Latent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the... more
Latent change score (LCS) models within a continuous-time state-space modeling framework provide a convenient statistical approach for analyzing developmental data. In this study, we evaluate the robustness of such an approach in the context of accelerated longitudinal designs (ALDs). ALDs are especially interesting because they imply a very high rate of planned data missingness. Additionally, most longitudinal studies present unexpected participant attrition leading to unplanned missing data. Therefore, in ALDs, both sources of data missingness are combined. Previous research has shown that ALDs for developmental research allow recovering the population generating process. However, it is unknown how participant attrition impacts the model estimates. We have three goals: (a) to evaluate the robustness of the group-level parameter estimates in scenarios with empirically plausible unplanned data missingness; (b) to evaluate the performance of Kalman scores (KS) imputations for individual data points that were expected but unobserved; and (c) to evaluate the performance of KS imputations for individual data points that were outside the age ranged observed for each case (i.e., to estimate the individual trajectories for the complete age range under study). In general, results showed lack of bias in the simulated conditions. The variability of the estimates increased with lower sample sizes and higher missingness severity. Similarly, we found very accurate estimates of individual scores for both planned and unplanned missing data points. These results are very important for applied practitioners in terms of forecasting and making individual-level decisions. R code is provided to facilitate its implementation by applied researchers.
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Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has investigated whether univariate... more
Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has investigated whether univariate developmental processes can be characterized with data obtained from ALDs. However, many important questions in psychology and related sciences imply working with several variables that are intercorrelated as they unfold over time, such as cognitive and cortical development. Therefore, bivariate developmental models are required. This study aimed to assess the effectiveness of continuous-time bivariate Latent Change Score (CT-BLCS) models for recovering the trajectories of two interdependent developmental processes using data from diverse ALDs. Through a Monte Carlo simulation study, the efficacy of different sampling designs and sample sizes was examined. The study fills a gap in the literature by examining the performance of ALDs in bivariate systems, providing specific recommendations for future application of ALDs for studying interrelated developmental variables.
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Accelerated longitudinal designs (ALD) allow studying developmental processes usually spanning multiple years in a much shorter time framework by including participants from different age cohorts, which are assumed to share the same... more
Accelerated longitudinal designs (ALD) allow studying developmental processes usually spanning multiple years in a much shorter time framework by including participants from different age cohorts, which are assumed to share the same population parameters. However, different cohorts may have been exposed to dissimilar contextual factors, resulting in different developmental trajectories. If such differences are not accounted for, the generating process will not be adequately characterized. In this paper, we propose a continuous-time latent change score model as an approach to capture cohort differences affecting the speed of maturation of psychological processes in ALDs. This approach fills an important gap in the literature because, until now, no method existed for this goal. Using a Monte-Carlo simulation study, we show that the proposed model detects cohort differences adequately, regardless of their size in the population. Our proposed model can help developmental researchers control for cohort effects in the context of ALDs.
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The Bivariate Latent Change Score (BLCS) model is a popular framework for the study of dynamics in longitudinal research. Despite its popularity, there is little evidence of the ability of this model to recover latent dynamics when the... more
The Bivariate Latent Change Score (BLCS) model is a popular framework for the study of dynamics in longitudinal research. Despite its popularity, there is little evidence of the ability of this model to recover latent dynamics when the latent trajectories are affected by stochastic innovations (i.e., dynamic error). The deterministic specification of the BLCS model does not account for the effect of these innovations in the system. In contrast, the stochastic specification of the BLCS model includes parameters that capture the effect of such innovations at the latent level. Through Monte Carlo simulation, we generated two developmental processes and examined the recovery of the parameters in the deterministic and stochastic BLCS models under a broad range of empirically relevant conditions. Based on our findings, we provide specific guidelines and recommendations for the application of BLCS models in developmental research.
