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A large space still exists for improving the measurements used in orthopaedics and sports medicine, especially as we face rapid technological progress in devices used for diagnostic or patient monitoring purposes. For a specific measure to be valuable and applicable in clinical practice, its reliability must be established. Reliability refers to the extent to which measurements can be replicated, and three types of reliability can be distinguished: inter-rater, intra-rater, and test–retest. The present article aims to provide insights into reliability as one of the most important and relevant properties of measurement tools. It covers essential knowledge about the methods used in orthopaedics and sports medicine for reliability studies. From design to interpretation, this article guides readers through the reliability study process. It addresses crucial issues such as the number of raters needed, sample size calculation, and breaks between particular trials. Different statistical methods and tests are presented for determining reliability depending on the type of gathered data, with particular attention to the commonly used intraclass correlation coefficient.

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

Once considered mere noise, fMRI-based functional connectivity has become a major neuroscience tool in part due to early studies demonstrating its reliability. These fundamental studies revealed only the tip of the iceberg; over the past decade, many test-retest reliability studies have continued to add nuance to our understanding of this complex topic. A summary of these diverse and at times contradictory perspectives is needed. We aimed to summarize the existing knowledge regarding test-retest reliability of functional connectivity at the most basic unit of analysis: the individual edge level. This entailed (1) a meta-analytic estimate of reliability and (2) a review of factors influencing reliability. A search of Scopus was conducted to identify studies that estimated edge-level test-retest reliability. To facilitate comparisons across studies, eligibility was restricted to studies measuring reliability via the intraclass correlation coefficient (ICC). The meta-analysis included a random effects pooled estimate of mean edge-level ICC, with studies nested within datasets. The review included a narrative summary of factors influencing edge-level ICC. From an initial pool of 212 studies, 44 studies were identified for the qualitative review and 25 studies for quantitative meta-analysis. On average, individual edges exhibited a “poor” ICC of 0.29 (95% CI = 0.23 to 0.36). The most reliable measurements tended to involve: (1) stronger, within-network, cortical edges, (2) eyes open, awake, and active recordings, (3) more within-subject data, (4) shorter test-retest intervals, (5) no artifact correction (likely due in part to reliable artifact), and (6) full correlation-based connectivity with shrinkage. This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity. Key findings suggest there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity. By pooling existing knowledge regarding this key facet of accuracy, this study supports broader efforts to improve inferences in the field. •We present the first meta-analytic review of functional connectivity reliability.•On average, individual connections exhibit a “poor” ICC of 0.29.•Study characteristics that influence reliability are summarized and discussed.•Findings suggest room for improvement and need to also consider validity.

•In the baseline and moderate sedation conditions, the microstate topography and parameters in the 91-, 64-, and 32-channel configurations were consistent.•The microstate characteristics of propofol induction remained stable in the 91-, 64-, and 32-channel configurations, but not at the 19- or 8-channel resolutions.•These findings imply that microstate analysis is not reliable at low electrode densities, such as fewer than 20 channels. Electroencephalogram (EEG) microstate analysis is a promising and effective spatio-temporal method that can segment signals into several quasi-stable classes, providing a great opportunity to investigate short-range and long-range neural dynamics. However, there are still many controversies in terms of reproducibility and reliability when selecting different parameters or datatypes. In this study, five electrode configurations (91, 64, 32, 19, and 8 channels) were used to measure the reliability of microstate analysis at different electrode densities during propofol-induced sedation. First, the microstate topography and parameters at five different electrode densities were compared in the baseline (BS) condition and the moderate sedation (MD) condition, respectively. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were introduced to quantify the consistency of the microstate parameters. Second, statistical analysis and classification between BS and MD were performed to determine whether the microstate differences between different conditions remained stable at different electrode densities, and ICC was also calculated between the different conditions to measure the consistency of the results in a single condition. The results showed that in both the BS or MD condition, respectively, there were few significant differences in the microstate parameters among the 91-, 64-, and 32-channel configurations, with most of the differences observed between the 19- or 8-channel configurations and the other configurations. The ICC and CV data also showed that the consistency among the 91-, 64-, and 32-channel configurations was better than that among all five electrode configurations after including the 19- and 8-channel configurations. Furthermore, the significant differences between the conditions in the 91-channel configuration remained stable at the 64- and 32-channel resolutions, but disappeared at the 19- and 8-channel resolutions. In addition, the classification and ICC results showed that the microstate analysis became unreliable with fewer than 20 electrodes. The findings of this study support the hypothesis that microstate analysis of different brain states is more reliable with higher electrode densities; the use of a small number of channels is not recommended.

