Nature Neuroscience  Perspective
A solution to dependency: using multilevel analysis to accommodate nested data
 Emmeke Aarts^{1}^{, }
 Matthijs Verhage^{1, 2}^{, }
 Jesse V Veenvliet^{3}^{, }
 Conor V Dolan^{4}^{, }
 Sophie van der Sluis^{1, 5}^{, }
 Journal name:
 Nature Neuroscience
 Volume:
 17,
 Pages:
 491–496
 Year published:
 DOI:
 doi:10.1038/nn.3648
 Received
 Accepted
 Published online
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 socalled '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.
Subject terms:
At a glance
Figures
Main
Neuroscience has seen major advances in understanding the nervous system over the past decades. Serious concerns have, however, been raised about an excess of false positive results contaminating the neuroscience literature^{1, 2, 3, 4}. Controlling the false positive rate is critical, since theoretical progress in the neuroscience field relies fundamentally on drawing correct conclusions from experimental research. Reported causes of increased levels of false positives range from inadequate sample size (i.e., underpowered studies), to lack of standardization with respect to research design, applied measures and corrections, exclusion/inclusion criteria, and choice of statistical methods. To improve transparency and reproducibility, Nature journals recently developed a checklist to aid authors to report basic methods information^{5, 6}. Among things, authors are asked whether the assumptions of chosen statistical methods are met. Here, we show that one of these assumptions, i.e., the assumption of independent observations, is particularly relevant to neuroscience: neuroscience data often show dependency (that is, nesting; Box 1) and failure to accommodate this is another, as yet neglected, cause of false positive results.
Box 1: Key statistical terms
Nested designs are not unique to the neuroscience field, but are also encountered, for instance, in the social sciences (for example, children nested in classes, nested in schools), in behavioral genetics (for example, relatives nested in families) and in the field of medicine (for example, patients nested in doctors, nested in hospitals). In biomedical research, nested data are common in electron microscopy studies, with the n often at a subcellular level. In neuroscience, however, studies on neuron morphology and physiology typically give rise to nested data, as technical advances allow researchers to obtain measurements on every dendrite of a neuron and every spine of each dendrite or to acquire multiple recordings of neuronal activity from the same cell.
The problem of nesting
Nested designs are designs in which multiple observations or measurements are collected in each research object (for example, animal, tissue sample or neuron/cell)^{7}. Consider the following fictive, yet representative, research results. “The channel blocker significantly affected Ca^{2+} signals (n = 120 regions of interest (ROI) from 10 cells, P < 0.01).” “The number of vesicles docked at the active zone was smaller in presynaptic boutons in mutant neurons than in WT neurons (n = 20 and 25 synapses each from 3 neurons for mutant and WT, P < 0.01).” Both statements concern experimental designs involving nested (or clustered) data. These nested designs are particularly common to neuroscience, as many research questions in neuroscience consider multiple layers of complexity: from protein complexes, synapses and neurons, to neuronal networks, connected systems in the brain and behavior. In such multiple layer–crossing designs, careful consideration of the issues that come with nesting is crucial to avoid incorrect inferences. The generality of nested designs in molecular, cellular and developmental neuroscience is apparent from a literature study that we conducted involving research articles published over the last 18 months in Science, Nature, Cell, Nature Neuroscience and every first issue of the month of Neuron (see below): at least 53% of the 314 publications included nested data.
But why is nesting an issue? Given that observations taken from the same research object (for example, brain, animal, cell) tend to be more similar than observations taken from different objects (for example, due to natural variation between objects, and differences in measurement procedures or conditions), nested designs yield clusters of observations that cannot be considered independent. Nevertheless, conventional statistical methods, such as the t test and ANOVA, are often used to analyze these nested data, even though these methods assume observations to be independent. However, the failure to take the dependency among observations into account forms a threat to the validity of the statistical inference. Depending on the number of observations per research object and the degree of dependence, the probability of incorrectly concluding that an effect is statistically significant (that is, type I error rate) can be far higher than the nominal level expressed by α (usually α = 0.05). To illustrate the effect of nesting on results obtained through conventional tests, we conducted a simulation study (Fig. 1 and Supplementary Simulation). Given a nominal α of 0.05, ignoring nesting can result in an actual type I error rate as high as 0.80. That is, if no experimental effect is present, conventional methods that do not accommodate dependency will yield spurious statistically significant results in 80% of the studies (see Box 2 for a detailed discussion of the results and the theoretical proof).