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People show stable differences in the way their affect fluctuates over time. Within the general framework of dynamical systems, the damped linear oscillator (DLO) model has been proposed as a useful approach to study affect dynamics. The... more
People show stable differences in the way their affect fluctuates over time. Within the general framework of dynamical systems, the damped linear oscillator (DLO) model has been proposed as a useful approach to study affect dynamics. The DLO model can be applied to repeated measures provided by a single individual, and the resulting parameters can capture relevant features of the person's affect dynamics. Focusing on negative affect, we provide an accessible interpretation of the DLO model parameters in terms of emotional lability, resilience, and vulnerability. We conducted a Monte Carlo study to test the DLO model performance under different empirically relevant conditions in terms of individual characteristics and sampling scheme. We used State-Space Models (SSM) in continuous-time. The results show that, under certain conditions, the DLO model is able to accurately and efficiently recover the parameters underlying the affective dynamics of a single individual. We discuss the results and the theoretical and practical implications of using this model, illustrate how to use it for studying psychological phenomena at the individual level, and provide specific recommendations on how to collect data for this purpose. We also provide a tutorial website and computer code in R to implement this approach
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The spread of COVID-19 has led to the disruption of K-12 education for about 90% of the world's student population. The effects on children's academic development are unknown. We examined how disruption in schooling over three consecutive... more
The spread of COVID-19 has led to the disruption of K-12 education for about 90% of the world's student population. The effects on children's academic development are unknown. We examined how disruption in schooling over three consecutive summers in disadvantaged minority children affects reading and whether an intensive intervention can ameliorate these effects. Our data were collected prior to the COVID-19 pandemic. We applied Latent Change Score models to examine developmental trends in a longitudinal study of reading in 111 economically disadvantaged children, assessed biannually from grades 1 to 4, including 3 summers (for a total of 6 months of school hiatus). The students fell behind the normative population in their ability to understand written texts, a decrease in their relative percentile of 0.25 of a standard deviation each summer, and an effect 3-4 times greater than prior studies suggested. Compared to children in a comparison group, children who received an evidence-based intervention during the school year were better able to maintain their reading scores. These findings provide evidence that disruptions in schooling, for example, those implemented to slow the spread of COVID-19, may have a significant detrimental effect on the reading abilities of disadvantaged children and that children who received a reading intervention were better able to maintain their reading scores during the hiatus. It is critical that policy makers prioritize the allocation of necessary resources to minimize the negative effects on reading this pandemic has wrought on these most disadvantaged children.
Accelerated longitudinal designs (ALDs) allow examining developmental changes over a period of time longer than the duration of the study. In ALDs, participants enter the study at different ages (i.e., different cohorts), and provide... more
Accelerated longitudinal designs (ALDs) allow examining developmental changes over a period of time longer than the duration of the study. In ALDs, participants enter the study at different ages (i.e., different cohorts), and provide measures during a time frame shorter than the total study. They key assumption is that participants from the different cohorts come from the same population and, therefore, can be assumed to share the same general trajectory. The consequences of not meeting that assumption have not been examined systematically. In this article, we propose an approach to detect and control for cohort differences in ALDs using latent change score models in both discrete and continuous time. We evaluated the effectiveness of such a method through a Monte Carlo study. Our results indicate that, in a broad set of empirically relevant conditions, both latent change score (LCS) specifications can adequately estimate cohort effects ranging from very small to very large, with slightly better performance of the continuous-time version. Across all conditions, cohort effects on the asymptotic level (dAs) caused much larger bias than on the latent initial level (d₀). When cohort differences were present, including them in the model led to unbiased estimates. In contrast, not including them led to tenable results only when such differences were not large (d₀ ≤ 1 and dAs ≤ .2). Among the sampling schedules evaluated, those including at least three measurements per participant over 4 years or more led to the best performance. Based on our findings, we offer recommendations regarding study designs and data analysis.
