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Recursos bibliográficos en papel y digitales -
- libros, artículos de revistas,
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» Resultado: 2 registros
| Registro 1 de 2 | |
| Autor: | Yasui, Yutaka - Feng, Ziding - Diehr, Paula - McLerran, Dale - Beresford, Shirley A. A. - McCulloch, Charles E. |
| Título: | Evaluation of community-intervention trials via generalized linear mixed models |
| Fuente: | Biometrics. v.60, n.4. International Biometric Society |
| Páginas: | pp. 1043-1052 |
| Año: | dec. 2004 |
| Resumen: | In community-intervention trials, communities, rather than individuals, are randomized to experimental arms. Generalized linear mixed models offer a flexible parametric framework for the evaluation of community-intervention trials, incorporating both systematic and random variations at the community and individual levels. We propose here a simple two-stage inference method for generalized linear mixed models, specifically tailored to the analysis of community-intervention trials. In the first stage, community-specific random effects are estimated from individual-level data, adjusting for the effects of individual-level covariates. This reduces the model approximately to a linear mixed model with the unit of analysis being community. Because the number of communities is typically small in community-intervention studies, we apply the small-sample inference method of Kenward and Roger (1997, Biometrics 53, 983-997) to the linear mixed model of second stage. We show by simulation that, under typical settings of community-intervention studies, the proposed approach improves the inference on the intervention-effect parameter uniformly over both the linearized mixed-effect approach and the adaptive Gaussian quadrature approach for generalized linear mixed models. This work is motivated by a series of large randomized trials that test community interventions for promoting cancer preventive lifestyles and behaviors. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 2 de 2 | |
| Autor: | Brumback, Babette - Greenland, Sander - Redman, Mary - Kiviat, Nancy - Diehr, Paula - |
| Título: | The intensity-score approach to adjusting for confounding |
| Fuente: | Biometrics. v.59, n.2. International Biometric Society |
| Páginas: | pp. 274-285 |
| Año: | jun. 2003 |
| Resumen: | In a recent article on the efficacy of antihypertensive therapy, Berlowitz et al. (1998, New England Journal of Medicine 339, 1957-1963) introduced an ad hoc method of adjusting for serial confounding assessed via an intensity score, which records cumulative differences over time between therapy actually received and therapy predicted by prior medical history. Outcomes are subsequently regressed on the intensity score and baseline covariates to determine whether intense treatment or exposure predicts a favorable response. We use a structural nested mean model to derive conditions sufficient for interpreting the Berlowitz results causally. We also consider a modified approach that scales the intensity at each time by the inverse expected treatment given prior medical history. This leads to a simple, two-step implementation of G-estimation if we assume a nonstandard but useful structural nested mean model in which subjects less likely to receive treatment are more likely to benefit from it. These modeling assumptions apply, for example, to health services research contexts in which differential access to care is a primary concern. They are also plausible in our analysis of the causal effect of potent antiretroviral therapy on change in CD4 cell count, because men in the sample who are less likely to initiate treatment when baseline CD4 counts are high are more likely to experience large positive changes. We further extend the methods to accomodate repeated outcomes and time-varying effects of time-varying exposures. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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