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Because it has been repeatedly demonstrated that low technical quality 9 , 10 and small sample sizes 11 — 13 overestimate effect sizes and lower the likelihood of reproducible results 14 , 15 , the attributes we summarize are essential determinants of the generalizability of published models.
The broad domain of knowledge included in this work can be utilized as a reference for MS researchers, funders, and reviewers. Methods Search Method We conducted a literature search to identify studies that generated statistical models to predict clinical outcomes among patients with MS. PubMed searches were performed using keywords related to MS, predictive models, and outcomes.
Exclusion Criteria Two reviewers JL and EK independently screened the studies that reported effect sizes for image-, clinical-, or biomarker-based models predicting a clinical outcome. We excluded studies with no predictive models, studies with no imaging, clinical, or biomarker predictors, studies with no clinical outcomes, non-human studies, non-MS studies, and studies with no full text available.
Information Extraction The following features were extracted from the methods and results of these studies: 1 types of predictors used for modeling i. Assessment of the Quality of Study Design in the Reviewed Models Seven technical quality indicators were extracted from the methods and results sections of each paper, with the following justifications: 1 presence and type of model validation i.
ML algorithms are so powerful that, contrary to expectations, developing models that have surprisingly high effect sizes in the training data set is common and easy. Without a validation strategy, it is not possible to determine the utility of such models, as most artificially and greatly inflate the true effect sizes Thus, the presence and type of model validation are the most important indicators of model reproducibility. The next four attributes of methodological study rigor safeguard against bias.
Their pre-specification e. Such observations should be identified and excluded prior to unblinding by a predefined outlier analysis. If such an analysis is not predefined and the description of methods does not specify how many outliers were excluded and based on what criteria, then the model might be biased Finally, a large amount of missingness that is not disclosed in the paper can also falsely overestimate the generalizability and clinical utility of the model.
Although randomization is also an essential bias-preventing attribute of methodological design, it is mostly applicable to interventional studies, not to modeling studies. The p-value represents the probability of obtaining results at least as extreme as the presented results if the null hypothesis was valid.
We would like to present an analogy that provides a reader without statistical knowledge with a practical intuition of how to judge p-values in the contexts of performing multiple comparisons: let us imagine we have 20 cards numbered from1 to 20 and we are assessing the ability of a blinded person i. Although we eagerly accept the p-value of 0.
On the last attempt, you suggest card 1 and the person correctly selects card 1. Will you still conclude that the person represents a good model for selecting card 1? We intuitively understand that if we ignore the previous failed attempts, we reach the wrong conclusion. Yet, when the same is done in reported biomedical research e.
The science of when and how to adjust for multiple comparisons is more complicated 22 , but the principle is that we must consider how many comparisons the investigators performed and whether they appropriately adjusted the p-values to make a correct inference. However, when applied to HVs, the model also differentiates two groups of people: younger and older. Clearly, this is not biologically valuable model of MS progression.
Or a model claims to be a diagnostic test of MS, buts its accuracy is tested only by differentiating MS from HV, instead of including appropriate controls such as people with non-MS white matter lesions and focal neurological deficits. Depending on how many of these criteria study fulfilled, the quality of the study design ranged from 0 to 7. Although it is not necessary for a study to fulfill all seven criteria to be reproducible, the score assesses methodological rigor between studies and identifies areas for improvement.
Validation of Published Inverse Relationships Between Study Design Quality and Reported Effect Sizes Previous studies on non-MS fields showed that 1 small cohort studies; 2 studies of low experimental quality; and 3 studies performed only in the training cohort, all significantly overestimate true effect sizes 10 , 11 , 13 , To assess whether the same can be observed in the MS field we investigated the relationships between the technical quality of studies including cohort sizes and comparisons of training vs.
In addition to univariate analyses, we also classified groups of studies based on the combination of cohort size and technical quality criteria: studies were considered high quality if they reached 1 standard deviation SD above the mean for both factors, whereas low quality were 1 SD below the mean for both. To compare all identified low- and high-quality studies two-sample Wilcoxon [Mann—Whitney] test , we normalized the different metrics of effect sizes to yield common metrics ranging from 0 to 1.
The results showed that the relationship between age and LDST performance was curvilinear i. Moreover, the relation between age and LDST performance was moderated by gender i. For example, the average relative increase in the LDST score of a 9-year-old child when compared with the test score of an 8-year-old child was The total variance in the LDST score that was explained by all predictors together i. The LDST and its normative data provide a useful tool in applied and research settings when information processing speed needs to be assessed, but there are some limitations that warrant further discussion.
First, all children who participated in the study were native Dutch speakers. The question rises whether the established normative data can also be applied to children who have a different native language or a different cultural background. This finding suggests that the LDST performance of children from different Western countries may also be similar, but empirical research is needed to substantiate this claim. Second, the normative data that were established in the present study were based on a sample of children who were aged between 8.
The question rises whether the regression model and its SD residual values that were established in the present study can also be used to norm the LDST scores of children whose age falls outside this age range. Extrapolating a regression model requires several assumptions.
For example, if we want to estimate the expected test score of a year-old child as based on the regression model shown in Table 2 , it has to be assumed that the relation between age and LDST performance in the age range between If this assumption is not fulfilled, the extrapolation of the regression model may result in biased conclusions e. Since this assumption is not testable as based on the present data set , it is not recommended to extrapolate the model except perhaps when the value to be extrapolated is very near to the age limits that were considered in the normative sample, e.
On a related note, it is not recommended to administer the LDST to children who are younger than 8 years. Apart from the extrapolation problem that was described in the previous paragraph, a prerequisite of valid LDST administration is that the letters and digits are well-known symbols for the testee.
By the age of 8 years, most children are sufficiently familiar with letters and numbers as they had at least 1 year of practice in reading and writing at school , but this may not be the case for younger children see also the SDMT manual, which also uses 8 years as the lower age limit for test administration; Smith, Third, the LDST responses can be given both in writing and verbally.
In the present study, the written LDST version was administered to all children. Previous research in adults has shown that the test scores that were obtained with the oral LDST version were on average higher than the scores that were obtained with the written LDST version Van der Elst et al. It is conceivable that similar effects of response modality also occur in children, and consequently, the normative data that were established in the present study should not be used when the oral version of the LDST is administered.
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