Meta Analysis of Advanced Cancer Survival Data Using Lognormal Parametric Fitting: A Statistical Method to Identify Effective Treatment Protocols

Qazi, Sanjive; DuMez, D.; Uckun, Fatih M.

doi:10.2174/138161207780765882

Cited by 12 publications

(11 citation statements)

References 102 publications

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“…These results are supported by research showing that in the social and health sciences, many variables follow a log-normal distribution (Limpert, Stahel, & Abbt, 2001). Examples in the field of medicine include the latency period of infectious diseases (Kondo, 1977), survival times for certain types of cancer (Claret et al, 2009;Qazi, DuMez, & Uckun, 2007), and the age of onset of Alzheimer's disease (Horner, 1987). In the social sciences, an example would be the age at which people first get married (Preston, 1981), while in psychology the log-normal distribution provides a good fit to data on reaction times or response latencies (Shang-wen & Ming-hua, 2010;Ulrich & Miller, 1993; Van der Linden, 2006), as well as to data on attentional skills (Brown, Weatherholt, & Burns, 2010).…”

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confidence: 80%

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs

et al. 2012

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Using a Monte Carlo simulation and the KenwardRoger (KR) correction for degrees of freedom, in this article we analyzed the application of the linear mixed model (LMM) to a mixed repeated measures design. The LMM was first used to select the covariance structure with three types of data distribution: normal, exponential, and log-normal. This showed that, with homogeneous between-groups covariance and when the distribution was normal, the covariance structure with the best fit was the unstructured population matrix. However, with heterogeneous between-groups covariance and when the pairing between covariance matrices and group sizes was null, the best fit was shown by the between-subjects heterogeneous unstructured population matrix, which was the case for all of the distributions analyzed. By contrast, with positive or negative pairings, the within-subjects and betweensubjects heterogeneous first-order autoregressive structure produced the best fit. In the second stage of the study, the robustness of the LMM was tested. This showed that the KR method provided adequate control of Type I error rates for the time effect with normally distributed data. However, as skewness increased-as occurs, for example, in the log-normal distribution-the robustness of KR was null, especially when the assumption of sphericity was violated. As regards the influence of kurtosis, the analysis showed that the degree of robustness increased in line with the amount of kurtosis. Keywords Longitudinal data . Linear mixed model . Kenward-Roger method . Robustness . Nonnormal distributionsOver the last three decades, the analysis of repeated measures data has centered on the linear mixed model (LMM). Laird and Ware (1982) established the basis of the LMM with the incorporation of the within-subjects error correlation. Their work was subsequently extended by Cnaan, Laird, and Slasor (1997) and Verbeke and Molenberghs (2000), who applied the LMM to longitudinal data. In contrast to analyses that are based on variances (ANOVA and MANOVA), the LMM models the structure of the covariance matrix. This enables a more efficient estimation of the fixed effects and, consequently, yields more robust statistical tests. However, when the covariance structures are not properly fitted and the sample sizes are small, the Type I error rate tends to rise (Wright & Wolfinger, 1996).With respect to the covariance structure, Keselman, Algina, Kowalchuk, and Wolfinger (1999) demonstrated that when the covariance matrix is not spherical, the degrees of freedom associated with the conventional F test are too large. One way of controlling this bias is to apply the procedure developed by Kenward and Roger (1997; henceforth, the KR method) to correct the degrees of freedom. Studies by Kowalchuk, Keselman, Algina, and Wolfinger (2004) and Vallejo and Ato (2006) showed that with an adequate covariance structure, the KR method is able, in

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confidence: 80%

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs

et al. 2012

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“…[5] [6] Se retomó el índice de marginación elaborado por CONAPO, el cual es una medida-resumen que permite diferenciar los estados y municipios del país, según sean el impacto global de las carencias que padece la población como resultado de la falta de acceso a la educación, la residencia en viviendas inadecuadas, la percepción de ingresos monetarios insuficientes y las relacionadas con la residencia en localidades pequeñas. El índice de marginación es una herramienta que contribuye a formular diagnósticos exhaustivos, identificar las disparidades socioespaciales que persisten en los estados y municipios del país y, con ello, apoyar el diseño e instrumentación de programas y acciones dirigidos a fortalecer la justicia distributiva en el ámbito regional y la atención prioritaria de la población con mayor desventaja.…”

Section: Suplemento 2 De 2009unclassified

“…L a prioridad de los sistemas de salud desarrollados es que toda la población, cualesquiera que sean su condición socioeconómica y el grupo étnico al que pertenezca, tenga las mismas oportunidades y calidad en la accesibilidad y servicios de atención preventiva y curativa. 1 Diversos estudios, 2 revisiones sistemáticas [3][4][5] y metaanálisis 6 han documentado las disparidades que existen en relación con la incidencia, cobertura de tamizaje, tratamiento, sobrevida y mortalidad por cáncer en la mujer, en especial en países de ingresos altos. 7 La evidencia muestra que poblaciones marginadas, en términos sociales, geográficos y económicos, tienen una mayor probabilidad de morir por cánceres prevenibles, lo cual se puede atribuir entre otros factores a que no reciben un diagnóstico oportuno y por tanto el tratamiento es tardío.…”

