Long-term changes in delay discounting following a smoking cessation treatment for patients with depression.

BACKGROUND: Delay discounting (DD) has been identified as a trans-disorder process underlying addictive behaviors, including smoking. Previous studies have evaluated how different treatments for drug dependence have affected DD, showing mixed results. Furthermore, no study has examined the effects of changes in depression on DD rates. The aim of this study was to evaluate the impact of treatment type: cognitive behavioral treatment (CBT), CBT + behavioral activation (BA), or CBT + BA + contingency management (CM), and changes in smoking status and depression on DD rates in long-term follow-up among a sample of treatment-seeking smokers with depression. METHODS: Participants were 180 treatment-seeking smokers with depression who were randomly assigned to one of the following treatment conditions: CBT (n = 60), CBT + BA (n = 60), and CBT + BA + CM (n = 60). Depressive symptomatology and major depression diagnosis were evaluated through the BDI-II and the SCID-I of the DSM-IV-TR. DD rates were assessed using the DD task with hypothetical monetary rewards. Smoking status, DD, and depressive symptomatology were collected at baseline, at end-of-treatment and at one-, two-, three-, and six-month follow-ups. RESULTS: CM for smoking cessation reduces DD rates (p = .0094). Smoking abstinence (p = .0024) and reduction in depressive symptoms (p = .0437) were associated with decreases in DD rates in long-term follow-up. CONCLUSIONS: CM interventions for smoking cessation, smoking abstinence, and the improvement of depression contribute to reductions in DD over time.

Additive effectiveness of contingency management on cognitive behavioural treatment for smokers with depression: Six-month abstinence and depression outcomes.

BACKGROUND: Depression and smoking co-occur at high rates and there is a lack of evidence on the efficacy of treatments specifically tailored to this population. This randomized controlled trial sought to compare the effectiveness of cognitive behavioural treatment (CBT) combined with behavioural activation (BA) and the same treatment protocol plus contingency management (CM). METHODS: A sample of 120 adult smokers (70.8%: females) with severe depressive symptoms were randomly allocated to: CBT + BA (n = 60) or CBT + BA + CM (n = 60). Smoking and depression outcomes were reported at end of treatment, 1-, 2-, 3-, and 6-month follow-ups. Self-reported smoking status was biochemically verified, and depression was assessed using the Beck Depression Inventory-II. RESULTS: At end of treatment, the overall quit rate was 69.2% (83/120). CM showed an additive effect on CBT + BA in enhancing abstinence rates. The significant effect of group [F(1,155) = 9.55, p =  .0024], time [F(4,96) = 7.93, p < .0001], and group by time interaction [F(4,96) = 6.12, p =  .0002], indicated that CM is more effective for generating longer durations of abstinence beyond those of CBT+BA. All treatment conditions equally promoted sustained reductions in depression across time [F(1,111) = 0.53, p =  .4665]. A greater number of days of continuous abstinence and lower depressive symptoms mutually influenced each other. CONCLUSIONS: Depressed smokers achieve high cessation rates without suffering negative mood changes. Quitting smoking is not detrimental and adding CM to CBT + BA enhances long-lasting abstinence rates while promoting large depression improvements.

Data analysis of incomplete repeated measures using a multivariate extension of the Brown-Forsythe procedure / Análisis de datos de medidas repetidas incompletas usando una extensión multivariante del enfoque de Brown-Forsythe

BACKGROUND: A multivariate extension of the Brown-Forsythe (MBF) procedure can be used for the analysis of partially repeated measure designs (PRMD) when the covariance matrices are arbitrary. However, the MBF procedure requires complete data over time for each subject, which is a significant limitation of this procedure. This article provides the rules for pooling the results obtained after applying the same MBF analysis to each of the imputed datasets of a PRMD. METHOD: Montecarlo methods are used to evaluate the proposed solution (MI-MBF), in terms of control of Type I and Type II errors. For comparative purposes, the MBF analysis based on the complete original dataset (OD-MBF) and the covariance pattern model based on an unstructured matrix (CPM-UN) were studied. RESULTS: Robustness and power results showed that the MI-MBF method performed slightly worse than tests based on CPM-UN when the homogeneity assumption was met, but slightly better when that assumption was not met. We also note that without assuming equality of covariance matrices, little power was sacrificed by using the MI-MBF method in place of the OD-MBF method. CONCLUSIONS: The results of this study suggest that the MI-MBF method performs well and could be of practical use

