Sexo, edad y sintomatología depresiva en la adolescencia / Sex, age and depressive symptoms in adolescence

El objetivo de este estudio fue analizar la relación del sexo y la edad con la sintomatología depresiva en adolescentes. Los participantes fueron 1.212 adolescentes, de 1º (53,9%) y 2º (46,1%) curso de la Educación Secundaria Obligatoria, pertenecientes a siete centros educativos de la Región de Murcia (España). Participaron también 104 padres. El porcentaje de adolescentes con sintomatología depresiva clínicamente significativa es similar al encontrado en otros estudios en España. Las chicas presentaron mayor sintomatología depresiva. La diferencia de sexo fue mayor en el grupo de alta sintomatología depresiva. La sintomatología depresiva aumentó con la edad en consonancia con investigaciones previas. La diferencia se puso de relieve en los autoinformes de los adolescentes, pero no en los informes de los padres. Se concluye la relevancia de tomar en consideración el sexo y la edad al elaborar programas de prevención de la depresión para adolescentes. (AU)

A Reliability Generalization Meta-Analysis of the Padua Inventory-Revised (PI-R) / Meta-análisis de generalización de la fiabilidad del Padua Inventory-Revised (PI-R)

Background/Objective: The Padua Inventory-Revised (PI-R) is a widely applied instrument to measure obsessive-compulsive symptoms in clinical and nonclinical samples. We conducted a reliability generalization meta-analysis on the PI-R. Method: An exhaustive literature search yielded 118 empirical studies that had applied the PI-R, from which 30 studies (33 samples) reported an original reliability estimate. Results: Assuming a random-effects model, the average internal consistency reliability (Cronbach's alpha) was .92 (95% CI [.91, .93]) for the total scores, and ranged from .74 to .89 for the subscales. Assuming mixed-effects models, moderator analyses showed a positive statistically significant association between the standard deviation of the total scores and the reliability coefficients (p = .002; R2 = .38). Conclusions: In terms of reliability, the PI-R scale was found to be adequate for both research and clinical purposes, although exhibiting large heterogeneity across studies. Future empirical studies using the PI-R should be required to provide at least one reliability estimate based on their own data

Antecedentes/Objetivo: El Padua Inventory-Revised (PI-R) es un instrumento ampliamente utilizado para medir los síntomas obsesivo-compulsivos en muestras clínicas y no clínicas. Llevamos a cabo un meta-análisis de generalización de la fiabilidad del PI-R. Método: Una búsqueda exhaustiva de la literatura arrojó 118 estudios empíricos que habían aplicado el PI-R, de los cuales 30 estudios (33 muestras) reportaron una estimación propia de la fiabilidad. Resultados: Asumiendo un modelo de efectos aleatorios, la fiabilidad en términos de consistencia interna promedio (alfa de Cronbach) fue de 0,92 (IC del 95% [0,91, 0,93]) para las puntuaciones totales, y osciló entre 0,74 y 0,89 para las subescalas. Asumiendo modelos de efectos mixtos, los análisis de moderadores mostraron una relación positiva estadísticamente significativa entre la desviación típica de las puntuaciones totales y los coeficientes de fiabilidad (p = 0,002; R2 = 0,38). Conclusiones: En términos de fiabilidad, se encontró que el PI-R es adecuado tanto para fines clínicos como de investigación, aunque con una alta heterogeneidad entre los estudios. Es necesario que los estudios empíricos futuros que apliquen el PI-R proporcionen al menos una estimación de la fiabilidad basada en sus propios datos.

A Reliability Generalization Meta-analysis of the Padua Inventory-Revised (PI-R).

Background/Objective: The Padua Inventory-Revised (PI-R) is a widely applied instrument to measure obsessive-compulsive symptoms in clinical and nonclinical samples. We conducted a reliability generalization meta-analysis on the PI-R. Method: An exhaustive literature search yielded 118 empirical studies that had applied the PI-R, from which 30 studies (33 samples) reported an original reliability estimate. Results: Assuming a random-effects model, the average internal consistency reliability (Cronbach's alpha) was .92 (95% CI [.91, .93]) for the total scores, and ranged from .74 to .89 for the subscales. Assuming mixed-effects models, moderator analyses showed a positive statistically significant association between the standard deviation of the total scores and the reliability coefficients (p = .002; R2  = .38). Conclusions: In terms of reliability, the PI-R scale was found to be adequate for both research and clinical purposes, although exhibiting large heterogeneity across studies. Future empirical studies using the PI-R should be required to provide at least one reliability estimate based on their own data.

