eligible for enrolment

The support for small studies with less statistical and mathematical rigour to detect or demonstrate a response may be just as important as large randomised controlled trials to validate a response. Justifying resources to be spent on designing and running a study first requires more than just a good idea, but also supporting data from smaller studies, as well as available timeframe and interest. While big studies are often desirable for improving validity, relatively smaller longitudinal studies may be no less significant in exposing a scientific law, if data was

the prognostication of cognitive impairment in clinical settings.

110 Alzheimer's Disease - The 21st Century Challenge

2. Challenges in data acquisition and analysis

which helps to encourage referrals from clinicians.

2.2. Leveraging data sets

2.1. Challenges in recruiting participants for dementia studies

taken into consideration when evaluating dementia and biomarker studies.

changes which include scanners, investigators, personnel, and economic cycles.

Research efforts are moving towards early identification of high risk subjects and prevention of progression. In the preclinical space, there is not yet a lot of longitudinal biomarker data. Longitudinal data provides important knowledge of biomarkers in predicting and monitoring cognitive and functional decline. To make the most of the limited data, use of both familiar as well as more sophisticated statistical techniques is required. There is a need for equations and formulas that can embrace heterogeneity without being too complex.

The Cox regression survival analysis is one statistical approach that can distill the heterogeneity of MCI aetiologies to determine independent risk factors for MCI conversion to AD. Cox regression is a survival analysis statistical technique that enables the simultaneous comparison and adjustment of the effects of several risk factors (i.e. the predictor variables or covariates) of an unwanted event occurring. It can also accommodate covariates that are dichotomous, continuous, and even if they might change in value. The required inputs are: time to an unwanted event of interest, the unwanted event of interest, and the predictor variables. The result is expressed as hazard ratios, which is the proportion of an unwanted event of interest between groups at an instantaneous moment in time. According to the Cox regression model, the hazard for an individual is a fixed hazard for any other individual. By inputting all known variables (risk factors) in a study cohort into the Cox model, we can adjust for all of them simultaneously.

#### 2.3. Source of subjects, where and when the study was conducted

The source of subjects is a significant point that affects rates of conversion to AD [8]. People seeking specialist care for memory loss are more selected compared with people in the community who happen to have some memory problems [9]. Different studies have different aims and designs, and different methods to operationalize criteria [7]. Cognitive complains can be spontaneous, yet not routinely elicited in some cases; and clinical assessments can be standardised in some cases but based on more subjective clinical judgement in others.

Recruitment sites are an important consideration in designing studies. Cohorts at different sites are demographically different in some ways, so academic sites perform differently from commercial sites. Some cohorts like the Australian Imaging Biomarkers and Lifestyle healthy control cohort are Apolipoprotein E ε4 (E4) enriched [10]. The Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort consists of 398 MCI subjects, who were mostly white and highly educated, had intermediate cognitive measures and cerebral spinal fluid (CSF) biomarker levels between the ADNI controls and AD groups [11], and there was also a high proportion of E4 carriers.

MCI cohorts recruited today may not be entirely relevant to tomorrow's world. Secular changes influence the predictive value of cognitive performance in dementia. For example, in the Flynn Effect [12], massive gains in IQ of Americans were observed between 1932 and 1978. Humanity seems to gain skills that make IQ tests outdated. Lifestyle technology development like software apps may further leverage our function and so delay residential care.

#### 2.4. Challenges in comparing data sets

Retrofitting criteria and statistical models developed from experience with one cohort to another that has different demographic characteristics will end up with varying outcomes, not to mention the different combinations of measurements, cut-offs, number of subjects, and length of follow-up between samples that will further compound the variability of results [13–17].

A joint modelling approach can potentially reduce the bias which attenuates the effect of neuropathology on cognitive decline. This bias occurs if non-random drop outs are excluded

Challenges in Dementia Studies

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http://dx.doi.org/10.5772/intechopen.72866

The dementia field is filled with many contradictory ideas and controversies. Accuracy of dementia diagnoses has been an unresolved challenge. For example, in the religious orders study involving over 1000 nuns, the majority of cases particularly in those over 85 have AD pathology as well as several other pathologies [25]. Of the phenotypes that look like clinically probable AD, some had Lewy bodies or other predominant neurodegenerative disor-