ANTECEDENTES: para analizar diseños de medidas parcialmente repetidas (DMPR) con matrices de covarianza arbitrarias se puede usar una extensión multivariante del enfoque de Brown-Forsythe (MBF). Una importante limitación de este enfoque es que requiere datos completos para cada sujeto. Este artículo proporciona las reglas para agrupar los resultados obtenidos tras aplicar el análisis MBF a los diferentes conjuntos de datos imputados de un DMPR. MÉTODO: se aplican técnicas de Montecarlo para evaluar la solución propuesta (IM-MBF), en términos de control de los errores Tipo I y Tipo II. Con fines comparativos, también se evalúan los resultados obtenidos con el enfoque MBF basado en los datos originales (DO-MBF), así como con el modelo de patrones de covarianza basado en asumir una matriz no estructurada (MPC-NE). RESULTADOS: cuando se cumple el supuesto de homogeneidad, el desempeño de la prueba IM-MBF es ligeramente inferior al obtenido con la prueba MPC-NE, mientras que sucede lo contrario cuando se incumple dicho supuesto. También encontramos que se pierde poca potencia usando el enfoque MI-MBF, en lugar del enfoque DO-MBF, cuando las matrices de covarianza son heterogéneas. CONCLUSIONES: los resultados sugieren que el enfoque MI-MBF funciona bien y podría ser de uso práctico

Data analysis of incomplete repeated measures using a multivariate extension of the Brown-Forsythe procedure.

BACKGROUND: A multivariate extension of the Brown-Forsythe (MBF) procedure can be used for the analysis of partially repeated measure designs (PRMD) when the covariance matrices are arbitrary. However, the MBF procedure requires complete data over time for each subject, which is a significant limitation of this procedure. This article provides the rules for pooling the results obtained after applying the same MBF analysis to each of the imputed datasets of a PRMD. METHOD: Montecarlo methods are used to evaluate the proposed solution (MI-MBF), in terms of control of Type I and Type II errors. For comparative purposes, the MBF analysis based on the complete original dataset (OD-MBF) and the covariance pattern model based on an unstructured matrix (CPM-UN) were studied. RESULTS: Robustness and power results showed that the MI-MBF method performed slightly worse than tests based on CPM-UN when the homogeneity assumption was met, but slightly better when that assumption was not met. We also note that without assuming equality of covariance matrices, little power was sacrificed by using the MI-MBF method in place of the OD-MBF method. CONCLUSIONS: The results of this study suggest that the MI-MBF method performs well and could be of practical use.

The (Ir)Responsibility of (Under)Estimating Missing Data.

It is practically impossible to avoid losing data in the course of an investigation, and it has been proven that the consequences can reach such magnitude that they could even invalidate the results of the study. This paper describes some of the most likely causes of missing data in research in the field of clinical psychology and the consequences they may have on statistical and substantive inferences. When it is necessary to recover the missing information, analyzing the data can become extremely complex. We summarize the experts' recommendations regarding the most powerful procedures for performing this task, the advantages each one has over the others, the elements that can or should influence our choice, and the procedures that are not a recommended option except in very exceptional cases. We conclude by offering four pieces of advice, on which all the experts agree and to which we must attend at all times in order to proceed with the greatest possible success. Finally, we show the pernicious effects produced by missing data on the statistical result and on the substantive or clinical conclusions. For this purpose we have planned to lose data in different percentage rates under two mechanisms of loss of data, MCAR and MAR in the complete data set of two very different real researchs, and we proceed to analyze the set of the available data, listwise deletion. One study is carried out using a quasi-experimental non-equivalent control group design, and another study using a experimental design completely randomized.