There are similarities between the different forms of reliability, such as internal consistency (internal reliability) and interrater and intrarater reliability. Reliability coefficients that are based on classical test theory can be expressed as intraclass correlation coefficients (ICCs), such as Cronbach's alpha. The Spearman–Brown prophecy formula (SB formula) is used to calculate the reliability when the number of items in a questionnaire is changed. This paper aims to increase insight into reliability studies by pointing to the assumptions of reliability coefficients, similarities between various coefficients, and the subsequent new applications of reliability coefficients. The origin and assumptions of Cronbach's alpha and the SB formula are discussed. Cronbach's alpha is written as an ICC formula, using the well-known property that taking the average value of a number of ratings increases the reliability of a measurement. We illustrate with an example that the ICC formulas for average measurements of multiple raters and the SB formula give similar results. This implies that the SB formula can be used to decide on the number of measurements to be averaged and thus on the number of raters required, for obtaining measurements with acceptable reliability, even if the variance components of the ICC formula are not known. Using the same example, we illustrate the principle of “Cronbach's alpha if item deleted” to decide on the poorest performing raters in a set of raters. These applications have different assumptions: the principle of “Cronbach's alpha if item deleted” is based on the assumption of a fixed set of items/raters and the SB formula is based on the assumption of random raters. The example also emphasizes the need for more raters in the design of the reliability study to obtain a robust estimation of reliability.

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson′s correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman′s rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Neuroimaging, in addition to many other fields of clinical research, is both time-consuming and expensive, and recruitable patients can be scarce. These constraints limit the possibility of large-sample experimental designs, and often lead to statistically underpowered studies. This problem is exacerbated by the use of outcome measures whose accuracy is sometimes insufficient to answer the scientific questions posed. Reliability is usually assessed in validation studies using healthy participants, however these results are often not easily applicable to clinical studies examining different populations. I present a new method and tools for using summary statistics from previously published test-retest studies to approximate the reliability of outcomes in new samples. In this way, the feasibility of a new study can be assessed during planning stages, and before collecting any new data. An R package called relfeas also accompanies this article for performing these calculations. In summary, these methods and tools will allow researchers to avoid performing costly studies which are, by virtue of their design, unlikely to yield informative conclusions.

Reliability and measurement error are related but distinct measurement properties. They are connected because both can be evaluated using the same data, typically collected from studies involving repeated measurements in individuals who are stable on the outcome of interest. However, they are calculated using different statistical methods and refer to different quality aspects of measurement instruments. We explain that a measurement error refers to the precision of a measurement, that is, how similar or close the scores are across repeated measurements in a stable individual (variation within individuals). In contrast, reliability indicates an instrument's ability to distinguish between individuals, which depends both on the variation between individuals (ie, heterogeneity in the outcome being measured in the population) and the precision of the score, ie, the measurement error. Evaluating reliability helps to understand if a particular source of variation (eg, occasion, type of machine, or rater) influences the score, and whether the measurement can be improved by better standardizing this source. Intraclass-correlation coefficients, standards error of measurement, and variance components are explained and illustrated with an example.

This paper aims to introduce multilevel logistic regression alysis in a simple and practical way. First, we introduce the basic principles of logistic regression alysis (conditiol probability, logit transformation, odds ratio). Second, we discuss the two fundamental implications of running this kind of alysis with a nested data structure: In multilevel logistic regression, the odds that the outcome variable equals one (rather than zero) may vary from one cluster to another (i.e. the intercept may vary) and the effect of a lower-level variable may also vary from one cluster to another (i.e. the slope may vary). Third and filly, we provide a simplified three-step “turnkey” procedure for multilevel logistic regression modeling:-Prelimiry phase: Cluster- or grand-mean centering variables -Step #1: Running an empty model and calculating the intraclass correlation coefficient (ICC) -Step #2: Running a constrained and an augmented intermediate model and performing a likelihood ratio test to determine whether considering the cluster-based variation of the effect of the lower-level variable improves the model fit -Step #3 Running a fil model and interpreting the odds ratio and confidence intervals to determine whether data support your hypothesisCommand syntax for Stata, R, Mplus, and SPSS are included. These steps will be applied to a study on Justin Bieber, because everybody likes Justin Bieber.1