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Background. The typical methylation patterns associated with cancer are hypermethylation at gene promoters and global genome hypomethylation. Aberrant CpG island hypermethylation at promoter regions and global genome hypomethylation have... more
Background. The typical methylation patterns associated with cancer are hypermethylation at gene promoters and global genome hypomethylation. Aberrant CpG island hypermethylation at promoter regions and global genome hypomethylation have not been associated with histological colorectal carcinomas (CRC) subsets. Using Illumina’s 450 k Infinium Human Methylation beadchip, the methylome of 82 CRCs were analyzed, comprising different histological subtypes: 40 serrated adenocarcinomas (SAC), 32 conventional carcinomas (CC) and 10 CRCs showing histological and molecular features of microsatellite instability (hmMSI-H), and, additionally, 35 normal adjacent mucosae. Scores reflecting the overall methylation at 250 bp, 1 kb and 2 kb from the transcription starting site (TSS) were studied. Results. SAC has an intermediate methylation pattern between CC and hmMSI-H for the three genome locations. In addition, the shift from promoter hypermethylation to genomic hypomethylation occurs at a smal...
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The constant growth of computer-delivered instruction makes necessary a careful study of the factors that improve the effects and quality of e-learning. We conducted a web log based correlational study to explore the relationships between... more
The constant growth of computer-delivered instruction makes necessary a careful study of the factors that improve the effects and quality of e-learning. We conducted a web log based correlational study to explore the relationships between amount of teacher's ...
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Latent Change Score models (LCS) are a popular tool for the study of dynamics in longitudinal research. They represent processes in which the short-term dynamics have direct and indirect consequences on the long-term behavior of the... more
Latent Change Score models (LCS) are a popular tool for the study of dynamics in longitudinal research. They represent processes in which the short-term dynamics have direct and indirect consequences on the long-term behavior of the system. However, this dual interpretation of the model parameters is usually overlooked in the literature, and researchers often find it difficult to see the connection between parameters and specific patterns of change. The goal of this paper is to provide a comprehensive examination of the meaning and interpretation of the parameters in LCS models. Importantly, we focus on their relation to the shape of the trajectories and explain how different specifications of the LCS model involve particular assumptions about the mechanisms of change. On a supplementary website, we present an interactive Shiny App that allows users to explore different sets of parameter values and examine their effects on the predicted trajectories. We also include fully explained code to estimate some of the most relevant specifications of the LCS model with the R-packages lavaan and OpenMx.
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Background: Burnout syndrome is very prevalent among healthcare residents. Initiatives addressing workload conditions have had limited impact on burnout. The present study aims to explore the contribution of two emotion regulation... more
Background: Burnout syndrome is very prevalent among healthcare residents. Initiatives addressing workload conditions have had limited impact on burnout. The present study aims to explore the contribution of two emotion regulation strategies, namely emotion suppression and cognitive reevaluation, to residents' burnout, while accounting for workload factors. Methods: Participants were 105 residents (68.6% women; mean age = 27.5, SD = 3.0). They completed measures of workload, burnout, and emotion regulation. The study was cross-sectional. Results: Emotional suppression was associated with higher burnout (depersonalization scale; β = 0.20, p < 0.05, CI 0.15-2.48) and cognitive revaluation was linked to lower burnout (higher personal accomplishment; β = 0.35, p < 0.01, CI 0.16-2.56), even after controlling for demographic and workload factors. We found interaction effects between workload variables (supervisor support and number of patient hours) and emotion regulation (p < 0.05). Conclusions: The relationship between workload, emotion regulation, and burnout seems to be complex. That is, similar work conditions might generate different levels of burnout depending on the resident's emotional regulation strategies. This might partly explain why existing initiatives based on workload changes have had a modest impact on burnout. Results also support including emotion regulation training in prevention and treatment programs targeting burnout during residency.
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Background. Although average based effect size (ES) and percentage of individual changes (PIC) are quite different, they are not independent: larger ESs, lead to higher PICs. However, this association has not been sufficiently explored.... more
Background. Although average based effect size (ES) and percentage of individual changes (PIC) are quite different, they are not independent: larger ESs, lead to higher PICs. However, this association has not been sufficiently explored.