Antecedentes/Objetivo: El Padua Inventory-Revised (PI-R) es un instrumento ampliamente utilizado para medir los síntomas obsesivo-compulsivos en muestras clínicas y no clínicas. Llevamos a cabo un meta-análisis de generalización de la fiabilidad del PI-R. Método: Una búsqueda exhaustiva de la literatura arrojó 118 estudios empíricos que habían aplicado el PI-R, de los cuales 30 estudios (33 muestras) reportaron una estimación propia de la fiabilidad. Resultados: Asumiendo un modelo de efectos aleatorios, la fiabilidad en términos de consistencia interna promedio (alfa de Cronbach) fue de 0,92 (IC del 95% [0,91, 0,93]) para las puntuaciones totales, y osciló entre 0,74 y 0,89 para las subescalas. Asumiendo modelos de efectos mixtos, los análisis de moderadores mostraron una relación positiva estadísticamente significativa entre la desviación típica de las puntuaciones totales y los coeficientes de fiabilidad (p = 0,002; R2  = 0,38). Conclusiones: En términos de fiabilidad, se encontró que el PI-R es adecuado tanto para fines clínicos como de investigación, aunque con una alta heterogeneidad entre los estudios. Es necesario que los estudios empíricos futuros que apliquen el PI-R proporcionen al menos una estimación de la fiabilidad basada en sus propios datos.

Improving the reporting quality of reliability generalization meta-analyses: The REGEMA checklist.

Reliability generalization (RG) is a meta-analytic approach that aims to characterize how reliability estimates from the same test vary across different applications of the instrument. With this purpose RG meta-analyses typically focus on a particular test and intend to obtain an overall reliability of test scores and to investigate how the composition and variability of the samples affect reliability. Although several guidelines have been proposed in the meta-analytic literature to help authors improve the reporting quality of meta-analyses, none of them were devised for RG meta-analyses. The purpose of this investigation was to develop REGEMA (REliability GEneralization Meta-Analysis), a 30-item checklist (plus a flow chart) adapted to the specific issues that the reporting of an RG meta-analysis must take into account. Based on previous checklists and guidelines proposed in the meta-analytic arena, a first version was elaborated by applying the nominal group methodology. The resulting instrument was submitted to a list of independent meta-analysis experts and, after discussion, the final version of the REGEMA checklist was reached. In a pilot study, four pairs of coders applied REGEMA to a random sample of 40 RG meta-analyses in Psychology, and results showed satisfactory inter-coder reliability. REGEMA can be used by: (a) meta-analysts conducting or reporting an RG meta-analysis and aiming to improve its reporting quality; (b) consumers of RG meta-analyses who want to make informed critical appraisals of their reporting quality, and (c) reviewers and editors of journals who are considering submissions where an RG meta-analysis was reported for potential publication.

The Padua Inventory-Washington State University Revision of Obsessions and Compulsions: A Reliability Generalization Meta-Analysis.

The Padua Inventory-Washington State University Revision (PI-WSUR) is a frequently used test to assess obsessive-compulsive symptoms in screening and clinical contexts. A reliability generalization meta-analysis was carried out to estimate the average reliability of the PI-WSUR scores and its subscales and to search for characteristics of the studies that can explain the heterogeneity among reliability coefficients. A total of 124 independent samples reported some coefficient alpha or test-retest correlation with the data at hand for the PI-WSUR scores. The average internal consistency reliability of the PI-WSUR total scores was .929 (95% CI [.922, .936]), and for the subscales, the means ranged from .792 to .900. The test-retest reliability for PI-WSUR total scores was .767 (95% CI [.700, .820]), with the subscales ranging from .540 to .790. Moderator analyses revealed a positive relationship between the standard deviation of PI-WSUR total scores and alpha coefficients, as well as higher reliability estimates for the original version of the test and for studies from North America. The reliability induction rate for the PI-WSUR was 53.7%. Regarding reliability, the PI-WSUR ranks among the best scales for assessing obsessive-compulsive symptoms. Internal consistency reliability was excellent for the PI-WSUR total score and good for the subscales.

