We have previously derived power calculation formulas for cohort studies and clinical trials using the longitudinal mixed effects model with random slopes and intercepts to compare rate of change across groups [Ard & Edland, Power calculations for clinical trials in Alzheimer's disease. J Alzheim Dis 2011;21:369-77]. We here generalize these power formulas to accommodate 1) missing data due to study subject attrition common to longitudinal studies, 2) unequal sample size across groups, and 3) unequal variance parameters across groups. We demonstrate how these formulas can be used to power a future study even when the design of available pilot study data (i.e., number and interval between longitudinal observations) does not match the design of the planned future study. We demonstrate how differences in variance parameters across groups, typically overlooked in power calculations, can have a dramatic effect on statistical power. This is especially relevant to clinical trials, where changes over time in the treatment arm reflect background variability in progression observed in the placebo control arm plus variability in response to treatment, meaning that power calculations based only on the placebo arm covariance structure may be anticonservative. These more general power formulas are a useful resource for understanding the relative influence of these multiple factors on the efficiency of cohort studies and clinical trials, and for designing future trials under the random slopes and intercepts model.

Background

Composite scales have recently been proposed as outcome measures for clinical trials. For example, the Prodromal Alzheimer's Cognitive Composite (PACC) is the sum of z-score normed component measures assessing episodic memory, timed executive function, and global cognition. Alternative methods of calculating composite total scores using the weighted sum of the component measures that maximize signal-to-noise of the resulting composite score have been proposed. Optimal weights can be estimated from pilot data, but it is an open question how large a pilot trial is required to calculate reliably optimal weights.

Methods

In this manuscript, we describe the calculation of optimal weights, and use large-scale computer simulations to investigate the question of how large a pilot study sample is required to inform the calculation of optimal weights. The simulations are informed by the pattern of decline observed in cognitively normal subjects enrolled in the Alzheimer's Disease Cooperative Study (ADCS) Prevention Instrument cohort study, restricting to n=75 subjects age 75 and over with an ApoE E4 risk allele and therefore likely to have an underlying Alzheimer neurodegenerative process.

Results

In the context of secondary prevention trials in Alzheimer's disease, and using the components of the PACC, we found that pilot studies as small as 100 are sufficient to meaningfully inform weighting parameters. Regardless of the pilot study sample size used to inform weights, the optimally weighted PACC consistently outperformed the standard PACC in terms of statistical power to detect treatment effects in a clinical trial. Pilot studies of size 300 produced weights that achieved near-optimal statistical power, and reduced required sample size relative to the standard PACC by more than half.

Conclusions

These simulations suggest that modestly sized pilot studies, comparable to that of a phase 2 clinical trial, are sufficient to inform the construction of composite outcome measures. Although these findings apply only to the PACC in the context of prodromal AD, the observation that weights only have to approximate the optimal weights to achieve near-optimal performance should generalize. Performing a pilot study or phase 2 trial to inform the weighting of proposed composite outcome measures is highly cost-effective. The net effect of more efficient outcome measures is that smaller trials will be required to test novel treatments. Alternatively, second generation trials can use prior clinical trial data to inform weighting, so that greater efficiency can be achieved as we move forward.

Hippocampal sclerosis (HS) is a prevalent cause of dementia in the oldest old but is generally misdiagnosed as Alzheimer's disease (AD) due to similarities in clinical presentation. To determine if clinical and cognitive features diverge over time, we compared results from longitudinal evaluations of participants in the University of California, San Diego, Alzheimer's Disease Research Center with autopsy-confirmed AD (n = 195), HS (n = 21), or both HS + AD (n = 18). Each group exhibited decline in all cognitive measures, with HS declining at a slower rate than AD on the Mini-Mental State Examination, immediate recall condition of a word-list learning test, and Dementia Rating Scale total and subtest scores (except memory). Five years before the final evaluation, more prominent semantic and visuospatial deficits were apparent in AD participants than in HS participants despite comparable global cognitive impairment. Groups did not differ on any measure of executive function. HS + AD differed from AD only on the Boston Naming Test. Overall, results suggest that HS dementia is associated with cognitive deficits that progress more slowly than, but generally mimic, those observed in AD.

Objective

To examine whether domain-specific patterns of cognitive impairment and trajectories of decline differed in patients with clinically diagnosed Parkinson disease dementia (PDD) (N = 29) and autopsy-confirmed dementia with Lewy bodies (DLB) (N = 58) or Alzheimer disease (AD) (N = 174) and to determine the impact of pooling patients with PDD and DLB in clinical trials targeting cognition.