Method. We analyzed this association based on data simulated in the context of a pre-post design, with and without control group. We simulated various distributions, sample sizes, degrees of test-retest reliability, effect sizes, and different variances in pre- and post-test.
Results. The PIC is closely associated with the ES across a wide variety of empirically frequent scenarios. In the “single group pre-post designs”, the linear regression model shows R2 values above 0.90. In the “control group pre-post designs”, the linear regression model shows R2 values above 0.80. These results were found even when the post-test variability differs from that of the pre-test, replicating, extending and generalizing the findings in previous studies.
Conclusions. (1) In the absence of information about the PIC, the ES may be used to estimate such percentage. (2) The PIC is useful to interpret the meaning of the ES measures.
Method. We analyzed this association based on data simulated in the context of a pre-post design, with and without control group. We simulated various distributions, sample sizes, degrees of test-retest reliability, effect sizes, and different variances in pre- and post-test.
Results. The PIC is closely associated with the ES across a wide variety of empirically frequent scenarios. In the “single group pre-post designs”, the linear regression model shows R2 values above 0.90. In the “control group pre-post designs”, the linear regression model shows R2 values above 0.80. These results were found even when the post-test variability differs from that of the pre-test, replicating, extending and generalizing the findings in previous studies.
Conclusions. (1) In the absence of information about the PIC, the ES may be used to estimate such percentage. (2) The PIC is useful to interpret the meaning of the ES measures.
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Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes... more
Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score models (LCS) provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.
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Studying the time-related course of psychological processes is a challenging endeavor, particularly over long developmental periods. Accelerated longitudinal designs (ALD) allow capturing such periods with a limited number of assessments... more
Studying the time-related course of psychological processes is a challenging endeavor, particularly over long developmental periods. Accelerated longitudinal designs (ALD) allow capturing such periods with a limited number of assessments in a much shorter time framework. In ALDs, participants from different cohorts are measured repeatedly but the measures provided by each participant cover only a fraction of the time range of the study. It is then assumed that the common trajectory can be studied by aggregating the information provided by the different converging cohorts. We conducted a Monte Carlo study to evaluate the practical relevance of using discrete- and continuous-time latent change score models for recovering the trajectories of a developmental process from ALD data under different sampling conditions. We focused on exponential trajectories typically found in the development of cognitive abilities from childhood to early adulthood. The results support the appropriateness of ALD designs to study such processes under various conditions of sampling. When all cohorts are drawn from the same population, both discrete- and continuous-time models are able to recover the parameters defining the underlying developmental process. However, discrete-time models yield biased estimates when time lags between observations are not constant. When cohorts are not from the same population and, thus, lack convergence, both types of models show bias in various parameters. We discuss the findings in the context of developmental methodology, encourage researchers to adopt continuous time models to analyze data from ALDs, and provide recommendations about how to implement such research designs.
Research Interests:
Throughout childhood and adolescence, humans experience marked changes in cortical structure and cognitive ability. Cortical thickness and surface area, in particular, have been associated with cognitive ability. Here we ask the question:... more
Throughout childhood and adolescence, humans experience marked changes in cortical structure and cognitive ability. Cortical thickness and surface area, in particular, have been associated with cognitive ability. Here we ask the question: What are the time-related associations between cognitive changes and cortical structure maturation. Identifying a developmental sequence requires multiple measurements of these variables from the same individuals across time. This allows capturing relations among the variables and, thus, finding whether: (a) developmental cognitive changes follow cortical structure maturation, (b) cortical structure maturation follows cognitive changes, or (c) both processes influence each other over time. 430 children and adolescents (age range = 6.01 – 22.28 years) completed the WASI battery and were MRI scanned at three time points separated by ≈ 2 years (mean age t1 = 10.60, SD = 3.58, mean age t2=12.63, SD=3.62, mean age t3=14.49, SD=3.55). Latent Change Score (LCS) models were applied to quantify age-related relationships among the variables of interest. Our results indicate that cortical and cognitive changes related to each other reciprocally. Specifically, the magnitude or rate of the change in each variable at any occasion –and not the previous level– was predictive of later changes. These results were replicated for brain regions selected according to the coordinates identified in the Basten et al.’s (2015) meta-analysis, to the Parieto-Frontal Integration Theory (P-FIT, Jung & Haier, 2007) and to the whole cortex. Potential implications regarding brain plasticity and cognitive enhancement are discussed.
