Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: baseline adjustment.
Ontology highlight
ABSTRACT:
SUBMITTER: Schott JM
PROVIDER: S-EPMC2947486 | biostudies-literature | 2010 Aug
REPOSITORIES: biostudies-literature
Publications
Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: baseline adjustment.
Schott J M JM Bartlett J W JW Barnes J J Leung K K KK Ourselin S S Fox N C NC
Neurobiology of aging 20100801 8
Cerebral atrophy rate is increasingly used as an outcome measure for Alzheimer's disease (AD) trials. We used the Alzheimer's disease Neuroimaging initiative (ADNI) dataset to assess if adjusting for baseline characteristics can reduce sample sizes. Controls (n = 199), patients with mild cognitive impairment (MCI) (n = 334) and AD (n = 144) had two MRI scans, 1-year apart; approximately 55% had baseline CSF tau, p-tau, and Abeta1-42. Whole brain (KN-BSI) and hippocampal (HMAPS-HBSI) atrophy rate ...[more]
Similar Datasets
Project description:BackgroundProgressive brain atrophy in multiple sclerosis (MS) may reflect neuroaxonal and myelin loss and MRI measures of brain tissue loss are used as outcome measures in MS treatment trials. This study investigated sample sizes required to demonstrate reduction of brain atrophy using three outcome measures in a parallel group, placebo-controlled trial for secondary progressive MS (SPMS).MethodsData were taken from a cohort of 43 patients with SPMS who had been followed up with 6-monthly T1-weighted MRI for up to 3 years within the placebo arm of a therapeutic trial. Central cerebral volumes (CCVs) were measured using a semiautomated segmentation approach, and brain volume normalized for skull size (NBV) was measured using automated segmentation (SIENAX). Change in CCV and NBV was measured by subtraction of baseline from serial CCV and SIENAX images; in addition, percentage brain volume change relative to baseline was measured directly using a registration-based method (SIENA). Sample sizes for given treatment effects and power were calculated for standard analyses using parameters estimated from the sample.ResultsFor a 2-year trial duration, minimum sample sizes per arm required to detect a 50% treatment effect at 80% power were 32 for SIENA, 69 for CCV, and 273 for SIENAX. Two-year minimum sample sizes were smaller than 1-year by 71% for SIENAX, 55% for CCV, and 44% for SIENA.ConclusionSIENA and central cerebral volume are feasible outcome measures for inclusion in placebo-controlled trials in secondary progressive multiple sclerosis.
| S-EPMC2818185 | biostudies-literature
Project description:This study modeled predementia Alzheimer's disease clinical trials. Longitudinal data from cognitively normal (CN) and mild cognitive impairment (MCI) participants in the Alzheimer's Disease Neuroimaging Initiative were used to calculate sample size requirements for trials using outcome measures, including the Clinical Dementia Rating scale sum of boxes, Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-cognitive subscale with and without delayed recall, and the Rey Auditory Verbal Learning Task. We examined the impact on sample sizes of enrichment for genetic and biomarker criteria, including cerebrospinal fluid protein and neuroimaging analyses. We observed little cognitive decline in the CN population at 36 months, regardless of the enrichment strategy. Nonetheless, in CN subjects, using Rey Auditory Verbal Learning Task total as an outcome at 36 months required the fewest subjects across enrichment strategies, with apolipoprotein E genotype ?4 carrier status requiring the fewest (n = 499 per arm to demonstrate a 25% reduction in disease progression). In MCI, enrichment reduced the required sample sizes for trials, relative to estimates based on all subjects. For MCI, the Clinical Dementia Rating scale sum of boxes consistently required the smallest sample sizes. We conclude that predementia clinical trial conduct in Alzheimer's disease is enhanced by the use of biomarker inclusion criteria.
| S-EPMC3412892 | biostudies-literature
Project description:BackgroundDetecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer's disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials.MethodsBaseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org ). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only.ResultsAlthough highly correlated to DVR (ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm (n = 855) compared with DVR (n = 1508) and the early composite also provided considerable sample size reductions (n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR).ConclusionSample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development.
