Combination rules for homoscedastic and heteroscedastic MANOVA models from multiply imputed datasets.

Classical MANOVA tests do not pose any difficulty when the assumptions on which they are based are satisfied, while the modified Brown-Forsythe (MBF) procedure has low sensitivity to the lack of multivariate normality and homogeneity of covariance matrices. Both methods assume complete data for all subjects. In this paper, we present combination rules for the MANOVA and MBF procedures with multiply imputed datasets. These rules are illustrated by pooling the results obtained with a two-factor multivariate design after applying the two approaches to each of the imputed datasets when the covariance matrices were equal (MI-MANOVA) and when the covariance matrices were unequal (MI-MBF). A Monte-Carlo study was carried out to compare the proposed solution, in terms of type I error rates and statistical power, with the MANOVA and MBF approaches without missing data, and with listwise deletion of missing data followed by the MANOVA approach (LD-MANOVA) and listwise deletion followed by the MBF procedure (LD-MBF). Simulations showed that the type I error rates in all analyses on datasets with missing values (with or without imputation) were well controlled. We also found that the MI-MANOVA approach was substantially more powerful than LD-MANOVA. Moreover, the power of the MI-MANOVA was generally comparable to that of its complete data counterpart. Similar results were obtained for the MI-MBF procedure when covariance matrices were unequal. We conclude, based on the current evidence, that the solution presented performs well and could be of practical use. We illustrate the application of combination rules using a real dataset.

Sample size estimation for heterogeneous growth curve models with attrition.

In this study, two approaches were employed to calculate how large the sample size needs to be in order to achieve a desired statistical power to detect a significant group-by-time interaction in longitudinal intervention studies-a power analysis method, based on derived formulas using ordinary least squares estimates, and an empirical method, based on restricted maximum likelihood estimates. The performance of both procedures was examined under four different scenarios: (a) complete data with homogeneous variances, (b) incomplete data with homogeneous variances, (c) complete data with heterogeneous variances, and (d) incomplete data with heterogeneous variances. Several interesting findings emerged from this research. First, in the presence of heterogeneity, larger sample sizes are required in order to attain a desired nominal power. The second interesting finding is that, when there is attrition, the sample size requirements can be quite large. However, when attrition is anticipated, derived formulas enable the power to be calculated on the basis of the final number of subjects that are expected to complete the study. The third major finding is that the direct mathematical formulas allow the user to rigorously determine the sample size required to achieve a specified power level. Therefore, when data can be assumed to be missing at random, the solution presented can be adopted, given that Monte Carlo studies have indicated that it is very satisfactory. We illustrate the proposed method using real data from two previously published datasets.

Selecting the best unbalanced repeated measures model.

This study examined the performance of selection criteria available in the major statistical packages for both mean model and covariance structure. Unbalanced designs due to missing data involving both a moderate and large number of repeated measurements and varying total sample sizes were investigated. The study also investigated the impact of using different estimation strategies for information criteria, the impact of different adjustments for calculating the criteria, and the impact of different distribution shapes. Overall, we found that the ability of consistent criteria in any of the their examined forms to select the correct model was superior under simple covariance patterns than under complex covariance patterns, and vice versa for the efficient criteria. The simulation studies covered in this paper also revealed that, regardless of method of estimation used, the consistent criteria based on number of subjects were more effective than the consistent criteria based on total number of observations, and vice versa for the efficient criteria. Furthermore, results indicated that, given a dataset with missing values, the efficient criteria were more affected than the consistent criteria by the lack of normality.

Análisis de medidas repetidas usando métodos de remuestreo / Analyzing repeated measures using resampling methods

Este artículo evalúa la robustez de varios enfoques para analizar diseños de medidas repetidas cuando los supuestos de normalidad y esfericidad multimuetral son separada y conjuntamente violados. Específicamente, el trabajo de los autores compara el desempeño de dos métodos de remuestreo, pruebas de permutación y de bootstrap, con el desempeño del usual modelo de análisis de varianza (ANOVA) y modelo lineal mixto con la solución Kenward-Roger implementada en SAS PROC MIXED. Los autores descubrieron que la prueba de permutación se comportaba mejor que las pruebas restantes cuando se incumplían los supuestos de normalidad y de esfericidad. Por el contrario, cuando se violaban los su-puestos de normalidad y de esfericidad multimuestral los resultados pusieron de relieve que la prueba Bootstrap-F proporcionaba un control de las tasas de error superior al ofrecido por la prueba de permutación y por enfoque del modelo mixto. La ejecución del enfoque ANOVA se vio afectada considerablemente por la presencia de heterogeneidad y por la falta de esfericidad, pero escasamente por la ausencia de normalidad (AU)