Estimation of an overall standardized mean difference in random-effects meta-analysis if the distribution of random effects departs from normal.

The random-effects model, applied in most meta-analyses nowadays, typically assumes normality of the distribution of the effect parameters. The purpose of this study was to examine the performance of various random-effects methods (standard method, Hartung's method, profile likelihood method, and bootstrapping) for computing an average effect size estimate and a confidence interval (CI) around it, when the normality assumption is not met. For comparison purposes, we also included the fixed-effect model. We manipulated a wide range of conditions, including conditions with some degree of departure from the normality assumption, using Monte Carlo simulation. To simulate realistic scenarios, we chose the manipulated conditions from a systematic review of meta-analyses on the effectiveness of psychological treatments. We compared the performance of the different methods in terms of bias and mean squared error of the average effect estimators, empirical coverage probability and width of the CIs, and variability of the standard errors. Our results suggest that random-effects methods are largely robust to departures from normality, with Hartung's profile likelihood methods yielding the best performance under suboptimal conditions.

Recomendaciones para el Reporte de Revisiones Sistemáticas y Meta-análisis / Guidelines for Reporting Systematic Reviews and Meta-analyses

El meta-análisis es una metodología esencial que permite a los investigadores sintetizar la evidencia científica disponible sobre una determinada cuestión de investigación. Debido a su amplia aplicabilidad en la mayoría de los campos de investigación aplicados, es realmente importante que los meta-análisis se escriban y se reporten de manera apropiada. En este artículo proponemos pautas para reportar los resultados de un meta-análisis en una revista científica como Anales de Psicología. Concretamente, se detalla la estructura para reportar un meta-análisis siguiendo sus fases. Además, proporcionamos recomendaciones relacionadas con las tareas usuales en meta-análisis. Un meta-análisis reciente centrado en el campo de la psicología es usado para ilustrar las guías propuestas. Finalmente, presentamos algunas observaciones finales

Meta-analysis is an essential methodology that allows researchers to synthesize the scientific evidence available on a given research question. Due to its wide applicability in most applied research fields, it is really important that meta-analyses be written and reported appropriately. In this paper we propose some guidelines to report the results of a meta-analysis in a scientific journal as Annals of Psychology. Concretely, the structure for reporting a meta-analysis following its different stages is detailed. In addition, some recommendations related to the usual tasks when conducting a meta-analysis are provided. A recent meta-analysis focused on the psychological field is used to illustrate the guidelines proposed. Finally, some concluding remarks are presented

A methodological review of meta-analyses of the effectiveness of clinical psychology treatments.

This article presents a methodological review of 54 meta-analyses of the effectiveness of clinical psychological treatments, using standardized mean differences as the effect size index. We statistically analyzed the distribution of the number of studies of the meta-analyses, the distribution of the sample sizes in the studies of each meta-analysis, the distribution of the effect sizes in each of the meta-analyses, the distribution of the between-studies variance values, and the Pearson correlations between effect size and sample size in each meta-analysis. The results are presented as a function of the type of standardized mean difference: posttest standardized mean difference, standardized mean change from pretest to posttest, and standardized mean change difference between groups. These findings will help researchers design future Monte Carlo and theoretical studies on the performance of meta-analytic procedures, based on the manipulation of realistic model assumptions and parameters of the meta-analyses. Furthermore, the analysis of the distribution of the mean effect sizes through the meta-analyses provides a specific guide for the interpretation of the clinical significance of the different types of standardized mean differences within the field of the evaluation of clinical psychological interventions.

A Reliability Generalization Meta-Analysis of the Padua Inventory of Obsessions and Compulsions.