Diagnosing during the pre-dementia stages is challenged by fluctuations in cognitive ability over long periods of time [26]. In short term MCI studies, outcomes are rather volatile, such that one can revert to normal, remain MCI with improvement or deterioration in cognitive abilities, convert to dementia, improve after deteriorating further, or deteriorate again after improving. For example, in the Rochester Minnesota longitudinal study, as high as 35% of MCI reverted to normal when followed long enough [27]. However two-thirds of these ultimately progressed again to MCI or dementia. In the Pittsburgh longitudinal health study after over a decade of follow-up, a small percent return to normal after being diagnosed with

One way to account for the observed volatility is the rigid way disease and states are categorised. By taking a disease continuum and subjecting it to arbitrary boundaries, patients are likely to bounce in and out of them. Another cause of volatility is the random fluctuation of cognitive test scores up to half a standard deviation. Someone vulnerable near the cut-off could be having a good day and so their scores may be considered to be within the normal range, or having a bad day and so their scores may be considered to be within the MCI range. This variability of performance from day to day is not a trivial matter because it predicts future decline over and beyond cognitive performance [29]. Consecutive clinical information should be taken more

The entire trajectory of cognitive decline in one at risk of AD is not necessarily due solely to AD. To date only up to half of cognitive decline can be accounted for by neuropathology seen on autopsies of brains, e.g. AD, micro and macro infarcts, Lewy bodies, TDP-43, pre-synaptic proteins, and neuronal density and locus [30]. Pathology may trigger events or formation of other pathologies, thus causing people's brains to differ in how they respond to the predominant neurodegenerative pathology. For example, mixed AD with Lewy Bodies will have more

from analyses, or if the last observation carried forward method is used.

3. Diagnostic challenges

3.2. Volatility of clinical outcomes

seriously as it may discount initial diagnoses.

variability in their cognition due to attention impairment [31].

3.1. Accuracy of diagnosis

ders at autopsy.

MCI [28].

Validity is gained when results are repeatable. Power is gained when shared data is combined. Sometimes data sets are easily comparable. For example, the ability of 3.0-Tesla (T) and 1.5-T scanners to track longitudinal atrophy in AD and MCI patients using tensor based morphometry are both similar and powerful enough to detect atrophy longitudinally [18], so it may not matter much that one cohort had their magnetic resonance imaging (MRI) on a 1.5-T scanner and another cohort had their MRI on a 3.0-T scanner. However in dementia studies, combining data sets is not a trivial issue. Comparing results from different studies that have used different methodologies is rather difficult. Combining data from different scanners introduces noise. Different positron emission tomography (PET) or MRI scanners have different scanner and software combinations. Inter scanner variability is excluded if all cross-sectional and longitudinal scans are performed on the same scanner—but this is not practical.

Lack of standardisation threatens to hamper the comparison and replication of results, increase analytical variability, and complicate the evaluation of methods [7]. Different methods of biomarker analyses give varying degrees of precision [19]. Drop outs or missing data are dealt with differently. Time lag between receiving a clinical diagnosis of subjective cognitive impairment (SCI) or MCI and enrolment differs between studies. If the time lag between diagnosis and recruitment is long, this might make one SCI or MCI cohort have more stable subjects, and so less likely to progress to a dementia subtype. Different population norms are used for neuropsychological tests, and different batteries of neuropsychological tests are used.

Given that the stability of cognition can be affected by many factors in the short term, it is important to consider what variables are corrected for when we read published studies. As mentioned above, a down side to robustly designed studies which are generally informative as they control for many factors, is that they may not simulate routine clinical practice well.

#### 2.5. Drop outs and their risk factors

Drop outs in research studies due to relocation and loss of interest should be classified as random dropouts. However drop outs from MCI studies are not entirely random [20]. Traditional survival analysis assumes censored observations are non-informative and ignorable [21]. Yet death alters the probability of observing dementia.

Risk factors for cognitive and functional impairments in MCI can also be risk factors for dropping out early from MCI studies causing potential bias in the sample. For example, E4 is a risk factor for progression from a clinical dementia rating (CDR) of 0.5 to a CDR of 1 and above and a risk factor for cardiovascular mortality [22]. Heart failure is a risk factor for progression from mild cognitive to severe cognitive impairment, and for functional decline [23]. Stroke is a risk factor for non-amnestic cognitive and functional decline [24].

A joint modelling approach can potentially reduce the bias which attenuates the effect of neuropathology on cognitive decline. This bias occurs if non-random drop outs are excluded from analyses, or if the last observation carried forward method is used.
