Dataset Information

Power estimation for non-standardized multisite studies.

ABSTRACT: A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.

SUBMITTER: Keshavan A

PROVIDER: S-EPMC5656257 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Power estimation for non-standardized multisite studies.

Keshavan Anisha A Paul Friedemann F Beyer Mona K MK Zhu Alyssa H AH Papinutto Nico N Shinohara Russell T RT Stern William W Amann Michael M Bakshi Rohit R Bischof Antje A Carriero Alessandro A Comabella Manuel M Crane Jason C JC D'Alfonso Sandra S Demaerel Philippe P Dubois Benedicte B Filippi Massimo M Fleischer Vinzenz V Fontaine Bertrand B Gaetano Laura L Goris An A Graetz Christiane C Gröger Adriane A Groppa Sergiu S Hafler David A DA Harbo Hanne F HF Hemmer Bernhard B Jordan Kesshi K Kappos Ludwig L Kirkish Gina G Llufriu Sara S Magon Stefano S Martinelli-Boneschi Filippo F McCauley Jacob L JL Montalban Xavier X Mühlau Mark M Pelletier Daniel D Pattany Pradip M PM Pericak-Vance Margaret M Cournu-Rebeix Isabelle I Rocca Maria A MA Rovira Alex A Schlaeger Regina R Saiz Albert A Sprenger Till T Stecco Alessandro A Uitdehaag Bernard M J BMJ Villoslada Pablo P Wattjes Mike P MP Weiner Howard H Wuerfel Jens J Zimmer Claus C Zipp Frauke F Hauser Stephen L SL Oksenberg Jorge R JR Henry Roland G RG

NeuroImage 20160401

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based corr ...[more]

PMID: 27039700

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Project description:BackgroundBefore conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (pi1) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes.ResultsA sample size estimate based on the common formulation, to achieve the desired sensitivity on average, can be calculated using a univariate method without taking the correlation among genes into consideration. This formulation of sample size problem is inadequate because the probability of detecting the specified sensitivity can be lower than 50%. On the other hand, the needed sample size calculated by the proposed permutation method will ensure detecting at least the desired sensitivity with 95% probability. The method is shown to perform well for a real example dataset using a small pilot dataset with 4-6 samples per group.ConclusionsWe recommend that the sample size problem should be formulated to detect a specified proportion of differentially expressed genes with 95% probability. This formulation ensures finding the desired proportion of true positives with high probability. The proposed permutation method takes the correlation structure and effect size heterogeneity into consideration and works well using only a small pilot dataset.

Project description:BackgroundWhen designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS.ResultsThe current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (Δβ), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user settings.ConclusionBoth under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in the design and planning of EWAS.AvailabilityThe web interface is written in the R statistical programming language using Shiny (RStudio Inc., 2016) and is available at https://biostats-shinyr.kumc.edu/pwrEWAS/ . The R package for pwrEWAS is publicly available at GitHub ( https://github.com/stefangraw/pwrEWAS ).

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Dataset Information

Power estimation for non-standardized multisite studies.

Publications

Power estimation for non-standardized multisite studies.

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