| S-EPMC8056524 | biostudies-literature
Project description:ObjectiveThe identification of sensitive biomarkers is essential to validate therapeutics for Huntington disease (HD). We directly compare structural imaging markers across the largest collective imaging HD dataset to identify a set of imaging markers robust to multicenter variation and to derive upper estimates on sample sizes for clinical trials in HD.MethodsWe used 1 postprocessing pipeline to retrospectively analyze T1-weighted magnetic resonance imaging (MRI) scans from 624 participants at 3 time points, from the PREDICT-HD, TRACK-HD, and IMAGE-HD studies. We used mixed effects models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possible treatment effects in disease-affected anatomical regions. We used our model to estimate the statistical power of possible treatment effects for anatomical regions and clinical markers.ResultsWe identified a set of common anatomical regions that have similarly large standardized effect sizes (>0.5) between healthy control and premanifest HD (PreHD) groups. These included subcortical, white matter, and cortical regions and nonventricular cerebrospinal fluid (CSF). We also observed a consistent spatial distribution of effect size by region across the whole brain. We found that multicenter studies were necessary to capture treatment effect variance; for a 20% treatment effect, power of >80% was achieved for the caudate (n = 661), pallidum (n = 687), and nonventricular CSF (n = 939), and, crucially, these imaging markers provided greater power than standard clinical markers.InterpretationOur findings provide the first cross-study validation of structural imaging markers in HD, supporting the use of these measurements as endpoints for both observational studies and clinical trials. ANN NEUROL 2020;87:751-762.
| S-EPMC7187160 | biostudies-literature
Project description:BackgroundAdjustment for baseline prognostic factors in randomized clinical trials is usually performed by means of sample-based regression models. Sample-based models may be incorrect due to overfitting. To assess whether overfitting is a problem in practice, we used simulated data to examine the performance of the sample-based model in comparison to a "true" adjustment model, in terms of estimation of the treatment effect.MethodsWe conducted a simulation study using samples drawn from a "population" in which both the treatment effect and the effect of the potential confounder were specified. The outcome variable was binary. Using logistic regression, we compared three estimates of the treatment effect in each situation: unadjusted, adjusted for the confounder using the sample, adjusted for the confounder using the true effect. Experimental factors were sample size (from 2 × 50 to 2 × 1000), treatment effect (logit of 0, 0.5, or 1.0), confounder type (continuous or binary), and confounder effect (logit of 0, - 0.5, or - 1.0). The assessment criteria for the estimated treatment effect were bias, variance, precision (proportion of estimates within 0.1 logit units), type 1 error, and power.ResultsSample-based adjustment models yielded more biased estimates of the treatment effect than adjustment models that used the true confounder effect but had similar variance, accuracy, power, and type 1 error rates. The simulation also confirmed the conservative bias of unadjusted analyses due to the non-collapsibility of the odds ratio, the smaller variance of unadjusted estimates, and the bias of the odds ratio away from the null hypothesis in small datasets.ConclusionsSample-based adjustment yields similar results to exact adjustment in estimating the treatment effect. Sample-based adjustment is preferable to no adjustment.
| S-EPMC9924183 | biostudies-literature
Project description:Traditionally in acute stroke clinical trials, the primary clinical outcome employed is a dichotomized modified Rankin Scale (mRS). New statistical methods, such as responder analysis, are being used in stroke studies to address the concern that baseline prognostic variables, such as stroke severity, impact the likelihood of a successful outcome. Responder analysis allows the definition of success to vary according to baseline prognostic variables, producing a more clinically relevant insight into the actual effect of investigational treatments. It is unclear whether or not statistical analyses should adjust for prognostic variables when responder analysis is used, as the outcome already takes these prognostic variables into account. This research aims to investigate the effect of covariate adjustment in the responder analysis framework in order to determine the appropriate analytic method.Using a current stroke clinical trial and its pilot studies to guide simulation parameters, 1,000 clinical trials were simulated at varying sample sizes under several treatment effects to assess power and type I error. Covariate-adjusted and unadjusted logistic regressions were used to estimate the treatment effect under each scenario. In the case of covariate-adjusted logistic regression, the trichotomized National Institute of Health Stroke Scale (NIHSS) was used in adjustment.Under various treatment effect settings, the operating characteristics of the unadjusted and adjusted analyses do not substantially differ. Power and type I error are preserved for both the unadjusted and adjusted analyses.Our results suggest that, under the given treatment effect scenarios, the decision whether or not to adjust for baseline severity when using a responder analysis outcome should be guided by the needs of the study, as type I error rates and power do not appear to vary largely between the methods. These findings are applicable to stroke trials which use the mRS for the primary outcome, but also provide a broader insight into the analysis of binary outcomes that are defined based on baseline prognostic variables.This research is part of the Stroke Hyperglycemia Insulin Network Effort (SHINE) trial, Identification Number NCT01369069.