Box 2: Inflation of the type I error in nested data
The distinction between the observed and the effective sample size is essential for understanding why clustering affects the type I error rate. The core of this distinction is whether each individual observation contributes unique information. This can be inferred from the degree of relative similarity between observations obtained from the same research object. This similarity is expressed in the intracluster correlation (ICC), which ranges from 0 to 1 (Fig. 2a–c). If clustering is absent (ICC = 0), all observations obtained from a research object are independent, that is, contribute fully unique information. In the extreme case of ICC = 1, all observations obtained from the same research objects are equal and therefore convey the very same information.
The experimental variable (for example, genotype) can contribute to the dissimilarity of observations from different objects, and thereby to the relative similarity of observations from the same object. The part of the relative similarity that is attributable to the experimental variable is referred to as the explained ICC, whereas the part of the ICC that is attributable to other, unknown factors is called the unexplained ICC. We use the term ICC to indicate the unexplained ICC, unless stated otherwise. Notably, the unexplained part of the ICC causes inflation of the type I error rate.
In the extreme case of ICC = 1, the observed sample size may be N, but the effective sample size, that is, the number of unique information units, equals the number of research objects (that is, the number of clusters). For example, given five measurements on ten cells, ICC = 0 implies a sample size of 5 × 10 = 50, but as the ICC tends to 1, the effective sample size tends to 10 (Fig. 2d). In terms of variation, correlation between observations from the same research objects (ICC > 0) reduces the variation in the total sample, compared with the variation expected in a random sample (ICC = 0; Fig. 2a). Because conventional statistical analyses are based on the observed rather than the effective sample size, standard errors of parameters are underestimated and test statistics are overestimated. As a result, the associated P values are too low, which results in an excessive type I error rate^{8}.
To correctly handle dependence in nested designs, multilevel models (also known as hierarchical or random effects models) can be used. These models produce correct type I error rates (Fig. 1a). Alternatively, multilevel analysis can be circumvented by conducting conventional analyses on clusterbased summary statistics, for example, by performing a t test on the means or medians calculated in each cluster. Although this strategy is statistically valid, information contributed by the individual observations is lost, and, relative to multilevel analysis, statistical power to detect the experimental effect of interest decreases^{7, 9, 10}. Conducting t tests on clusterbased means instead of multilevel analysis on all observations results in up to a 40% loss of statistical power, depending on the number of clusters in the study and the ICC (Fig. 1b, and Supplementary Simulation).
The prevalence of nesting in neuroscience studies
To assess the prevalence of nested data and the ensuing problem of inflated type I error rate in neuroscience, we scrutinized all molecular, cellular and developmental neuroscience research articles published in five renowned journals (Science, Nature, Cell, Nature Neuroscience and every month's first issue of Neuron) in 2012 and the first six months of 2013. Unfortunately, precise evaluation of the prevalence of nesting in the literature is hampered by incomplete reporting: not all studies report whether multiple measurements were taken from each research object and, if so, how many. Still, at least 53% of the 314 examined articles clearly concerned nested data, of which 44% specifically reported the number of observations per cluster with a minimum of five observations per cluster (that is, for robust multilevel analysis a minimum of five observations per cluster is required^{11, 12}). The median number of observations per cluster, as reported in literature, was 13 (Fig. 1a), yet conventional analysis methods were used in all of these reports.
The studies reporting nested data typically do not provide information on the ICC, which is required to evaluate the extent to which clustering affected the type I error rates of these studies. To assess the range of ICCs that can be expected, we analyzed 36 research questions in 18 neuroscience data sets from varying disciplines. In these data, unexplained ICCs ranged between 0.00 and 0.74, with a mean of 0.19 (Supplementary Table 1). As even a low degree of dependency (for example, ICC = 0.10) increases the type I error rate from 5% to nearly 20% when the number of observations per cluster is 13 (the median number of observations per cluster observed in our literature study; Fig. 1a), an excess number of false positive results is to be expected. It should be noted that differences in the statistical significance of the findings between multilevel analysis and conventional testing are a result of the unexplained ICC only, not the total ICC (see, for example, results of analysis 7, where all of the ICC is explained). Further inspection of the research articles that reported nested data with a minimum of five observations per cluster showed that 25% of the P values were between 0.01 and 0.001, and 31% between 0.05 and 0.01. False positive effects are to be expected in at least some of these articles. Moreover, another 7% of the examined papers used clusterbased summary statistics in which multilevel analysis could have been applied, resulting in a loss of power to detect experimental effects (see Fig. 1b and Supplementary Table 1 for examples of nonsignificant results obtained with pooled t tests, which actually prove significant when multilevel analysis is used).
