scholarly journals Development of PowerMap: a Software Package for Statistical Power Calculation in Neuroimaging Studies

2012 ◽  
Vol 10 (4) ◽  
pp. 351-365 ◽  
Author(s):  
Karen E. Joyce ◽  
Satoru Hayasaka
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ian D. Buller ◽  
Derek W. Brown ◽  
Timothy A. Myers ◽  
Rena R. Jones ◽  
Mitchell J. Machiela

Abstract Background Cancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design. Results We developed sparrpowR as an open-source R package to estimate the statistical power of the spatial relative risk function. sparrpowR generates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications of sparrpowR that perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility of sparrpowR to calculate the local power for spatial cluster detection. Conclusions sparrpowR improves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.


1999 ◽  
Vol 88 (01) ◽  
pp. 7-16 ◽  
Author(s):  
D Anderson ◽  
AJ Edwards ◽  
P Fisher ◽  
DP Lovell

AbstractPrevious studies have been interpreted as suggesting that low concentrations of N-methyl-N′-nitro-N-nitrosoguanidine (MNNG) have an adaptive effect in the cultured lymphocytes of responsive donors (that is, the cells are protected against the mutagenic effects of a subsequent challenge with a higher concentration of MNNG). The objectives of the present study were to investigate, under stringent experimental conditions, whether a protective effect exists at very low and extremely low doses of MNNG (10−8 and 10−24M, respectively).Peripheral blood lymphocytes from a donor considered responsive in a previous study were stimulated to divide and were cultured under standard conditions. Pre-adaptive treatments with dilutions of MNNG were added to the cultures repeatedly before a challenge treatment with MNNG. Bromodeoxyuridine was added at the same time as the challenge treatment and, following mitotic arrest, cells were differentially stained so that the number of sister chromatid exchanges (SCEs) could be counted. The study was designed to address potential criticisms of earlier studies which did not include replicate cultures. Samples of blood were divided into two identical batches for independent processing. Five replicate cultures were prepared for each combination of pre-adaptive and challenge treatments in each batch. The complete experiment was repeated to provide a further test of the consistency of results. Five replicates per treatment combination were chosen in an attempt to provide an experiment of adequate statistical power. Considerable precautions were taken to minimise the effect of factors outside experimental control on the results. Scoring was done by three scorers. In order to minimise inter-scorer variation, 240 cells were scored at each treatment observation (five cells per scorer, three scorers per culture, four cultures per batch, two batches per experiment and two experiments). The study was designed in this way to take account of the sources of variability to ensure that any response obtained would exceed that obtainable by experimental variability alone. A high level of quality assurance monitoring was undertaken throughout the investigation. Two measures of SCE induction were used: (i) the mean frequency of SCEs; (ii) proportion of cells with at least 20 SCEs. In both experiments, the challenge concentration of MNNG significantly increased SCE frequency. There were, however, highly significant differences between the two experiments. The proportion of high frequency cells (HFCs) in Experiment 1 was increased significantly; the proportion of HFCs was also increased in Experiment 2, but the increase was not statistically significant. The pre-adaptive concentrations of MNNG included an extremely low dilution of 6.8 × 10−24 M and a very low dilution of 6.8 × 10−8 M in Experiment 1 and 1.4 × 10−7 M in Experiment 2. The various pre-adaptive concentrations used had no consistent protective effect against the SCE-inducing capacity of the challenge concentration of MNNG of 6.8 × 10−6 M.It is concluded that an adaptive response to the alkylating agent MNNG could not be demonstrated in cultured human lymphocytes. Neither a very low nor an extremely low dilution of MNNG elicited an adaptive response in terms of SCE induction (measured either as SCE frequency or as proportion of HFCs). This is in contradiction to previous reports published by us and other groups. This study was carefully designed with large numbers of replicates, a preliminary statistical power calculation, predefined comparisons and extensive quality assurance at each treatment administration. Despite these precautions the variability between scorers and between batches was much larger than anticipated. This resulted in some statistically significant differences, but these are likely to be false positives. Our findings indicate the need for such methodological refinement in human cell adaptive response studies.


Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nils Kappelmann ◽  
Bertram Müller-Myhsok ◽  
Johannes Kopf-Beck

AbstractAdaptations to the gold standard randomised controlled trial (RCT) have been introduced to decrease trial costs and avoid high sample sizes. To facilitate development of precision medicine algorithms that aim to optimise treatment allocation for individual patients, we propose a new RCT adaptation termed the nested-precision RCT (npRCT). The npRCT combines a traditional RCT (intervention A versus B) with a precision RCT (stratified versus randomised allocation to A or B). This combination allows online development of a precision algorithm, thus providing an integrated platform for algorithm development and its testing. Moreover, as both the traditional and the precision RCT include participants randomised to interventions of interest, data from these participants can be jointly analysed to determine the comparative effectiveness of intervention A versus B, thus increasing statistical power. We quantify savings of the npRCT compared to two independent RCTs by highlighting sample size requirements for different target effect sizes and by introducing an open-source power calculation app. We describe important practical considerations such as blinding issues and potential biases that need to be considered when designing an npRCT. We also highlight limitations and research contexts that are less suited for an npRCT. In conclusion, we introduce the npRCT as a novel precision medicine trial design strategy which may provide one opportunity to efficiently combine traditional and precision RCTs.


2021 ◽  
Author(s):  
Jörn Bethune ◽  
April Kleppe ◽  
Søren Besenbacher

AbstractThe mutation rate of a specific position in the human genome depends on the sequence context surrounding it. Modeling the mutation rate by estimating a rate for each possible k-mer, however, only works for small values of k since the data becomes too sparse for larger values of k. Here we propose a new method that solves this problem by grouping similar k-mers using IUPAC patterns. We refer to the method as k-mer pattern partition and have implemented it in a software package called kmerPaPa. We use a large set of human de novo mutations to show that this new method leads to improved prediction of mutation rates and makes it possible to create models using wider sequence contexts than previous studies. Revealing that for some mutation types, the mutation rate of a position is significantly affected by nucleotides that are up to four base pairs away. As the first method of its kind, it does not only predict rates for point mutations but also indels. We have additionally created a software package called Genovo that, given a k-mer pattern partition model, predicts the expected number of synonymous, missense, and other functional mutation types for each gene. Using this software, we show that the created mutation rate models increase the statistical power to detect genes containing disease-causing variants and to identify genes under strong constraint, e.g. haploinsufficient genes.


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