| S-EPMC3821551 | biostudies-literature
Project description:5xFAD transgenic (TG) mice are used widely in AD preclinical trials; however, data on sample sizes are largely unaddressed. We therefore performed estimates of sample sizes and effect sizes for typical behavioural and neuropathological outcome measures in TG 5xFAD mice, based upon data from single-sex (female) groups. Group-size estimates to detect normalisation of TG body weight to WT littermate levels at 5.5m of age were N = 9-15 depending upon algorithm. However, by 1 year of age, group sizes were small (N = 1 -<6), likely reflecting the large difference between genotypes at this age. To detect normalisation of TG open-field hyperactivity to WT levels at 13-14m, group sizes were also small (N = 6-8). Cued learning in the Morris water maze (MWM) was normal in Young TG mice (5m of age). Mild deficits were noted during MWM spatial learning and memory. MWM reversal learning and memory revealed greater impairment, and groups of up to 22 TG mice were estimated to detect normalisation to WT performance. In contrast, Aged TG mice (tested between 13 and 14m) failed to complete the visual learning (non-spatial) phase of MWM learning, likely due to a failure to recognise the platform as an escape. Estimates of group size to detect normalisation of this severe impairment were small (N = 6-9, depending upon algorithm). Other cognitive tests including spontaneous and forced alternation and novel-object recognition either failed to reveal deficits in TG mice or deficits were negligible. For neuropathological outcomes, plaque load, astrocytosis and microgliosis in frontal cortex and hippocampus were quantified in TG mice aged 2m, 4m and 6m. Sample-size estimates were ≤9 to detect the equivalent of a reduction in plaque load to the level of 2m-old TG mice or the equivalent of normalisation of neuroinflammation outcomes. However, for a smaller effect size of 30%, larger groups of up to 21 mice were estimated. In light of published guidelines on preclinical trial design, these data may be used to provide provisional sample sizes and optimise preclinical trials in 5xFAD TG mice.
| S-EPMC10085059 | biostudies-literature
Project description:BACKGROUND:Clinical trials of investigational drugs for Alzheimer disease (AD) increasingly focus on the prodromal (symptomatic) stage of the illness and now its preclinical (asymptomatic) stage. Sensitive and specific cognitive and functional endpoints are needed to track subtle cognitive and functional changes in the early and preclinical stages to minimize sample sizes in these trials. OBJECTIVES:To identify informative items in a standard clinical assessment protocol and a psychometric battery that are predictive of onset of dementia symptom. DESIGN:Longitudinal retrospective study. SETTING:Washington University (WU) Knight Alzheimer Disease Research Center (ADRC). PARTICIPANTS:A total of 735 individuals at least 65 years old and cognitively normal at baseline from a longitudinal clinical cohort at the WU Knight ADRC. MEASUREMENTS:The annual clinical assessment included a wide spectrum of functional and cognitive domains; a comprehensive psychometric battery was completed about 2 weeks after the clinical evaluation. Psychometricians are blinded to the results of the clinical evaluation and to the prior performance of the participants on the psychometric tests. RESULTS:The mean age at baseline of the 735 participants was 74.30 and 62.31% were female. 240 individuals developed prodromal dementia symptoms (consistent with mild cognitive impairment due to AD and with very mild AD dementia) during longitudinal follow-up (mean follow-up=6.79 years). Among a total of 562 items in the clinical and cognitive assessments under analysis, 292 (52%) were identified as informative because their longitudinal changes were predictive of symptomatic onset. When these items were used to form the functional and cognitive composites, the longitudinal rates of changes were free of a learning effect and captured subtle longitudinal progression prior to symptomatic onset. The rates of change were much greater right after the symptomatic onset than those from the functional and cognitive composites formed using non-informative items. Although the sample sizes for prevention trials (prior to symptomatic onset) using the informative items still yield large numbers, the sample sizes for early treatment trial (after symptomatic onset) was much smaller than those derived from all the items or from the non-informative items alone. CONCLUSIONS:The antecedent longitudinal changes in nearly half of the items in a clinical assessment protocol and a comprehensive cognitive battery did not show statistically significant ability to predict the dementia symptom onset, and hence may be non-informative to track the preclinical functional and cognitive progression of AD. The remaining items, on the other hand, captured some of the preclinical changes prior to the symptom onset, but performed much better right after the symptom onset. Currently ongoing prevention trials on preclinical AD of elderly individuals may need to re-assess the sample sizes and statistical power.