Contingency management for cigarette smokers with depressive symptoms.

Despite depressive symptoms being very common among smokers from the general population, few studies have examined the effects of depressive symptoms on smoking treatment outcomes, and even less research has been carried out in the context of contingency management (CM). The authors conducted a secondary analysis to assess the interrelation between treatment condition, depressive symptoms and treatment outcomes among treatment-seeking smokers. The sample was made up of 147 treatment-seeking smokers who were randomly allocated 2 treatment conditions: cognitive behavioral treatment (CBT; n = 74), or CBT + CM (n = 73). CBT was applied in 1-hr group-based sessions over 6 weeks. The CM protocol was voucher-based with maximum earnings of €300 (US$339). Depressive symptoms were assessed using the Beck Depression Inventory-II. Smoking abstinence was verified though cotinine and carbon monoxide. Several analyses were conducted to explore the effect of treatment condition and baseline depressive symptoms on treatment outcomes, as well as the effect of treatment condition and smoking status on depressive symptoms. The CBT + CM condition was more effective than CBT, independent of depressive symptoms. The presence of depressive symptoms decreased the number of days of continuous smoking abstinence. Participants with a greater number of days of continuous smoking abstinence had fewer depressive symptoms than those with fewer days of continuous smoking abstinence. Findings suggest that health care providers should consider encouraging their patients with depressive symptoms to seek smoking cessation services that include both smoking cessation protocols and behavioral activation for mood management, thus maximizing both smoking and depressive outcomes.

Valores personales en estudiantes y profesionales de enfermería / Personal Values among Students and Nurses / Valores pessoais em estudantes e profissionais de enfermagem

El estudio explora el contraste intergeneracional entre valores personales en estudiantes y profesionales de enfermería y su ajuste a las predicciones de la teoría del cambio de valores. La muestra estuvo formada por 369 estudiantes y profesionales de enfermería distribuidos en tres grupos: estudiantes (n = 150), profesionales menores de 40 años (n = 114), y profesionales entre 41 y 60 años (n = 105). Los participantes informaron de sus valores en orden de prioridad en un cuestionario abierto. Los informes se organizaron en categorías de valores para analizar las diferencias entre grupos. Se encuentran dos tendencias en el informe de valores personales, que se ajustan en algunos casos a lo predicho por la teoría del cambio de valores y, en otros casos, la contradicen. Se discute la importancia de estos hallazgos y la necesidad de potenciar una formación orientada hacia los valores de la profesión de enfermería. El estudio contribuye al conocimiento del cambio de valores personales en profesionales de enfermería.

The study explores the generational contrast between personal values in students and nurses and their adjustment to the predictions of the theory of changing values. The sample was comprised of 369 students and nurses who were divided into three groups: students (n = 150), nurses under age 40 (n = 114), and nurses between 41 and 60 years of age (n = 105). The participants reported their values on an open questionnaire, in order of priority. The reports were organized into value categories to analyze the differences between groups. Two trends were found in the report on personal values: in some cases, they fit what is predicted by the theory of changing values; in others, they contradict it. The importance of these findings and the need to empower training oriented towards the values of the nursing profession is discussed. The study contributes to what we know about the change in personal values among nursing professionals.

Este estudo explora o contraste intergeracional entre valores pessoais em estudantes e profissionais de enfermagem e seu ajuste às predições da teoria da mudança de valores. A amostra esteve conformada por 369 estudantes e profissionais de enfermagem distribuídos em três grupos: estudantes (n = 150), profissionais menores de 40 anos (n = 114) e profissionais entre 41 e 60 anos (n = 105). Os participantes informaram sobre seus valores em ordem de prioridade num questionário aberto. Os relatórios foram organizados por categorias de valores para analisar as diferenças entre grupos. Constataram-se duas tendências no relatório de valores pessoais que se ajustam, em alguns casos, ao dito pela teoria da mudança de valores; em outros casos, contradizem-na. Discute-se a importância dessas constatações e a necessidade de potencializar uma formação orientada aos valores da profissão de enfermagem. O estudo contribui para o conhecimento da mudança de valores pessoais em profissionais de enfermagem.