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In a number of scientific fields, researchers need to assess whether a variable has changed between two time points. Average-based change statistics (ABC) such as Cohen's d or Hays' ω 2 evaluate the change in the distributions' center,... more
In a number of scientific fields, researchers need to assess whether a variable has changed between two time points. Average-based change statistics (ABC) such as Cohen's d or Hays' ω 2 evaluate the change in the distributions' center, whereas Individual-based change statistics (IBC) such as the Standardized Individual Difference or the Reliable Change Index evaluate whether each case in the sample experienced a reliable change. Through an extensive simulation study we show that, contrary to what previous studies have speculated, ABC and IBC statistics are closely related. The relation can be assumed to be linear, and was found regardless of sample size, pre-post correlation, and shape of the scores' distribution, both in single group designs and in experimental designs with a control group. We encourage other researchers to use IBC statistics to evaluate their effect sizes because: (a) they allow the identification of cases that changed reliably; (b) they facilitate the interpretation and communication of results; and (c) they provide a straightforward evaluation of the magnitude of empirical effects while avoiding the problems of arbitrary general cutoffs.
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Identifying change at the individual level is an important goal for researchers, educators, and clinicians. We present a set of statistical procedures for identifying individuals who depart from a normative change. Using Latent Change... more
Identifying change at the individual level is an important goal for researchers, educators, and clinicians. We present a set of statistical procedures for identifying individuals who depart from a normative change. Using Latent Change Scores models (LCS), we illustrate how the Individual Likelihood computed from a statistical model for change (IL) and from an alternative unrestricted model (ILsat) can be used to identify atypical trajectories in situations with several measurement occasions. Using LCS and linear regression, we also show how the observed and latent change residuals can be used to identify atypical individual change between 2 measurement occasions. We apply these methods to a measure of general verbal ability (from WISC–R), from a large sample of individuals assessed every 2 years from Grade 1 to 9. We demonstrate the efficiency of these techniques, illustrate their use to identify individual change in longitudinal data, and discuss potential applications in developmental research.
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Cognitive training and brain stimulation studies have suggested that human cognition, primarily working memory and attention control processes, can be enhanced. Some authors claim that gains (i.e., post-test minus pretest scores) from... more
Cognitive training and brain stimulation studies have suggested that human cognition, primarily working memory and attention control processes, can be enhanced. Some authors claim that gains (i.e., post-test minus pretest scores) from such interventions are unevenly distributed among people. The magnification account (expressed by the evangelical “who has will more be given”) predicts that the largest gains will be shown by the most cognitively efficient people, who will also be most effective in exploiting interventions. In contrast, the compensation account (“who has will less be given”) predicts that such people already perform at ceiling, so interventions will yield the largest gains in the least cognitively efficient people. Evidence for this latter account comes from reported negative correlations between the pretest and the training/stimulation gain. In this paper, with the use of mathematical derivations and simulation methods, we show that such correlations are pure statistical artifacts caused by the widely known methodological error called “regression to the mean”. Unfortunately, more advanced methods, such as alternative measures, linear models, and control groups do not guarantee correct assessment of the compensation effect either. The only correct method is to use direct modeling of correlations between latent true measures and gain. As to date no training/stimulation study has correctly used this method to provide evidence in favor of the compensation account, we must conclude that most (if not all) of the evidence should be considered inconclusive.