Methods

Patients were matched on demographics and level of global cognitive impairment. Patterns of cross-sectional performance and longitudinal decline were examined in 4 cognitive domains: Visuospatial, Memory, Executive, and Language. Power analyses were performed to determine the numbers of participants needed to adequately power a hypothetical clinical trial to slow cognitive decline in pure PDD, pure DLB, or a mixed PDD/DLB group.

Results

Both DLB and PDD were more impaired and declined more rapidly than AD in the Visuospatial domain. Patients with PDD exhibited the most impairment and fastest decline in Executive, although patients with DLB also declined faster than AD. Memory was more impaired in AD than DLB and in both compared with PDD; however, all 3 groups declined at comparable rates. In contrast, PDD declined at a slower rate on Language measures than DLB or AD. Power analyses suggest that Visuospatial and Executive outcome measures would be most sensitive in PDD, but Memory and Language in DLB.

Conclusion

DLB and PDD differ from each other, and from AD, in a cognitive domain-specific manner. As such, different outcome measures may be most sensitive to detecting changes in DLB vs PDD, suggesting that the 2 should be analyzed separately in clinical trials.

Background

Tobacco smoking and related health problems are still major public health concerns in the United States despite the declining smoking prevalence.

Objectives

This study explored differences in smoking prevalence between urban and rural areas potentially relevant to tobacco control efforts in California.

Methods

Public use adult smoking data from the California Health Interview Survey (CHIS) between 2001 and 2011-2012 were analyzed. A total of 282 931 adults were surveyed across the six CHIS cycles. A ZIP code-based geographic classification (Urban, Second-City, Suburban, and Town/Rural) was used to examine the association between smoking prevalence and area of residency.

Results

The overall smoking prevalence in California decreased from 17.0% in 2001 to 13.8% in 2011-2012. Within each CHIS cycle, the Town/Rural areas had the highest smoking prevalence, followed by Urban and Second-City areas, and Suburban areas had the lowest. Pooled data from all CHIS cycles showed a similar pattern, with rates in Urban, Second-City, Suburban and Town/Rural areas being 15.2%, 15.2%, 13.1% and 17.3%, respectively. Weighted multivariate logistic regression analysis indicated significantly higher odds of smoking in Urban, Second-City and Town/Rural areas compared to Suburban areas (all adjusted odds ratios > 1.10), although this trend varied by race/ethnicity, being present in non-Hispanic Whites and not present in Hispanics.

Conclusions

Town/Rural and Urban populations of California are consistently at higher risk of smoking than Suburban populations. These results indicate a need for population-specific tobacco control approaches that address the lifestyle, behavior, and education of disparate populations within the same state or region.

Introduction

Cognitive composite scales constructed by combining existing neuropsychometric tests are seeing wide application as endpoints for clinical trials and cohort studies of Alzheimer's disease (AD) predementia conditions. Preclinical Alzheimer's Cognitive Composite (PACC) scales are composite scores calculated as the sum of the component test scores weighted by the reciprocal of their standard deviations at the baseline visit. Reciprocal standard deviation is an arbitrary weighting in this context, and may be an inefficient utilization of the data contained in the component measures. Mathematically derived optimal composite weighting is a promising alternative.

Methods

Sample size projections using standard power calculation formulas were used to describe the relative performance of component measures and their composites when used as endpoints for clinical trials. Power calculations were informed by (n=1,333) amnestic mild cognitive impaired participants in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set.

Results

A composite constructed using PACC reciprocal standard deviation weighting was both less sensitive to change than one of its component measures and less sensitive to change than its optimally weighted counterpart. In standard sample size calculations informed by NACC data, a clinical trial using the PACC weighting would require 38% more subjects than a composite calculated using optimal weighting.

Discussion

These findings illustrate how reciprocal standard deviation weighting can result in inefficient cognitive composites, and underscore the importance of component weights to the performance of composite scales. In the future, optimal weighting parameters informed by accumulating clinical trial data may improve the efficiency of clinical trials in AD.

Objective

Little is known about the relationship between cigarette smoking initiation and subsequent alcohol involvement. To address this question, the present study compared alcohol use between students who initiated smoking during college and a matched sample of never-smoking students. We hypothesized greater increases in alcohol involvement among smoking initiators, mediated by exposure to cigarette use situations.