Measuring stages of recovery from psychosis.

BACKGROUND: Mental health consumers invite us to abandon the pathology model, which is tied to pessimism, and instead to embrace a model of personal recovery that goes beyond being free from symptoms, and involves self-management of the illness. The Stages of Recovery Instrument (STORI) is a measure developed from the perspective of consumers according to a conceptual five-stage model of recovery. AIMS: The main aim of this work was to study the psychometric properties of the STORI, but we also set out to compare the stages of recovery in our sample with the five-stage model in the sample with which the scale was developed. METHODS: Our sample consisted of 95 people diagnosed with schizophrenia-spectrum psychoses, with a mean age of 34.74 (SD=9.25). RESULTS: The STORI scores showed adequate psychometric properties in this sample. Cluster analysis indicated that the three-cluster model fitted the data better than the five-cluster model. Internal consistency of the STORI scores ranged between .83 and .87. STORI stages were associated with Recovery Styles Questionnaire scores. DISCUSSION: The results provide empirical validation of the STORI in other countries. Empirical evidence revealed that the stages of recovery found in our own and other clinical samples differ from those found in the samples with which the scale was developed.

Classical and causal inference approaches to statistical mediation analysis / El enfoque clásico y el enfoque de la inferencia causal para el análisis de la mediación

BACKGROUND: Although there is a broad consensus on the use of statistical procedures for mediation analysis in psychological research, the interpretation of the effect of mediation is highly controversial because of the potential violation of the assumptions required in application, most of which are ignored in practice. METHOD: This paper summarises two currently independent procedures for mediation analysis, the classical/SEM and causal inference/CI approaches, together with the statistical assumptions required to estimate unbiased mediation effects, in particular the existence of omitted variables or confounders. A simulation study was run to test whether violating the assumptions changes the estimation of mediating effects. RESULTS: The simulation study showed a significant overestimation of mediation effects with latent confounders. CONCLUSIONS: We recommend expanding the classical with the causal inference approach, which generalises the results of the first approach to mediation using a common estimation method and incorporates new tools to evaluate the statistical assumptions. To achieve this goal, we compare the distinguishing features of recently developed software programs in R, SAS, SPSS, STATA and Mplus

ANTECEDENTES: aunque existe un amplio consenso en el uso de los procedimientos estadísticos para el análisis de la mediación en la investigación psicológica, la interpretación del efecto de mediación resulta muy controvertida debido al potencial incumplimiento de los supuestos que requiere su aplicación, la mayoría de los cuales son ignorados en la práctica. MÉTODO: se resumen los procedimientos actualmente vigentes para el análisis de mediación desde los enfoques clásico y de la inferencia causal, junto con los supuestos estadísticos para estimar efectos de mediación no sesgados, en particular la existencia de variables omitidas o confundidores, y se utiliza un estudio de simulación para determinar si la violación de los supuestos puede cambiar la estimación del efecto de mediación. RESULTADOS: el estudio de simulación mostró una sobreestimación importante del efecto de mediación en presencia de confundidores latentes. CONCLUSIONES: se recomienda complementar el enfoque clásico con el enfoque de la inferencia causal, que generaliza los resultados del primer enfoque al análisis de la mediación e incorpora nuevas herramientas para evaluar sus supuestos estadísticos. Para alcanzar tal objetivo se comparan las características distintivas de los programas de software recientemente desarrollados en R, SAS, SPSS y Mplus

