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Recursos bibliográficos en papel y digitales -
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» Resultado: 3 registros
| Registro 1 de 3 | |
| Autor: | Rosenbaum, Paul R. - |
| Título: | Sensitivity analysis for m-estimates, tests, and confidence intervals in matched observational studies |
| Fuente: | Biometrics. v.63, n.2. International Biometric Society |
| Páginas: | pp. 456-464 |
| Año: | jun. 2007 |
| Resumen: | Huber’s m-estimates use an estimating equation in which observations are permitted a controlled level of influence. The family of m-estimates includes least squares and maximum likelihood, but typical applications give extreme observations limited weight. Maritz proposed methods of exact and approximate permutation inference for m-tests, confidence intervals, and estimators, which can be derived from random assignment of paired subjects to treatment or control. In contrast, in observational studies, where treatments are not randomly assigned, subjects matched for observed covariates may differ in terms of unobserved covariates, so differing outcomes may not be treatment effects. In observational studies, a method of sensitivity analysis is developed for m-tests, m-intervals, and m-estimates: it shows the extent to which inferences would be altered by biases of various magnitudes due to nonrandom treatment assignment. The method is developed for both matched pairs, with one treated subject matched to one control, and for matched sets, with one treated subject matched to one or more controls. The method is illustrated using two studies: (i) a paired study of damage to DNA from exposure to chromium and nickel and (ii) a study with one or two matched controls comparing side effects of two drug regimes to treat tuberculosis. The approach yields sensitivity analyses for: (i) m-tests with Huber’s weight function and other robust weight functions, (ii) the permutational t-test which uses the observations directly, and (iii) various other procedures such as the sign test, Noether’s test, and the permutation distribution of the efficient score test for a location family of distributions. Permutation inference with covariance adjustment is briefly discussed. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 2 de 3 | |
| Autor: | Rosenbaum, Paul R. - |
| Título: | Attributable effects in case2-studies |
| Fuente: | Biometrics. v.61, n.1. International Biometric Society |
| Páginas: | pp. 246-253 |
| Año: | mar. 2005 |
| Resumen: | In an effort to determine whether a particular treatment causes a particular outcome event, data are obtained from a database system that records events when they occur, and for such events, the system records exposure to the treatment. That is, the system records information about cases. The system provides no information about events that might have occurred but did not, that is, about units which are not cases. Roughly speaking, we know the number of successes for two proportions, treated and control, but not the numbers of trials or units for these proportions; indeed, the concept of a "trial" may be somewhat vague. With no further information, the situation is quite hopeless. However, an interesting strategy that is sometimes used entails identifying two types of cases whose origin is entirely different so that it is known the cases of the second type were definitely not affected by the treatment under study. This strategy - the case-case or case 2-study - seems to have been reinvented independently many times, and has recently been offered as a general strategy for infectious disease epidemiology by McCarthy and Giesecke (1999, International Journal of Epidemiology 28, 764-768). Can this strategy permit estimation of the number of cases caused by the treatment? Using attributable effects in a new way, a method of exact inference is proposed, along with a large sample approximation. Two examples are discussed: one concerning the effects of daytime running lights (DRLs) on the risk of multivehicle accidents; the other concerning the origin of a Salmonella infection. A counterexample with superficially similar appearance is also discussed concerning suicide rates following the publication of Final Exit; here, the treatment may alter the outcome, or it may alter the type, and the attributable effect cannot be estimated. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 3 de 3 | |
| Autor: | Rosenbaum, Paul R. - |
| Título: | The Case-Only Odds Ratio as a Causal Parameter |
| Fuente: | Biometrics. v.60, n.1. International Biometric Society |
| Páginas: | pp. 233-240 |
| Año: | mar. 2004 |
| Resumen: | In the simplest case-only design, cases of a disease are cross-classified into a 2 × 2 table describing a genotype attribute and exposure to some environmental agent. In some instances, the genetic attribute has described inherited genes; in other instances, it has described mutations, for instance, damage to proto-oncogenes or tumor suppressor genes leading to cancer. Here, the population case-only odds ratio is written as a causal parameter in terms of potential outcomes with and without exposure to the agent. It is shown that the case-only odds ratio makes sense as a causal parameter with inherited genes, but its magnitude does not have a causal interpretation with mutations, although deviations from 1 do provide information. The difference is that the environmental agent certainly did not cause an individual to inherit particular genes, but it may have caused the mutation. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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