A solution to dependency: using multilevel analysis to accommodate nested data

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Abstract

In neuroscience, experimental designs in which multiple observations are collected from a single research object (for example, multiple neurons from one animal) are common: 53% of 314 reviewed papers from five renowned journals included this type of data. These so-called 'nested designs' yield data that cannot be considered to be independent, and so violate the independency assumption of conventional statistical methods such as the t test. Ignoring this dependency results in a probability of incorrectly concluding that an effect is statistically significant that is far higher (up to 80%) than the nominal α level (usually set at 5%). We discuss the factors affecting the type I error rate and the statistical power in nested data, methods that accommodate dependency between observations and ways to determine the optimal study design when data are nested. Notably, optimization of experimental designs nearly always concerns collection of more truly independent observations, rather than more observations from one research object.

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Targeting neurons and photons for optogenetics.

Rowena M. A. Packer, Botond Roska, Michael Häusser (2013)

Optogenetic approaches promise to revolutionize neuroscience by using light to manipulate neural activity in genetically or functionally defined neurons with millisecond precision. Harnessing the full potential of optogenetic tools, however, requires light to be targeted to the right neurons at the right time. Here we discuss some barriers and potential solutions to this problem. We review methods for targeting the expression of light-activatable molecules to specific cell types, under genetic, viral or activity-dependent control. Next we explore new ways to target light to individual neurons to allow their precise activation and inactivation. These techniques provide a precision in the temporal and spatial activation of neurons that was not achievable in previous experiments. In combination with simultaneous recording and imaging techniques, these strategies will allow us to mimic the natural activity patterns of neurons in vivo, enabling previously impossible 'dream experiments'.

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Statistical analysis and optimal design for cluster randomized trials.

Stephen Raudenbush (1997)

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A study of clustered data and approaches to its analysis.

Sally Galbraith, James A. Daniel, Bryce Vissel (2010)

Statistical analysis is critical in the interpretation of experimental data across the life sciences, including neuroscience. The nature of the data collected has a critical role in determining the best statistical approach to take. One particularly prevalent type of data is referred to as "clustered data." Clustered data are characterized as data that can be classified into a number of distinct groups or "clusters" within a particular study. Clustered data arise most commonly in neuroscience when data are compiled across multiple experiments, for example in electrophysiological or optical recordings taken from synaptic terminals, with each experiment providing a distinct cluster of data. However, there are many other types of experimental design that can yield clustered data. Here, we provide a statistical model for intracluster correlation and systematically investigate a range of methods for analyzing clustered data. Our analysis reveals that it is critical to take data clustering into account and suggests appropriate statistical approaches that can be used to account for data clustering.