Validez Estructurada para una investigación cuasi-experimental de calidad. Se cumplen 50 años de la presentación en sociedad de los diseños cuasi-experimentales / Structured Validity for a quasi-experimental research of quality. They are fulfilled 50 years of the presentation in company of the quasi-experimental designs

Investigación cuasi-experimental es aquella que tiene como objetivo poner a prueba una hipótesis causal manipulando (al menos) una variable independiente donde por razones logísticas o éticas no se puede asignar las unidades de investigación aleatoriamente a los grupos. Debido a que muchas decisiones a nivel social se toman en base al resultado de investigaciones con estas características, es imperativo que tengan una planificación exquisita de la aplicación del tratamiento, del control en el proceso de la investigación y del análisis de los datos. El pasado año 2013 los diseños cuasi-experimentales cumplieron 50 años, y este trabajo es un homenaje a Campbell y a todos los investigadores que día a día aportan ideas para mejorar el método cuasi-experimental en alguno de sus aspectos. De la mano de una revisión de las investigaciones cuasi-experimentales publicadas en un período de 11 años en tres revistas de Psicología destacamos algunos aspectos que se refieren al cuidado del método. Finalizamos el trabajo proponiendo el concepto de Validez Estructurada, que en resumen, es el hilo conductor que debe seguir la realización de toda investigación para poner a prueba con garantía las hipótesis que responden a los objetivos que en ella se plantean, concretamente, en las investigaciones cuasi-experimentales

Quasi-experimental investigation is that one that has as aim test a causal hypothesis manipulating (at least) an independent variable where for logistic or ethical reasons it is not possible to assign the units of investigation at random to the groups. Due to the fact that many decisions at the social level take on the basis of the result of investigations with these characteristics, it is imperative that have an exquisite planning of the application of the treatment, of the control in the process of the investigation and of the analysis of the data. Last year 2013 the quasi-experimental designs expired 50 years, and this work in an honoring to Campbell and to all the investigators who day after day contribute ideas to improve the quasi-experimental method in someone of his aspects. From the hand of a re-view of the quasi-experimental investigations published in a period of 11 years in three journals of psychology we distinguish some aspects that refer to the care of the method. We finished work by proposing the concept of Structured Validity, which in summary, is the thread that must follow all re-search to test with guarantee the hypothesis that respond to the objectives it raised, in particular, in quasi-experimental investigations

Classical and causal inference approaches to statistical mediation analysis.

BACKGROUND: Although there is a broad consensus on the use of statistical procedures for mediation analysis in psychological research, the interpretation of the effect of mediation is highly controversial because of the potential violation of the assumptions required in application, most of which are ignored in practice. METHOD: This paper summarises two currently independent procedures for mediation analysis, the classical/SEM and causal inference/CI approaches, together with the statistical assumptions required to estimate unbiased mediation effects, in particular the existence of omitted variables or confounders. A simulation study was run to test whether violating the assumptions changes the estimation of mediating effects. RESULTS: The simulation study showed a significant overestimation of mediation effects with latent confounders. CONCLUSIONS: We recommend expanding the classical with the causal inference approach, which generalises the results of the first approach to mediation using a common estimation method and incorporates new tools to evaluate the statistical assumptions. To achieve this goal, we compare the distinguishing features of recently developed software programs in R, SAS, SPSS, STATA and Mplus.

Multilevel bootstrap analysis with assumptions violated / Análisis bootstrap multinivel con supuestos incumplidos

Background: Likelihood-based methods can work poorly when the residuals are not normally distributed and the variances across clusters are heterogeneous. Method: The performance of two estimation methods, the non-parametric residual bootstrap (RB) and the restricted maximum likelihood (REML) for fitting multilevel models are compared through simulation studies in terms of bias, coverage, and precision. Results: We find that (a) both methods produce unbiased estimates of the fixed parameters, but biased estimates of the random parameters, although the REML was more prone to give biased estimates for the variance components; (b) the RB method yields substantial reductions in the difference between nominal and actual confidence interval coverage, compared with the REML method; and (c) for the square root of the mean squared error (RMSE) of the fixed effects, the RB method performed slightly better than the REML method. For the variance components, however, the RB method did not offer a systematic improvement over the REML method in terms of RMSE. Conclusions: It can be stated that the RB method is, in general, superior to the REML method with violated assumptions (AU)