Multilevel analysis
Multilevel models can be used to statistically accommodate dependence between observations in nested designs. The basics of multilevel analysis are readily explained with reference to the conventional twogroup t test. Suppose we studied whether characteristic X of the cell is affected by a specific gene mutation. In 15 mice carrying the mutation, we collect ten cells (15 × 10 observations) and we do the same in 15 mice that do not carry the mutation, resulting in 300 observations in total. A standard t test on these data can be carried out by regressing X on the dummy coded (0/1) experimental variable. Significance of the slope parameter, representing the differences in means, can be tested using a t test (Fig. 3a). In this conventional analysis, cluster information is discarded: all 15 × 10 observations in each group are simply pooled. In contrast, in multilevel analysis, the individual observations (the cells) are regarded as level 1 units, which are nested in the level 2 units: the clusters (the mice). Multilevel analysis retains clustermembership information by conducting the t test on the clusterlevel (mouse) means while retaining the distinction between the variance within clusters (differences between cells within a mouse) and variance between clusters (differences between the mice in clusterlevel means; Fig. 3b). Multilevel analysis therefore effectively accommodates the possibly increased similarity of observations taken from the same research object by retaining clustermembership information of each individual observation when evaluating parameters such as group differences.
Various standardized effect size measures have been suggested in the context of multilevel analysis^{13, 14}. When comparing only two experimental conditions, Cohen's d is a generally accepted index. When comparing more than two experimental conditions, the overall effect size can be represented by the explained variance R^{2}, which equals the explained ICC when the experimental condition only varies over clusters and not within clusters. Cohen^{15} defined a Cohen's d of 0.20, 0.50 and 0.80, and an explained variance R^{2} of 0.01, 0.09 and 0.25 as small, medium and large effects, respectively. Note that these two effect sizes are not on the same scale and can therefore not be compared directly. However, d and R^{2} can be converted into each other^{16}
Note that in equation (1), the sum of the Cohen's d values obtained in pairwise comparisons is used when multiple pairwise comparisons are combined into one omnibus test. Equations (1) and (2) assume experimental groups with equal sample sizes (see ref. 16 for formulas for unbalanced designs). A worked example of the analysis of nested data, including effect size calculation, is provided in the Supplementary Analysis and Supplementary Tables 2–6.
Power up: determining the optimal study design
Generally, power is increased by increasing the number of observations in a study. In conventional analysis, this is straightforward, but, in multilevel analysis, the relation between sample size and power is more complicated as the total number of observations is distributed over the research objects (clusters). In the allocation of research resources (for example, money and time), a tradeoff must be considered between the number of clusters and the number of observations per cluster. In practice, collecting many measures in a few clusters may be easier, faster and cheaper than collecting a few measures in many clusters. But which option confers the greatest power?
In multilevel analysis, power depends essentially on the number of clusters: power steadily increases to 100% as the number of clusters increases (Fig. 4a). In contrast, when increasing the number of observations per cluster, the power curve often approaches an asymptote below 100%, with the maximum level depending on the ICC (Fig. 4b). In general, high ICCs result in lower power and, unless the ICC is low, adding extra observations per cluster does little to increase power (Fig. 4a,b).
Given available resources (for example, money or time), the optimal balance between the number of clusters (N) and the number of observations per cluster (n) can be determined, given a specified level of dependency (ICC). In theory, optimal N and n are dictated by the desired level of statistical power. In practice, however, available resources have a bearing on the attainable values of N and n. Given that including additional observations within a cluster (C_{1}) is usually less costly than including an additional cluster (C_{2}), these two costs are defined distinctly. The total costs of a study are calculated as (3)
while the optimal number of observations per cluster can be obtained by solving^{9, 13} (4)
where is the residual variance, which equals 1 − overall ICC (note that we make use of the standardized model, that is, the observations are standardized such that they have a mean of 0 and an s.d. of 1). Given the total available resources T and the optimal number of observations per cluster n_{optimal}, the optimal number of clusters N can be obtained by (5)
The optimal balance between the number of clusters and number of observations per cluster does not guarantee that the subsequent study will have sufficient power to detect the experimental effect of interest. The actual power of the experiment also depends on the chosen α level and on the expected effect size (for example, the magnitude of the difference between the control and experimental group). However, the calculated optimal N and n can be used to estimate the expected power given specific values of the effect size and the ICC (Box 3).