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Diferencias regionales en la mortalidad por cáncer de mama y cérvix en México entre 1979 y 2006

Palacio-Mejía¹,

Lazcano‐Ponce²,

Allen‐Leigh³

et al. 2009

Salud pública Méx

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“…However, the variables encountered in the field of health and social sciences often do not follow a normal distribution (Blanca, Arnau, Bono, López-Montiel & Bendayan, 2013;Limpert, Stahel & Abbt, 2001;Micceri, 1989). Examples of such variables in the health sciences are survival times for certain types of cancer (Claret et al, 2009;Qazi, DuMez & Uckun, 2007) or the age at onset of Alzheimer's disease (Horner, 1987), while in the social sciences it is the case of variables such as social support (Matud, Carballeira, Lopez, Marrero & Ibáñez, 2002), physical and verbal aggression in couple relationships (Soler, Vinyak & Quadagno, 2000), certain psychosocial aspects of addictions (Deluchi & Bostrom, 2004), post-traumatic stress (Sullivan & Holt, 2008), reaction times or response latency (ShangWen & Ming-Hua, 2010;Ulrich & Miller, 1993; Van der Linden, 2006), certain attentional skills (Brown, Weatherholt & Burns, 2010) and variables of a psychophysiological nature (Keselman, Wilcox & Lix, 2003).…”

Section: Introductionmentioning

confidence: 99%

Comparación de los procedimientos de Fleishman y Ramberg et al. para generar datos no normales en estudios de simulación

et al. 2014

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Título: Comparación de los procedimientos de Fleishman y Ramberg et al. para generar datos no normales en estudios de simulación. Resumen: Las técnicas de simulación deben posibilitar la generación adecuada de las distribuciones más frecuentes en la realidad como son las distribuciones no normales. Entre los procedimientos para la generación de datos no normales destacan el método de transformaciones lineales propuesto por Fleishman y el método basado en la generalización de la distribución lambda de Tukey propuesto por Ramberg et al. Este estudio compara los procedimientos en función del ajuste de las distribuciones generadas a sus respectivos modelos teóricos y del número de simulaciones necesarias para dicho ajuste. Con este objetivo se seleccionan, junto con la distribución normal, una serie de distribuciones no normales frecuentes en datos reales, y se analiza el ajuste según el grado de violación de la normalidad y del número de simulaciones realizadas. Los resultados muestran que ambos procedimientos de generación de datos tienen un comportamiento similar. A medida que aumenta el grado de contaminación de la distribución teórica hay que aumentar el número de simulaciones a realizar para asegurar un mayor ajuste a la generada. Los dos procedimientos son más precisos para generar distribuciones normales y no normales a partir de 7000 simulaciones aunque cuando el grado de contaminación es severo (con valores de asimetría y curtosis de 2 y 6, respectivamente), se recomienda aumentar el número de simulaciones a 15000. Palabras clave: Simulación; Monte Carlo; generadores de datos; datos no normales; número de simulaciones.Abstract: Simulation techniques must be able to generate the types of distributions most commonly encountered in real data, for example, nonnormal distributions. Two recognized procedures for generating nonnormal data are Fleishman's linear transformation method and the method proposed by Ramberg et al. that is based on generalization of the Tukey lambda distribution. This study compares these procedures in terms of the extent to which the distributions they generate fit their respective theoretical models, and it also examines the number of simulations needed to achieve this fit. To this end, the paper considers, in addition to the normal distribution, a series of non-normal distributions that are commonly found in real data, and then analyses fit according to the extent to which normality is violated and the number of simulations performed. The results show that the two data generation procedures behave similarly. As the degree of contamination of the theoretical distribution increases, so does the number of simulations required to ensure a good fit to the generated data. The two procedures generate more accurate normal and non-normal distributions when at least 7000 simulations are performed, although when the degree of contamination is severe (with values of skewness and kurtosis of 2 and 6, respectively) it is advisable to perform 15000 simulations.

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Meta Analysis of Advanced Cancer Survival Data Using Lognormal Parametric Fitting: A Statistical Method to Identify Effective Treatment Protocols

Cited by 12 publications

References 102 publications

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs

Diferencias regionales en la mortalidad por cáncer de mama y cérvix en México entre 1979 y 2006

Comparación de los procedimientos de Fleishman y Ramberg et al. para generar datos no normales en estudios de simulación

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