Antecedentes: los métodos basados en la verosimilitud pueden trabajar con dificultad cuando los errores no se distribuyen normalmente y las varianzas a través de los grupos son heterogéneas. Método: el desempeño de dos métodos de estimación, el bootstrap residual (BR) no paramétrico y el de la máxima verosimilitud restringida (MVR), para ajustar modelos multinivel es comparado mediante estudios de simulación en términos de sesgo, cobertura y precisión. Resultados: encontramos que: (a) ambos métodos proporcionan estimaciones no sesgadas de los efectos fijos, pero sesgadas de los efectos aleatorios, aunque el método MVR es más propenso a generar estimaciones sesgadas para los componentes de la varianza; (b) el método BR depara diferencias más pequeñas entre las tasas de cobertura real y nominal de los intervalos de confianza que el método MVR; y (c) los valores de la raíz del error cuadrático medio (RECM) para los efectos fijos son algo más pequeños bajo el método BR que bajo el método REML. Sin embargo, en lo referido a los componentes de la varianza, el método de BR no ofrece una mejora sistemática sobre el método MVR en términos de RECM. Conclusiones: en general, se puede afirmar que el método BR resulta superior al método MVR con supuestos incumplidos (AU)

Multilevel bootstrap analysis with assumptions violated.

BACKGROUND: Likelihood-based methods can work poorly when the residuals are not normally distributed and the variances across clusters are heterogeneous. METHOD: The performance of two estimation methods, the non-parametric residual bootstrap (RB) and the restricted maximum likelihood (REML) for fitting multilevel models are compared through simulation studies in terms of bias, coverage, and precision. RESULTS: We find that (a) both methods produce unbiased estimates of the fixed parameters, but biased estimates of the random parameters, although the REML was more prone to give biased estimates for the variance components; (b) the RB method yields substantial reductions in the difference between nominal and actual confidence interval coverage, compared with the REML method; and (c) for the square root of the mean squared error (RMSE) of the fixed effects, the RB method performed slightly better than the REML method. For the variance components, however, the RB method did not offer a systematic improvement over the REML method in terms of RMSE. CONCLUSIONS: It can be stated that the RB method is, in general, superior to the REML method with violated assumptions.

The two-way mixed model: a long and winding controversy / El modelo mixto de dos factores: una larga y tortuosa controversia

Background: With the 2-way mixed model, one a fixed factor and the other random, the procedure followed to test statistical significance of the random factor has been the focus of a heated controversy in theoretical and applied statistics, and the debating continues even now. One of the main consequences of this controversy is that the position defended in the classical ANOVA texts on the hypothesis of the significance of the random effect is not the same as that defended in almost all of the professional statistical software programs. Method: In this paper, we deal with a detailed analysis about the controversy of mixed model and the decision about one of two basic options, the non restrictive and the restrictive model. Results: Three key questions we consider to go beyond the controversy are: (1) the two classical models are equivalent, (2) the marginality principle do not allow to test main effects in presence of interactive significant effects and (3) the relevance of linear mixed approach to analyze models with fixed and random effects. Conclusions: We propose the simple solution of using the mixed linear approach with REML estimation instead of the classical linear approach, which is really unapplicable in this context (AU)