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The analysis of the relationships between cortical and intellectual development is a complex matter. Greater brain plasticity in brighter individuals has been suggested, but the associations between developmental cortical changes and... more
The analysis of the relationships between cortical and intellectual development is a complex matter. Greater brain plasticity in brighter individuals has been suggested, but the associations between developmental cortical changes and variations in the general factor of intelligence (g) across time at the latent level have not been addressed. For filling this gap, here we relate longitudinal changes in g with developmental changes in cortical thickness and cortical surface area. One hundred and thirty-two children and adolescents representative of the population from the Pediatric MRI Data Repository completed the Wechsler Abbreviated Scale of Intelligence in three time points and MRI scans were also obtained (mean inter-registration interval ≈ 2 years, age range = 6.1 to 21.3 years). Longitudinal latent variable analyses revealed an increase in g scores amounting to a full standard deviation on average. Intelligence differences estimated at the latent level were significantly correlated related with cortical changes. Older individuals showed greater decrease in cortical values along with smaller increase in intelligence. Furthermore, thickness preservation in brighter individuals was observed at early adolescence (10–14 years).
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Gulliksen (1950) established the distinction between pure power and pure speed intelligence tests. Most standardized measures combine power and speed requirements. The speediness component affects test scores' reliability and validity,... more
Gulliksen (1950) established the distinction between pure power and pure speed intelligence tests. Most standardized measures combine power and speed requirements. The speediness component affects test scores' reliability and validity, since it involves variance not due to the mental ability of interest. Here we propose the use of the Stafford's Speediness Quotient (SQ, 1971) for identifying items biased by the speed component. We developed two converging methods based on Structural Equation Modeling (SEM) to assess the validity of the SQ index. The methods concurrently identify items substantially affected by speededness in three standardized fluid intelligence tests with different speed requirements. Basing on the SQ at the item level, a simple strategy for separating the power and speed components of mental ability tests applied under time constraints is proposed. This strategy allows an estimation of the respondents' level uncontaminated by the speed unwanted variance. This procedure only requires right/wrong responses (e.g., does not need any information external to the test, such as response times) and it is appropriate for medium-small sized samples. A rule of thumb is suggested for identifying items affected by speediness. The simplicity of the proposed procedure allows its use in applied settings for detecting and controlling speed-related variance in tests' scores.
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Objectives: (i) To analyze if general cognitive performance, perceived health and depression are predictors of Subjective Memory Complaints (SMC) contrasting their effect sizes; (ii) to analyze the relationship between SMC and objective... more
Objectives: (i) To analyze if general cognitive performance, perceived health and depression are predictors of Subjective Memory Complaints (SMC) contrasting their effect sizes; (ii) to analyze the relationship between SMC and objective memory by comparing a test that measures memory in daily life and a classical test of associated pairs; (iii) to examine if different subgroups, formed according to the MFE score, might have different behaviors regarding the studied variables.
Methods: Sample: 3921 community-dwelling people (mean age 70.41 ± 4.70) without cognitive impairment. Consecutive non-probabilistic recruitment. Assessment: Mini Cognitive Exam (MCE), daily memory Rivermead Behavioural Memory Test (RBMT), Paired Associates Learning (PAL), Geriatric Depression Scale (GDS), Nottingham Health Profile (NHP). Dependent variable: Memory Failures Everyday Questionnaire (MFE).
Results: Two different dimensions to explain SMC were found: One subjective (MFE, GDS, NHP) and other objective (RBMT, PAL, MCE), the first more strongly associated with SMC. SMC predictors were NHP, GDS, RBMT and PAL, in this order according to effect size. Considering MFE scores we subdivided the sample into three groups (low, medium, higher scores): low MFE group was associated with GDS; medium, with GDS, NPH and RBMT, and higher, with age as well. Effect size for every variable tended to grow as the MFE score was higher.
Conclusion: SMC were associated with both health profile and depressive symptoms and, in a lesser degree, with memory and overall cognitive performance. In people with fewer SMC, these are only associated with depressive symptomatology. More SMC are associated with depression, poor health perception and lower memory.