The Padua Inventory (PI) of obsessions and compulsions is one of the most usually applied tests to assess obsessive-compulsive symptomatology in research contexts as well as for clinical and screening purposes. A reliability generalization meta-analysis was accomplished to estimate the average reliability of the PI scores and to search for characteristics of the samples and studies that can explain the variability among reliability estimates. An exhaustive literature search enabled us to select 39 studies (53 independent samples) that reported alpha and/or test-retest coefficients with the data at hand for the PI total score and subscales. An excellent average coefficient alpha was found for the PI total score (M = .935; 95%CI = .922-.949) and for Impaired Mental Control subscale (M = .911; 95%CI = .897-.924), being good for Contamination (M = .861; 95%CI = .841-.882) and Checking (M = .880; 95%CI = .856-.903), and fair for Urges and Worries (M = .783; 95%CI = .745-.822). The average test-retest reliability for PI total score was also satisfactory (M = .835; 95%CI = .782-.877). Moderator analyses showed larger coefficients alpha for larger standard deviation of the PI total scores (p = .0005; R 2 = .46), for adapted versions of the test (p = .002; R 2 = .32), and for samples composed of clinical participants (p = .066; R 2 = .10). The practical implications of these results are discussed as well as the need for researchers to report reliability estimates with the data at hand.

Analysis of categorical moderators in mixed-effects meta-analysis: Consequences of using pooled versus separate estimates of the residual between-studies variances.

Subgroup analyses allow us to examine the influence of a categorical moderator on the effect size in meta-analysis. We conducted a simulation study using a dichotomous moderator, and compared the impact of pooled versus separate estimates of the residual between-studies variance on the statistical performance of the Q B(P) and Q B(S) tests for subgroup analyses assuming a mixed-effects model. Our results suggested that similar performance can be expected as long as there are at least 20 studies and these are approximately balanced across categories. Conversely, when subgroups were unbalanced, the practical consequences of having heterogeneous residual between-studies variances were more evident, with both tests leading to the wrong statistical conclusion more often than in the conditions with balanced subgroups. A pooled estimate should be preferred for most scenarios, unless the residual between-studies variances are clearly different and there are enough studies in each category to obtain precise separate estimates.

A reliability generalization meta-analysis of the padua inventory of obsessions and compulsions

The Padua Inventory (PI) of obsessions and compulsions is one of the most usually applied tests to assess obsessive-compulsive symptomatology in research contexts as well as for clinical and screening purposes. A reliability generalization meta-analysis was accomplished to estimate the average reliability of the PI scores and to search for characteristics of the samples and studies that can explain the variability among reliability estimates. An exhaustive literature search enabled us to select 39 studies (53 independent samples) that reported alpha and/or test-retest coefficients with the data at hand for the PI total score and subscales. An excellent average coefficient alpha was found for the PI total score (M = .935; 95%CI = .922-.949) and for Impaired Mental Control subscale (M = .911; 95%CI = .897-.924), being good for Contamination (M = .861; 95%CI = .841-.882) and Checking (M = .880; 95%CI = .856-.903), and fair for Urges and Worries (M = .783; 95%CI = .745-.822). The average test-retest reliability for PI total score was also satisfactory (M = .835; 95%CI = .782-.877). Moderator analyses showed larger coefficients alpha for larger standard deviation of the PI total scores (p = .0005; R2 = .46), for adapted versions of the test (p = .002; R2 = .32), and for samples composed of clinical participants (p = .066; R2 = .10). The practical implications of these results are discussed as well as the need for researchers to report reliability estimates with the data at hand

No disponible

A comparison of procedures to test for moderators in mixed-effects meta-regression models.

Several alternative methods are available when testing for moderators in mixed-effects meta-regression models. A simulation study was carried out to compare different methods in terms of their Type I error and statistical power rates. We included the standard (Wald-type) test, the method proposed by Knapp and Hartung (2003) in 2 different versions, the Huber-White method, the likelihood ratio test, and the permutation test in the simulation study. These methods were combined with 7 estimators for the amount of residual heterogeneity in the effect sizes. Our results show that the standard method, applied in most meta-analyses up to date, does not control the Type I error rate adequately, sometimes leading to overly conservative, but usually to inflated, Type I error rates. Of the different methods evaluated, only the Knapp and Hartung method and the permutation test provide adequate control of the Type I error rate across all conditions. Due to its computational simplicity, the Knapp and Hartung method is recommended as a suitable option for most meta-analyses.