This article evaluated the robustness of several approaches for analyzing repeated measures designs when the assumptions of normality and multisample sphericity are violated separately and jointly. Specifically, the authors’ work compares the performance of two resampling methods, bootstrapping and permutation tests, with the performance of the usual analysis of variance (ANOVA) model and the mixed linear model procedure ad-justed by the Kenward–Roger solution available in SAS PROC MIXED. The authors found that the permutation test outperformed the other three methods when normality and sphericity assumptions did not hold. In contrast, when normality and multisample sphericity assumptions were violated the results clearly revealed that the Bootstrap-F test provided generally better control of Type I error rates than the permutation test and mixed linear model approach. The execution of ANOVA approach was considerably influenced by the presence of heterogeneity and lack of spheric-ity, but scarcely affected by the absence of normality (AU)

A robust approach for analyzing unbalanced factorial designs with fixed levels.

The goal of this study was to investigate the performance of Hall's transformation of the Brunner-Dette-Munk (BDM) and Welch-James (WJ) test statistics and Box-Cox's data transformation in factorial designs when normality and variance homogeneity assumptions were violated separately and jointly. On the basis of unweighted marginal means, we performed a simulation study to explore the operating characteristics of the methods proposed for a variety of distributions with small sample sizes. Monte Carlo simulation results showed that when data were sampled from symmetric distributions, the error rates of the original BDM and WJ tests were scarcely affected by the lack of normality and homogeneity of variance. In contrast, when data were sampled from skewed distributions, the original BDM and WJ rates were not well controlled. Under such circumstances, the results clearly revealed that Hall's transformation of the BDM and WJ tests provided generally better control of Type I error rates than did the same tests based on Box-Cox's data transformation. Among all the methods considered in this study, we also found that Hall's transformation of the BDM test yielded the best control of Type I errors, although it was often less powerful than either of the WJ tests when both approaches reasonably controlled the error rates.

Gangrena de Fournier por Streptococcus agalactiae en mujer no gestante / Fournier’s gangrene caused by Streptococcus agalactiae in a non-pregnantwoman

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[Evaluation of methods for studying susceptibility to oxacillin and penicillin in 60 Staphylococcus lugdunensis isolates]. / Evaluación de métodos para el estudio de la sensibilidad a oxacilina y penicilina en 60 aislamientos de Staphylococcus lugdunensis.

INTRODUCTION: Staphylococcus lugdunensis is a coagulase-negative staphylococcus associated with a variety of clinical infections. In this paper we present the results of a comparative study using 4 methods to determine antimicrobial susceptibility to oxacillin and penicillin in 60 S. lugdunensis isolates. MATERIAL AND METHODS: We studied 60 S. lugdunensis isolates obtained from clinical specimens sent to our laboratory over an 8-year period. All isolates were free coagulase-negative and DNase-negative, and biochemically identified by API ID 32 STAPH (bioMérieux). Presence of mecA and ss-lactamase production were studied in all cases. Antimicrobial susceptibility was determined by the Vitek 2 System (bioMérieux) and broth microdilution (Wider) (Soria Melguizo) for penicillin and oxacillin, and the E-test (AB Biodisk) and cefoxitin disk diffusion test (BD BBLTM) for oxacillin. RESULTS: All isolates lacked the mecA gene and were susceptible to oxacillin by broth microdilution, E-test, and cefoxitin disk diffusion test. Only two isolates were oxacillin-resistant by the Vitek 2 System. Twenty-four isolates (40%) were ss-lactamase-positive, 4 after induction. Susceptibility testing to penicillin determined that 48 isolates showed concordance between the results obtained by broth microdilution and Vitek 2, but 12 isolates (20%), showed divergent results. CONCLUSIONS: We detected no resistance to oxacillin in S. lugdunensis. All the methods evaluated were adequate for determining oxacillin resistance. The Vitek 2 System is useful for detecting penicillin resistance, but the ss-lactamase test should be applied to isolates with a MIC=0.25microg/ml to avoid the interpretation of false resistance to this antibiotic.