Box 3: Estimating the power to detect an experimental effect
Discussion
Multilevel modeling is relevant to neuroscientific data collected using traditional techniques, such as the analysis of immunofluorescence signal intensity in slices (where the use of clusterbased summary statistics causes a loss of power), and the analysis of electrophysiological parameters, such as excitatory postsynaptic potentials (where the use of conventional statistical models inflates type I error rates). Recent advances in the field of neuroscience, such as optogenetics, superresolution microscopy, immunogold cytochemistry and optopharmacology, will, if anything, increase the relevance of multilevel modeling^{17}. A common feature of all these techniques is that they shift the n from the animal or tissue level to the cellular or even subcellular level, and invariably yield data with a nested structure. For instance, superresolution light microscopy allows imaging and advanced understanding of neuronal compartments^{18}, immunogold cytochemistry allows determination of subcellular localization of proteins^{19}, and recent advances in optogenetics and optopharmacology facilitate selective control of electrical and protein activity, respectively, in circuits, individual cells or subcellular compartments^{20, 21}. All these techniques concern the collection of multiple observations from one cell, thereby yielding nested data.
To fully exploit the advantages that these techniques offer, neuroscientists should adopt multilevel modeling to avoid the limitations of conventional analyses in this context. In addition, nested data come with specific design issues that are relevant to the statistical power to resolve the effects of interest. Optimization of design in terms of allocation of resources does not guarantee sufficiently powered studies. In terms of power, the ratio of number of research objects (for example, mice) to the number of measurements per object (for example, cells per mouse) is important. We showed that the power increase achievable by increasing the latter is limited (Fig. 4). In addition, to obtain robust and unbiased estimates of variance components in multilevel analysis, sufficient observations on both levels are required. As a rule of thumb, afforded by simulation studies^{11, 12}, a minimum of five observations per cluster and ten clusters per experimental group are recommended to obtain a robust and unbiased estimate of the standard error for the experimental effect. To also obtain a robust and unbiased estimate of the intracluster correlation, the number of clusters needs to be increased to 30.
Here we focused on the most common design, that is, data that span two levels (for example, cells in mice) and an experimental variable that does not vary within clusters (for example, in comparing cell characteristic X between mutants and wild types, all cells from one mouse have the same genotype). Other nested designs—featuring three or more levels of nesting, experimental variables that do vary within levels (for example, when investigating whether the number of docked vesicles differs between observations from a dendrite or an axon), nested longitudinal data (data collected on multiple time points describing dynamical processes^{22, 23}) or nested nonnormally distributed data (for example, binary or Poisson distributed data)—are, however, possible and can be analyzed using multilevel analysis. We refer to previous publications^{12, 13, 24} for comprehensive introductions to multilevel modeling and to the Centre for Multilevel Modeling website (http://www.bristol.ac.uk/cmm/learning/mmsoftware/) for a recent overview of existing multilevel software.
Various recent publications force neuroscientists to acknowledge the possibility that the harvest of their hard labor is contaminated by an abundance of false positive effects^{1, 2, 3, 4}. Nested designs are ubiquitous in neuroscience, and an increased awareness of the problem of nesting in both researchers and reviewers will prevent costly and timeconsuming quixotic pursuits of spurious effects, thereby assisting progress in the understanding of the nervous system.
References
 Button, K.S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).
 Ioannidis, J.P. Why most published research findings are false. PLoS Med. 2, e124 (2005).
 Nieuwenhuis, S., Forstmann, B.U. & Wagenmakers, E.J. Erroneous analyses of interactions in neuroscience: a problem of significance. Nat. Neurosci. 14, 1105–1107 (2011).
 Tsilidis, K.K. et al. Evaluation of excess significance bias in animal studies of neurological diseases. PLoS Biol. 11, e1001609 (2013).
 Raising standards. Nat. Neurosci. 16, 517 (2013).
 Making methods clearer. Nat. Neurosci. 16, 1 (2013).
 Galbraith, S., Daniel, J.A. & Vissel, B. A study of clustered data and approaches to its analysis. J. Neurosci. 30, 10601–10608 (2010).