Reliability generalization study of the Yale-Brown Obsessive-Compulsive Scale for children and adolescents.

The Yale-Brown Obsessive-Compulsive Scale for children and adolescents (CY-BOCS) is a frequently applied test to assess obsessive-compulsive symptoms. We conducted a reliability generalization meta-analysis on the CY-BOCS to estimate the average reliability, search for reliability moderators, and propose a predictive model that researchers and clinicians can use to estimate the expected reliability of the CY-BOCS scores. A total of 47 studies reporting a reliability coefficient with the data at hand were included in the meta-analysis. The results showed good reliability and a large variability associated to the standard deviation of total scores and sample size.

The Yale-Brown Obsessive Compulsive Scale: A Reliability Generalization Meta-Analysis.

The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) is the most frequently applied test to assess obsessive compulsive symptoms. We conducted a reliability generalization meta-analysis on the Y-BOCS to estimate the average reliability, examine the variability among the reliability estimates, search for moderators, and propose a predictive model that researchers and clinicians can use to estimate the expected reliability of the Y-BOCS. We included studies where the Y-BOCS was applied to a sample of adults and reliability estimate was reported. Out of the 11,490 references located, 144 studies met the selection criteria. For the total scale, the mean reliability was 0.866 for coefficients alpha, 0.848 for test-retest correlations, and 0.922 for intraclass correlations. The moderator analyses led to a predictive model where the standard deviation of the total test and the target population (clinical vs. nonclinical) explained 38.6% of the total variability among coefficients alpha. Finally, clinical implications of the results are discussed.

Meta-analysis of multiple outcomes: a multilevel approach.

In meta-analysis, dependent effect sizes are very common. An example is where in one or more studies the effect of an intervention is evaluated on multiple outcome variables for the same sample of participants. In this paper, we evaluate a three-level meta-analytic model to account for this kind of dependence, extending the simulation results of Van den Noortgate, López-López, Marín-Martínez, and Sánchez-Meca Behavior Research Methods, 45, 576-594 (2013) by allowing for a variation in the number of effect sizes per study, in the between-study variance, in the correlations between pairs of outcomes, and in the sample size of the studies. At the same time, we explore the performance of the approach if the outcomes used in a study can be regarded as a random sample from a population of outcomes. We conclude that although this approach is relatively simple and does not require prior estimates of the sampling covariances between effect sizes, it gives appropriate mean effect size estimates, standard error estimates, and confidence interval coverage proportions in a variety of realistic situations.

Estimation of the predictive power of the model in mixed-effects meta-regression: A simulation study.

Several methods are available to estimate the total and residual amount of heterogeneity in meta-analysis, leading to different alternatives when estimating the predictive power in mixed-effects meta-regression models using the formula proposed by Raudenbush (1994, 2009). In this paper, a simulation study was conducted to compare the performance of seven estimators of these parameters under various realistic scenarios in psychology and related fields. Our results suggest that the number of studies (k) exerts the most important influence on the accuracy of the results, and that precise estimates of the heterogeneity variances and the model predictive power can only be expected with at least 20 and 40 studies, respectively. Increases in the average within-study sample size (N¯) also improved the results for all estimators. Some differences among the accuracy of the estimators were observed, especially under adverse (small k and N¯) conditions, while the results for the different methods tended to convergence for more optimal scenarios.

Three-level meta-analysis of dependent effect sizes.

Although dependence in effect sizes is ubiquitous, commonly used meta-analytic methods assume independent effect sizes. We describe and illustrate three-level extensions of a mixed effects meta-analytic model that accounts for various sources of dependence within and across studies, because multilevel extensions of meta-analytic models still are not well known. We also present a three-level model for the common case where, within studies, multiple effect sizes are calculated using the same sample. Whereas this approach is relatively simple and does not require imputing values for the unknown sampling covariances, it has hardly been used, and its performance has not been empirically investigated. Therefore, we set up a simulation study, showing that also in this situation, a three-level approach yields valid results: Estimates of the treatment effects and the corresponding standard errors are unbiased.