Methods: Sample: 3921 community-dwelling people (mean age 70.41 ± 4.70) without cognitive impairment. Consecutive non-probabilistic recruitment. Assessment: Mini Cognitive Exam (MCE), daily memory Rivermead Behavioural Memory Test (RBMT), Paired Associates Learning (PAL), Geriatric Depression Scale (GDS), Nottingham Health Profile (NHP). Dependent variable: Memory Failures Everyday Questionnaire (MFE).
Results: Two different dimensions to explain SMC were found: One subjective (MFE, GDS, NHP) and other objective (RBMT, PAL, MCE), the first more strongly associated with SMC. SMC predictors were NHP, GDS, RBMT and PAL, in this order according to effect size. Considering MFE scores we subdivided the sample into three groups (low, medium, higher scores): low MFE group was associated with GDS; medium, with GDS, NPH and RBMT, and higher, with age as well. Effect size for every variable tended to grow as the MFE score was higher.
Conclusion: SMC were associated with both health profile and depressive symptoms and, in a lesser degree, with memory and overall cognitive performance. In people with fewer SMC, these are only associated with depressive symptomatology. More SMC are associated with depression, poor health perception and lower memory.
Research Interests:
Background Serrated adenocarcinoma (SAC) is a recently recognized colorectal cancer (CRC) subtype accounting for 7.5–8.7 % of CRCs. It has been shown that SAC has a worse prognosis and different histological and molecular features... more
Background
Serrated adenocarcinoma (SAC) is a recently recognized colorectal cancer (CRC) subtype accounting for 7.5–8.7 % of CRCs. It has been shown that SAC has a worse prognosis and different histological and molecular features compared to conventional carcinoma (CC) but, to date, there is no study analysing its methylome profile.
Results
The methylation status of 450,000 CpG sites using the Infinium Human Methylation 450 BeadChip array was investigated in 103 colorectal specimens, including 39 SACs and 34 matched CCs, from Spanish and Finnish patients. Microarray data showed a higher representation of morphogenesis-, neurogenesis-, cytoskeleton- and vesicle transport-related functions and also significant differential methylation of 15 genes, including the iodothyronine deiodinase DIO3 and the forkhead family transcription factor FOXD2 genes which were validated at the CpG, mRNA and protein level using pyrosequencing, methylation-specific PCR, quantitative polymerase chain reaction (qPCR) and immunohistochemistry. A quantification study of the methylation status of CpG sequences in FOXD2 demonstrated a novel region controlling gene expression. Moreover, differences in these markers were also evident when comparing SAC with CRC showing molecular and histological features of high-level microsatellite instability.
Conclusions
This methylome study demonstrates distinct epigenetic regulation patterns in SAC which are consistent to previous expression profile studies and that DIO3 and FOXD2 might be molecular targets for a specific histology-oriented treatment of CRC.
Serrated adenocarcinoma (SAC) is a recently recognized colorectal cancer (CRC) subtype accounting for 7.5–8.7 % of CRCs. It has been shown that SAC has a worse prognosis and different histological and molecular features compared to conventional carcinoma (CC) but, to date, there is no study analysing its methylome profile.
Results
The methylation status of 450,000 CpG sites using the Infinium Human Methylation 450 BeadChip array was investigated in 103 colorectal specimens, including 39 SACs and 34 matched CCs, from Spanish and Finnish patients. Microarray data showed a higher representation of morphogenesis-, neurogenesis-, cytoskeleton- and vesicle transport-related functions and also significant differential methylation of 15 genes, including the iodothyronine deiodinase DIO3 and the forkhead family transcription factor FOXD2 genes which were validated at the CpG, mRNA and protein level using pyrosequencing, methylation-specific PCR, quantitative polymerase chain reaction (qPCR) and immunohistochemistry. A quantification study of the methylation status of CpG sequences in FOXD2 demonstrated a novel region controlling gene expression. Moreover, differences in these markers were also evident when comparing SAC with CRC showing molecular and histological features of high-level microsatellite instability.