| S-EPMC6429951 | biostudies-literature
Project description:Eighteen-month-long randomized, placebo-controlled clinical trials are common for phase II and phase III drug development for Alzheimer's disease (AD). Yet, no 18-month trial has shown statistically significant outcomes favoring the test drug. We examined characteristics and underlying assumptions of these trials by assessing the placebo groups.We searched the clinicaltrials.gov registry for randomized, placebo-controlled clinical trials for AD of at least 18-month duration and extracted demographic, clinical, and trials characteristics, and change in main outcomes from the placebo groups. We obtained additional information from presentations, abstracts, publications, and sponsors.Of 23 trials identified, 11 were completed and had baseline data available; nine had follow-up data available; 17 were phase III. General inclusion criteria were very similar except that minimum Mini-Mental State Examination (MMSE) scores varied from 12 to 20. Sample sizes ranged from 402 to 1,684 for phase III trials and 80 to 400 for phase II. Cholinesterase inhibitor use was from 53% to 100%, and memantine use was from 13.5% to 78%. The AD Assessment Scale-cognitive (ADAS-cog) was the co-primary outcome in all trials; and activities of daily living, global severity, or global change ratings were the other co-primaries. APOE epsilon4 genotype carriers ranged from 58% to 67%; mean baseline ADAS-cog was 17.8 to 24.2. ADAS-cog worsening in the placebo groups during 18 months ranged from 4.34 to 9.10, with standard deviations from 8.17 to 9.39, increasing during 18 months.Inclusion criteria are essentially similar to earlier 6-month and 12-month trials in which cholinesterase inhibitors were not allowed, as were mean ADAS-cog rates of change. Yet increasing variability and relatively little change overall in the ADAS-cog placebo groups, eg, about 25% of patients do not worsen by more than 1 point, might make it more unlikely than previously assumed that a modestly effective drug can be reliably recognized, especially when the drug might work only to attenuate decline in function and not to improve function. These observations would be strengthened by pooling individual trials data, and pharmaceutical sponsors should participate in such efforts.
| S-EPMC3321732 | biostudies-literature
Project description:Sample size calculations for cluster-randomised trials require inclusion of an inflation factor taking into account the intra-cluster correlation coefficient. Often, estimates of the intra-cluster correlation coefficient are taken from pilot trials, which are known to have uncertainty about their estimation. Given that the value of the intra-cluster correlation coefficient has a considerable influence on the calculated sample size for a main trial, the uncertainty in the estimate can have a large impact on the ultimate sample size and consequently, the power of a main trial. As such, it is important to account for the uncertainty in the estimate of the intra-cluster correlation coefficient. While a commonly adopted approach is to utilise the upper confidence limit in the sample size calculation, this is a largely inefficient method which can result in overpowered main trials. In this paper, we present a method of estimating the sample size for a main cluster-randomised trial with a continuous outcome, using numerical methods to account for the uncertainty in the intra-cluster correlation coefficient estimate. Despite limitations with this initial study, the findings and recommendations in this paper can help to improve sample size estimations for cluster randomised controlled trials by accounting for uncertainty in the estimate of the intra-cluster correlation coefficient. We recommend this approach be applied to all trials where there is uncertainty in the intra-cluster correlation coefficient estimate, in conjunction with additional sources of information to guide the estimation of the intra-cluster correlation coefficient.
| S-EPMC8649444 | biostudies-literature