Antecedentes: en el modelo mixto de dos factores, con un factor fijo y el otro aleatorio, la forma de probar la significación del factor aleatorio ha sido objeto de una enconada controversia en la estadística teórica y aplicada que todavía hoy sigue siendo objeto de polémica. Una de las consecuencias más sorprendentes de esta controversia es que la posición que se defiende en los textos clásicos de ANOVA sobre la prueba de hipótesis del factor aleatorio no es la misma que la defendida en casi todos los programas estadísticos profesionales. Método: en este trabajo se aborda un análisis detallado de la controversia sobre el modelo mixto y la decisión de adoptar una de las dos opciones básicas, el modelo no restrictivo o el modelo restrictivo. Resultados: las cuestiones clave que se consideran para trascender esta controversia son: (1) las dos opciones básicas son matemáticamente equivalentes, (2) el principio de marginalidad no permite probar efectos principales en presencia de efectos interactivos significativos y (3) la pertinencia del enfoque lineal mixto para analizar modelos con efectos fijos y aleatorios. Discusión: en este trabajo se propone como solución a la controversia la utilización del enfoque lineal mixto con estimación REML en menoscabo del enfoque lineal clásico, que resulta inaplicable en este contexto (AU)

The two-way mixed model: a long and winding controversy.

BACKGROUND: With the 2-way mixed model, one a fixed factor and the other random, the procedure followed to test statistical significance of the random factor has been the focus of a heated controversy in theoretical and applied statistics, and the debating continues even now. One of the main consequences of this controversy is that the position defended in the classical ANOVA texts on the hypothesis of the significance of the random effect is not the same as that defended in almost all of the professional statistical software programs. METHOD: In this paper, we deal with a detailed analysis about the controversy of mixed model and the decision about one of two basic options, the non restrictive and the restrictive model. RESULTS: Three key questions we consider to go beyond the controversy are: (1) the two classical models are equivalent, (2) the marginality principle do not allow to test main effects in presence of interactive significant effects and (3) the relevance of linear mixed approach to analyze models with fixed and random effects. CONCLUSIONS: We propose the simple solution of using the mixed linear approach with REML estimation instead of the classical linear approach, which is really unapplicable in this context.

[Nested model selection for longitudinal data using information criteria and the conditional adjustment strategy]. / Selección de modelos anidados para datos longitudinales usando criterios de información y la estrategia de ajuste condicional.

Knowledge of the subject matter plays a vital role when attempting to choose the best possible linear mixed model to analyze longitudinal data. To date, in the absence of strong theory, much of the work has focused on modeling the covariance matrix by comparing non-nested models using selection criteria. In this paper, we compare the performance of conditional likelihood ratio test (LRT) and several versions of information criteria for selecting nested mean structures and/or nested covariance structures, assuming that the true data-generating processes are known. Simulation results indicate that the efficient criteria performed better than their consistent counterparts when covariance structures used in the data generation were complex, and worse when structures were simple. The conditional LRT under full maximum likelihood (FML) estimation was better overall than the other criteria in terms of selection performance. However, under restricted maximum likelihood (REML), estimation was inferior. We also find that the strategy suggested in the statistical literature of using REML for covariance structure selection, and FML for mean structure selection may be misleading.

Selección de modelos anidados para datos longitudinales usando criterios de información y la estrategia de ajuste condicional / Nested model selection for longitudinal data using information criteria and the conditional adjustment strategy

Un marco teórico potente resulta clave para especificar el modelo mixto que explica mejor la variabilidad de datos longitudinales. A falta de teoría, la mayoría de las investigaciones realizadas hasta la fecha, se ha centrado en ajustar la matriz de dispersión usando criterios de selección de modelos para elegir entre estructuras de covarianza no anidadas. En este trabajo, comparamos el desempeño del estadístico razón de verosimilitud (LRT) condicional y de varias versiones de los criterios de información para seleccionar estructuras de medias y/o de covarianzas anidadas, asumiendo conocido el verdadero proceso generador de datos. Los resultados numéricos indican que los criterios de información eficientes funcionaban mejor que sus homólogos consistentes cuando las matrices de dispersión usadas en la generación eran complejas y peor cuando eran simples. Globalmente, el desempeño del LRT condicional basado en el estimador de máxima verosimilitud completa (FML) era superior al resto de los criterios examinados. Sin embargo, el desempeño era inferior cuando se basaba en el estimador máxima verosimilitud restringida (REML). También encontramos que la estrategia sugerida en la literatura estadística de usar el estimador REML para seleccionar la estructura de covarianza y el estimador FML para seleccionar la estructura de medias debería ser evitada(AU)