Conclusions
This methylome study demonstrates distinct epigenetic regulation patterns in SAC which are consistent to previous expression profile studies and that DIO3 and FOXD2 might be molecular targets for a specific histology-oriented treatment of CRC.
Research Interests:
As a general rule, the repeated administration of tests measuring a given cognitive ability in the same participants reveals increased scores. This brings to life the well-known practice effect and it must be taken into account in... more
As a general rule, the repeated administration of tests measuring a given cognitive ability in the same participants reveals increased scores. This brings to life the well-known practice effect and it must be taken into account in research aimed at the proper assessment of changes after the completion of cognitive training programs. Here we focus in one specific research question: Are changes in test scores accounted for by the tapped underlying cognitive construct/factor? The evaluation of the factor of interest by several measures is required for that purpose. 477 university students completed twice a battery of four heterogeneous standardized intelligence tests within a time lapse of four weeks. Between the pre-test and the post-test sessions, some participants completed eighteen practice sessions based on memory span tasks, other participants completed eighteen practice sessions based on processing speed tasks, and a third group of participants did nothing between testing sessions. The three groups showed remarkable changes in test scores from the pre-test to the post-test intelligence session. However, results from multi-group longitudinal latent variable analyses revealed that the identified latent factor tapped by the specific intelligence measures fails to account for the observed changes.
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A recent report has shown that the relationship, at the latent variable level, between fluid ability and working memory capacity is affected by the time allowed for completing problems requiring the former (Chuderski, 2013): the greater... more
A recent report has shown that the relationship, at the latent variable level, between fluid ability and working memory capacity is affected by the time allowed for completing problems requiring the former (Chuderski, 2013): the greater the time, the lower the relationship. The underlying argument is that untimed administration of fluid ability problems compensates working memory capacity limitations. The present report analyzes a group of three hundred and two participants that completed a set of three fluid tests and six working memory tasks. Latent variable analyses revealed consistent correlations (weighted average r = .86) between fluid ability and working memory capacity irrespective of administration times. Furthermore, the lowest difference in fluid ability between individuals with high and low working memory capacity was observed for the highly speeded condition. Their difference was greater when increased time was allowed for completing the fluid problems. Therefore, the relationship between fluid ability and working memory capacity appeals to underlying general common mechanisms unrelated with time constraints. Here we suggest that the reliability by which the relevant information can be preserved in the short-term for successful on-line processing seems a likely candidate.
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Chapter prepared for the Cambridge Handbook of Research Methods in Clinical Psychology. Edited by A. Wright & M. N. Hallquist. We describe models for analyzing data from dyadic systems such as therapist-client, mother-child, or romantic... more
Chapter prepared for the Cambridge Handbook of Research Methods in Clinical Psychology. Edited by A. Wright & M. N. Hallquist. We describe models for analyzing data from dyadic systems such as therapist-client, mother-child, or romantic partners, among others. We define key characteristics of dyadic systems and then identify clinical research questions related to dyadic systems and processes that unfold over time. We use these questions to select a set of statistical models and data analytic techniques for answering clinical research questions related to dyadic research.
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Estrada, E. (2018). Power Tests. In B. B. Frey, The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2455 Teller Road, Thousand Oaks, California 91320: SAGE Publications, Inc.... more
Estrada, E. (2018). Power Tests. In B. B. Frey, The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2455 Teller Road, Thousand Oaks, California 91320: SAGE Publications, Inc. https://doi.org/10.4135/9781506326139.n533
Research Interests:
Estrada, E. (2018). Speeded Tests. In B. B. Frey, The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2455 Teller Road, Thousand Oaks, California 91320: SAGE Publications, Inc.... more
Estrada, E. (2018). Speeded Tests. In B. B. Frey, The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2455 Teller Road, Thousand Oaks, California 91320: SAGE Publications, Inc. https://doi.org/10.4135/9781506326139.n652