Evaluación de métodos para el estudio de la sensibilidad a oxacilina y penicilina en 60 aislamientos de Staphylococcus lugdunensis / Evaluation of methods for studying susceptibility to oxacillin and penicillin in 60 Staphylococcus lugdunensis isolates

Introducción: Staphylococcus lugdunensis es un estafilococo coagulasa negativo relacionado con diversos tipos de infección. En este trabajo se presentan los resultados de un estudio comparativo mediante cuatro métodos para determinar la sensibilidad a oxacilina y penicilina. Material y métodos: se estudiaron 60 aislamientos de S. lugdunensis procedentes de muestras clínicas enviadas a nuestro laboratorio durante 8 años. Todos los aislados fueron coagulasa y DNasa negativos. La identificación se realizó bioquímicamente mediante API ID 32 STAPH (bioMérieux). En todos los casos se analizó la presencia de betalactamasa y la detección del gen mecA. La susceptibilidad antimicrobiana se determinó mediante: Vitek 2 System (bioMérieux) y microdilución en caldo (Wider) (Soria Melguizo) para oxacilina y penicilina; E-test (AB Biodisk) y método de difusión con disco de cefoxitina (BD BBLTM), para ensayar la sensibilidad a oxacilina. Resultados: todos los aislamientos fueron mecA negativos y sensibles a oxacilina en microdilución en caldo, E-test y en el método de difusión con cefoxitina, mientras que en Vitek 2 solamente dos aislamientos fueron resistentes a oxacilina; 24 (40%) fueron betalactamasa positivos, 4 tras inducción. Los resultados de susceptibilidad a penicilina mostraron que 48 aislamientos presentaban concordancia entre los obtenidos por microdilución en caldo y Vitek 2, pero 12 (20%) mostraron resultados discrepantes Conclusiones: en nuestro estudio no hemos hallado ningún aislamiento de S. lugdunensis resistente a oxacilina; los métodos de microdilución en caldo (Wider), E-test de oxacilina y difusión con disco de cefoxitina son adecuados para el estudio de sensibilidad a este antibiótico. El empleo del sistema Vitek 2 es útil para el estudio de la sensibilidad a penicilina si se aplica la prueba de betalactamasa a los aislamientos con concentración mínima inhibitoria (CMI) de 0,25μg/ml para evitar la interpretación de una falsa resistencia a dicho antibiótico (AU)

Introduction: Staphylococcus lugdunensis is a coagulase-negative staphylococcus associated with a variety of clinical infections. In this paper we present the results of a comparative study using 4 methods to determine antimicrobial susceptibility to oxacillin and penicillin in 60 S. lugdunensis isolates. Material and methods: We studied 60 S. lugdunensis isolates obtained from clinical specimens sent to our laboratory over an 8-year period. All isolates were free coagulase-negative and DNase-negative, and biochemically identified by API ID 32 STAPH (bioMérieux). Presence of mecA and ß-lactamase production were studied in all cases. Antimicrobial susceptibility was determined by the Vitek 2 System (bioMérieux) and broth microdilution (Wider) (Soria Melguizo) for penicillin and oxacillin, and the E-test (AB Biodisk) and cefoxitin disk diffusion test (BD BBLTM) for oxacillin. Results: All isolates lacked the mecA gene and were susceptible to oxacillin by broth microdilution, E-test, and cefoxitin disk diffusion test. Only two isolates were oxacillin-resistant by the Vitek 2 System. Twenty-four isolates (40%) were ß-lactamase-positive, 4 after induction. Susceptibility testing to penicillin determined that 48 isolates showed concordance between the results obtained by broth microdilution and Vitek 2, but 12 isolates (20%), showed divergent results. Conclusions: We detected no resistance to oxacillin in S. lugdunensis. All the methods evaluated were adequate for determining oxacillin resistance. The Vitek 2 System is useful for detecting penicillin resistance, but the ß-lactamase test should be applied to isolates with a MIC=0.25μg/ml to avoid the interpretation of false resistance to this antibiotic (AU)

A practical method for analyzing factorial designs with heteroscedastic data.

The Type I error rates and powers of three recent tests for analyzing nonorthogonal factorial designs under departures from the assumptions of homogeneity and normality were evaluated using Monte Carlo simulation. Specifically, this work compared the performance of the modified Brown-Forsythe procedure, the generalization of Box's method proposed by Brunner, Dette, and Munk, and the mixed-model procedure adjusted by the Kenward-Roger solution available in the SAS statistical package. With regard to robustness, the three approaches adequately controlled Type I error when the data were generated from symmetric distributions; however, this study's results indicate that, when the data were extracted from asymmetric distributions, the modified Brown-Forsythe approach controlled the Type I error slightly better than the other procedures. With regard to sensitivity, the higher power rates were obtained when the analyses were done with the MIXED procedure of the SAS program. Furthermore, results also identified that, when the data were generated from symmetric distributions, little power was sacrificed by using the generalization of Box's method in place of the modified Brown-Forsythe procedure.