Malka Gorfine

Publications (27) View all

• Article: A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error.

  David M Zucker, Malka Gorfine, Yi Li, Mahlet G Tadesse, Donna Spiegelman
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  ABSTRACT: Summary Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer.
  Biometrics 02/2013; · 1.83 Impact Factor
• Article: Conditional and marginal estimates in case-control family data – extensions and sensitivity analyses

  Malka Gorfine, Rottem De-Picciotto, Li Hsu
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  ABSTRACT: This work considers two specific estimation techniques for the family-specific proportional hazards model and for the population-averaged proportional hazards model. So far, these two estimation procedures were presented and studied under the gamma frailty distribution mainly because of its simple interpretation and mathematical tractability. Modifications of both procedures for other frailty distributions, such as the inverse Gaussian, positive stable and a specific case of discrete distribution, are presented. By extensive simulations, it is shown that under the family-specific proportional hazards model, the gamma frailty model appears to be robust to frailty distribution mis-specification in both bias and efficiency loss in the marginal parameters. The population-averaged proportional hazards model, is found to be robust under the gamma frailty model mis-specification only under moderate or weak dependency within cluster members.
  Journal of Statistical Computation and Simulation 10/2012; 82(10):1449-1470. · 0.50 Impact Factor
• Article: Calibrated Predictions for Multivariate Competing Risks Model

  Malka Gorfine, D M Zucker, Giovanni Parmigiani, Li Hsu
  Lifetime Data Analysis 10/2012; · 0.92 Impact Factor
• Article: Frailty Models for Familial Risk with Application to Breast Cancer

  Malka Gorfine, Li Hsu, Giovanni Parmigiani
  under review. 03/2012;
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  Available from: Malka Gorfine

  Article: A consistent multivariate test of association based on ranks of distances

  Ruth Heller, Yair Heller, Malka Gorfine
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  ABSTRACT: We are concerned with the detection of associations between random vectors of any dimension. Few tests of independence exist that are consistent against all dependent alternatives. We propose a powerful test that is applicable in all dimensions and is consistent against all alternatives. The test has a simple form and is easy to implement. We demonstrate its good power properties in simulations and on examples.
  01/2012;