Method

Included in the present study were 104 Chinese American and Korean American undergraduates who at baseline (freshman year) reported never having smoked a cigarette. Subjects were drawn from 433 participants in a naturalistic longitudinal study of tobacco use who were assessed annually each year in college. Cigarette smoking status was assessed annually as part of a structured interview. Initiators and never-smokers were matched on gender, ethnicity, baseline alcohol use, parental smoking status, and behavioral undercontrol.

Results

As predicted, participants who initiated smoking during college reported significantly greater increases in the number of past-30-day total drinks consumed (p < .001) and reported greater prevalence of heavy drinking episodes (p < .05). The effect of smoking initiation on the change in the number of past-30-day drinks at the final assessment was partially mediated by exposure to smoking (p < .05). Exploratory analyses indicated that greater recent smoking significantly predicted increased alcohol consumption over and above the effect of exposure.

Conclusions

Students who initiate smoking during college appear at risk for increased alcohol involvement. Part of this risk is explained by environmental contextual factors, specifically exposure to situations involving other smokers that also may result in greater exposure to alcohol use.

Objective

To characterize age-related clinical heterogeneity in Alzheimer disease (AD) and determine whether it is modified by APOE genotype or concomitant non-AD pathology, we analyzed data from 1,750 patients with sporadic, pathologically confirmed severe AD.

Methods

In this retrospective cohort study, regression and mixed effects models assessed effects of estimated age at onset, APOE genotype, and their interaction on standardized clinical, cognitive, and pathologic outcome measures from the National Alzheimer's Coordinating Center database.

Results

A bimodal distribution of age at onset frequency in APOE ε4- cases showed best separation at age 63. Using this age cutoff, cases were grouped as ε4- early-onset AD (EOAD) (n = 169), ε4+ EOAD (n = 273), ε4- late-onset AD (LOAD) (n = 511), and ε4+ LOAD (n = 797). Patients with EOAD were more likely than patients with LOAD to present with noncognitive behavioral or motor symptoms or nonmemory cognitive complaints, and had more executive dysfunction, but less language impairment on objective cognitive testing. Age at onset and ε4- genotype were independently associated with lower baseline Mini-Mental State Examination scores and greater functional impairment and patients with EOAD had faster cognitive and functional decline than patients with LOAD regardless of APOE genotype. Patients with EOAD were more likely than patients with LOAD to receive a non-AD clinical diagnosis even though they were more likely to have pure AD without concomitant vascular or other non-AD neurodegenerative pathology.

Conclusions

Early-onset sporadic AD is associated with a greater likelihood of an atypical, non-memory-dominant clinical presentation, especially in the absence of the APOE ε4 allele, which may lead to misattribution to non-AD underlying pathology.

Introduction

The goal was to compare subgroups of dementia with Lewy Bodies (DLB) using neuropathological measures to differentiate 'pure' Lewy body (LB) dementia from 'mixed' DLB [co-occurring LB and Alzheimer's disease (AD) pathology] to facilitate diagnostic decision-making and future development of interventions based on predicted type(s) of neuropathology. Studies comparing these groups are rare relative to those differentiating 'pure' AD and all-cause DLB, and are limited by insufficient sample size, brief cognitive batteries, and/or absence of autopsy confirmation. To address these limitations, we assessed cognition and other features in a large, autopsy-confirmed DLB sample using an extensive neuropsychological battery.

Methods

Subjects from an AD research center autopsy series satisfying DLB pathology criteria were divided by an AD neuropathology index into DLB-LB (Braak stage 0-3) (n = 38) and DLB-AD (Braak stage 4-6) (n = 41) and compared on baseline variables from chart reviews and standardized measures.

Results

DLB-LB subjects were more impaired on visuospatial constructions, visual conceptual reasoning, and speed of processing, but less impaired on verbal memory and confrontation naming. All-type hallucinations occurred more frequently in DLB-LB, while delusions were common in both groups. Groups were similar in education and age at onset, and in baseline age, dementia severity, and functional capacity.

Conclusion

Salient findings included greater impairment on visual tasks and speed of processing and more frequent reports of all-type hallucinations in DLB-LB compared to DLB-AD. Relatively intact confrontation naming in DLB-LB and no differences in reported delusions were of note. Identifying differences in phenotypic features can improve prediction of underlying neuropathology.