 Walsh, J.E. Concerning the effects of the intraclass correlation on certain significance tests. Ann. Math. Stat. 18, 88–96 (1947).
 Raudenbush, S.W. Statistical analysis and optimal design for cluster randomized trials. Psychol. Methods 2, 173–185 (1997).
 Moerbeek, M., van Breukelen, G.J.P. & Berger, M.P. A comparison between traditional methods and multilevel regression for the analysis of multicenter intervention studies. J. Clin. Epidemiol. 56, 341–350 (2003).
 Maas, C.J.M. & Hox, J.J. Robustness issues in multilevel regression analysis. Stat. Neerl. 58, 127–137 (2004).
 Snijders, T.A.B. & Bosker, R.J. Multilevel Analysis: an Introduction to Basic and Advanced Multilevel Modeling (Sage Publications, London, 2011).
 Snijders, T.A.B. & Bosker, R.J. Standard errors and sample sizes for 2level research. J. Educ. Stat. 18, 237–259 (1993).
 Hox, J. Multilevel Analysis: Techniques and Applications (Erlbaum, New Jersey, 2010).
 Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Erlbaum, Hillsdale, JN, 1988).
 Lipsey, M.W. & Wilson, D.B. Practical Metaanalysis (Sage, Thousand Oaks, CA, 2001).
 Focus on neurotechniques. Nat. Neurosci. 16, 771 (2013).
 Maglione, M. & Sigrist, S.J. Seeing the forest tree by tree: superresolution light microscopy meets the neurosciences. Nat. Neurosci. 16, 790–797 (2013).
 AmiryMoghaddam, M. & Ottersen, O.P. Immunogold cytochemistry in neuroscience. Nat. Neurosci. 16, 798–804 (2013).
 Packer, A.M., Roska, B. & Hausser, M. Targeting neurons and photons for optogenetics. Nat. Neurosci. 16, 805–815 (2013).
 Kramer, R.H., Mourot, A. & Adesnik, H. Optogenetic pharmacology for control of native neuronal signaling proteins. Nat. Neurosci. 16, 816–823 (2013).
 Smith, A.C., Stefani, M.R., Moghaddam, B. & Brown, E.N. Analysis and design of behavioral experiments to characterize population learning. J. Neurophysiol. 93, 1776–1792 (2005).
 Czanner, G. et al. Analysis of betweentrial and withintrial neural spiking dynamics. J. Neurophysiol. 99, 2672–2693 (2008).
 Goldstein, H. Multilevel Statistical Models (Edward Arnold, London, 2010).
Acknowledgments
We are very grateful to our colleagues from the VU University/VU Medical Center Functional Genomics department for sharing their data. M.V. is supported by the European Union (ERC Advanced grant 322966; HEALTH–F2–2009–241498 EUROSPIN, and HEALTH–F2– 2009–242167 SynSys) and the Netherlands Organization for Scientific Research (TOP 903–42–095). C.V.D. is supported by the European Research Council (Genetics of Mental Illness, grant number: ERC–230374). S.v.d.S. is supported by the Netherlands Scientific Organization (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, gebied Maatschappij en Gedragswetenschappen: NWO/MaGW: VIDI–452–12–014).
Author information
Affiliations

Section Functional Genomics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands.
 Emmeke Aarts,
 Matthijs Verhage &
 Sophie van der Sluis

Section Functional Genomics, Department Clinical Genetics, VU Medical Center, Amsterdam, The Netherlands.
 Matthijs Verhage

Center for Neuroscience, Swammerdam Institute for Life Sciences, Science Park, University of Amsterdam, Amsterdam, The Netherlands.
 Jesse V Veenvliet

Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.
 Conor V Dolan

Section Complex Trait Genetics, Department of Clinical Genetics, VU Medical Center, Amsterdam, The Netherlands.
 Sophie van der Sluis
Competing financial interests
The authors declare no competing financial interests.
Author details
Emmeke Aarts
Search for this author in:
Matthijs Verhage
Search for this author in:
Jesse V Veenvliet
Search for this author in:
Conor V Dolan
Search for this author in:
Sophie van der Sluis
Search for this author in:
Supplementary information
PDF files
 Supplementary Text and Figures (522 KB)
Supplementary Simulation, Supplementary Analysis, and Supplementary Tables 2–6
Excel files
 Supplementary Table 1 (54 KB)
Conventional and multilevel analysis of various neuroscience datasets