Meta-análisis e intervención psicosocial basada en la evidencia / Meta-analysis and evidence-based psychosocial intervention

Las intervenciones psicosociales que se aplican en la práctica profesional deberían ser aquéllas que han recibido las mejores evidencias científicas acerca de su eficacia. El enfoque de la Intervención Psicosocial Basada en la Evidencia es una herramienta metodológica que pretende concienciar a los profesionales y a los gestores políticos de la necesidad de que su práctica profesional esté guiada por las mejores evidencias. Para este propósito las revisiones sistemáticas y los meta-análisis de los estudios evaluativos empíricos juegan un papel primordial, ya que permiten sintetizar los resultados de numerosas investigaciones sobre un mismo problema para determinar cuáles son los mejores tratamientos o intervenciones para resolverlo. En este artículo se presenta una panorámica de qué son los meta-análisis y qué información pueden ofrecer a la práctica profesional. Se presentan las fases en que se lleva a cabo un meta-análisis:(a) formulación del problema, (b) búsqueda de los estudios, (c) codificación de los estudios, (d) cálculo del tamaño del efecto, (e) técnicas estadísticas de integración y (f) publicación del estudio. El alcance de los meta-análisis y de sus resultados se ilustra con un ejemplo y se discuten sus implicaciones para la práctica profesional (AU)

Psychosocial interventions that are applied in practice should be those that have received the best scientific evidence about their effectiveness. Evidence-Based Psychosocial Intervention is a methodological tool that aims to raise awareness among professionals and policy makers of the need for professional practice to be guided by the best evidence. For this purpose, systematic reviews and meta-analyses of empirical evaluation studies play an important role as they allow us to synthesize the results of numerous studies on the same issue to determine which are the best treatments and interventions for solving the problem. This article presents an overview of the meta-analyses and the information they can provide for professional practice. The phases in which a meta-analysis is carried out are outlined as follows: (a) formulating the problem, (b) searching for the studies, (c) coding the studies, (d) calculating the effect size,(e) statistical techniques of integration and (f) publishing the study. The scope of meta-analyses and their results are illustrated with an example and their implications for professional practice are discussed (AU)

Psychological treatment of panic disorder with or without agoraphobia: a meta-analysis.

Although the efficacy of psychological treatment for panic disorder (PD) with or without agoraphobia has been the subject of a great deal of research, the specific contribution of techniques such as exposure, cognitive therapy, relaxation training and breathing retraining has not yet been clearly established. This paper presents a meta-analysis applying random- and mixed-effects models to a total of 65 comparisons between a treated and a control group, obtained from 42 studies published between 1980 and 2006. The results showed that, after controlling for the methodological quality of the studies and the type of control group, the combination of exposure, relaxation training, and breathing retraining gives the most consistent evidence for treating PD. Other factors that improve the effectiveness of treatments are the inclusion of homework during the intervention and a follow-up program after it has finished. Furthermore, the treatment is more effective when the patients have no comorbid disorders and the shorter the time they have been suffering from the illness. Publication bias and several methodological factors were discarded as a threat against the validity of our results. Finally the implications of the results for clinical practice and for future research are discussed.

Psychological treatment of obsessive-compulsive disorder: a meta-analysis.

The benefits of cognitive-behavioral treatment for obsessive-compulsive disorder (OCD) have been evidenced by several meta-analyses. However, the differential effectiveness of behavioral and cognitive approaches has shown inconclusive results. In this paper a meta-analysis on the effectiveness of psychological treatment for OCD is presented by applying random- and mixed-effects models. The literature search enabled us to identify 19 studies published between 1980 and 2006 that fulfilled our selection criteria, giving a total of 24 independent comparisons between a treated and a control group. The effect size index was the standardized mean difference in the posttest. The effect estimates for exposure with response prevention (ERP) alone (d(+)=1.127), cognitive restructuring (CR) alone (d(+)=1.090), and ERP plus CR (d(+)=0.998) were very similar, although the effect estimate for CR alone was based on only three comparisons. Therapist-guided exposure was better than therapist-assisted self-exposure, and exposure in vivo combined with exposure in imagination was better than exposure in vivo alone. The relationships of subject, methodological and extrinsic variables with effect size were also examined, and an analysis of publication bias was carried out. Finally, the implications of the results for clinical practice and for future research in this field were discussed.