Knowledge of the subject matter plays a vital role when attempting to choose the best possible linear mixed model to analyze longitudinal data. To date, in the absence of strong theory, much of the work has focused on modeling the covariance matrix by comparing non-nested models using selection criteria. In this paper, we compare the performance of conditional likelihood ratio test (LRT) and several versions of information criteria for selecting nested mean structures and/or nested covariance structures, assuming that the true data-generating processes are known. Simulation results indicate that the efficient criteria performed better than their consistent counterparts when covariance structures used in the data generation were complex, and worse when structures were simple. The conditional LRT under full maximum likelihood (FML) estimation was better overall than the other criteria in terms of selection performance. However, under restricted maximum likelihood (REML), estimation was inferior. We also find that the strategy suggested in the statistical literature of using REML for covariance structure selection, and FML for mean structure selection may be misleading(AU)

Symptomatic and functional outcome in youth at ultra-high risk for psychosis: a longitudinal study.

The current report assesses the clinical, functioning and demographic data of a cohort enrolled in the P3 prevention program for psychosis; a Spanish National Health System and Ministry of Science funded program. Comparisons are made between those individuals who had converted to psychosis and those who had not at 3years after an average of 24 treatment sessions. Subjects included 61 participants meeting Structured Interview for Prodromal Syndromes criteria, with ages ranging from 17 to 31, and all meeting criteria for ultra-high risk of psychosis. Prospective follow-up data are reported for patients re-evaluated at 1 and 3years. At 1-year follow-up, the conversion rate to psychosis was 18%, but increased to 23% at 3-year follow-up. The converted sample was older than the non-converted sample and more likely to have higher ratings on subsyndromal psychotic (positive and disorganized), negative and general symptoms, and lower levels of functioning at baseline assessment. Analyses of change over time indicated a clear clinical improvement in both clinically stable patients and in those who showed a transient psychotic state over time. No gender differences in symptom or functioning levels at the three follow-up time points were found; however, the interactions among conversionxgenderxSOPS total scorextime points significantly reflect that the growth profiles of the four groups (no conversion males, no conversion females, conversion males and conversion females) in the SOPS total score are not parallel and that, consequently, the four groups involved different patterns of change over time, males experiencing faster and longer deterioration when psychotic symptoms arise.

[Construction of hierarchical models in applied contexts]. / Construcción de modelos jerárquicos en contextos aplicados.

Hierarchical linear models have become a very popular tool for analyzing data with a hierarchical structure. This methodology recognizes the nested structure of the data and allows obtaining unbiased estimates of the variations found in the different levels of the hierarchy. The goal of this article is to illustrate the construction of hierarchical models both in cross-sectional and longitudinal contexts involving three and four levels, respectively. The efficiency of an intervention program designed to improve the mathematical performance of primary school students is evaluated to illustrate the statistical modelling process. The example used is analyzed by the SAS and SPSS packages, whose syntax is duly detailed in the manuscript.

[Generalization of the Brown-Forsythe approach to factorial designs]. / Generalización del enfoque Brown-Forsythe a diseños factoriales.

The current paper proposes a solution that generalizes ideas of Brown and Forsythe to the problem of comparing hypotheses in two-way classification designs with heteroscedastic error structure. Unlike the standard analysis of variance, the proposed approach does not require the homogeneity assumption. A comprehensive simulation study, in which sample size of the cells, relationship between the cell sizes and unequal variance, degree of variance heterogeneity, and population distribution shape were systematically manipulated, shows that the proposed approximation was generally robust when normality and heterogeneity were jointly violated.