Malka Gorfine | Tel Aviv University (original) (raw)
Papers by Malka Gorfine
International Psychogeriatrics, Mar 1, 1996
Biometrics, Aug 5, 2010
In this work, we provide a new class of frailty-based competing risks models for clustered failur... more In this work, we provide a new class of frailty-based competing risks models for clustered failure times data. This class is based on expanding the competing risks model of Prentice et al. (1978, Biometrics 34, 541-554) to incorporate frailty variates, with the use of cause-specific proportional hazards frailty models for all the causes. Parametric and nonparametric maximum likelihood estimators are proposed. The main advantages of the proposed class of models, in contrast to the existing models, are: (1) the inclusion of covariates; (2) the flexible structure of the dependency among the various types of failure times within a cluster; and (3) the unspecified within-subject dependency structure. The proposed estimation procedures produce the most efficient parametric and semiparametric estimators and are easy to implement. Simulation studies show that the proposed methods perform very well in practical situations.
Statistical Methods in Medical Research, Mar 3, 2010
A longitudinal discriminant analysis is applied to build predictive models based on repeated meas... more A longitudinal discriminant analysis is applied to build predictive models based on repeated measurements of serum hepatitis C virus RNA. These models are evaluated through the partial area under the receiver operating curve index (PA index) and, the final selection of the best model is based on cross-validated estimates of the PA index. Models are compared by building 95% bootstrap confidence interval for the difference in PA index between two models. Data from a randomised trial, in which chronic HCV patients were enrolled, are used to illustrate the application of the proposed method to predict treatment outcome.
Journal of The Royal Statistical Society Series B-statistical Methodology, Jul 8, 2003
Psychopharmacology, Jun 1, 1995
Onset and time course of antidepressant effect were examined in 47 patients with major depressive... more Onset and time course of antidepressant effect were examined in 47 patients with major depressive disorder who had been randomly assigned to twice weekly bilateral, brief pulse electroconvulsive therapy plus one simulated treatment per week (ECTx2) or to a three times weekly schedule of administration (ECTx3). Rapid improvement was observed in the ECTx3 group in whom the number of real ECTs to 30% reduction on the Hamilton Depression Scale (HAM-D) was 3.2 +/- 1.90, administered over 7.3 +/- 4.43 days and to 60% reduction, 5.9 +/- 3.09 real ECTs over 13.7 +/- 7.21 days. Among the responders in both groups combined, 24.3 +/- 29.58% of the overall improvement in HAM-D was contributed by the first real ECT, 60.9 +/- 28.13% by the first four real ECTs and 91.6 +/- 25.82% by the first eight. Although 85.3% of the responders had reached 60% HAM-D improvement after eight ECTs, a clinically significant minority (14.7%) responded later in the course (ECT 9-12). However, response was predictable on the basis of symptomatic improvement (30% on the HAM-D) by the sixth real ECT. Thirty-three out of 34 responders would have been correctly identified by this criterion and only 2 out of 13 non-responders mis-identified (P < 0.000001). Once achieved, the antidepressant effect was stable, without continuation pharmacotherapy, until 1 week after the last treatment and on lithium carbonate (Li) or Li plus clomipramine for a further 3 weeks. These findings confirm the clinical impression that ECT is a rapidly effective treatment for major depression with a shorter latency than generally reported for antidepressant drugs.(ABSTRACT TRUNCATED AT 250 WORDS)
Applied statistics, Mar 13, 2023
Journal of the American Statistical Association, Jun 22, 2023
Journal of the American Statistical Association, Jan 2, 2018
Bulletin of Mathematical Biology, Nov 1, 2003
In this paper we introduce a simple framework which provides a basis for estimating parameters an... more In this paper we introduce a simple framework which provides a basis for estimating parameters and testing statistical hypotheses in complex models. The only assumption that is made in the model describing the process under study, is that the deviations of the observations from the model have a multivariate normal distribution. The application of the statistical techniques presented in this paper may have considerable utility in the analysis of a wide variety of complex biological and epidemiological models. To our knowledge, the model and methods described here have not previously been published in the area of theoretical immunology.
Psychiatric Genetics, 1997
arXiv (Cornell University), Apr 12, 2022
Time-to-event analysis (survival analysis) is used when the response of interest is the time unti... more Time-to-event analysis (survival analysis) is used when the response of interest is the time until a pre-specified event occurs. Time-to-event data are sometimes discrete either because time itself is discrete or due to grouping of failure times into intervals or rounding off measurements. In addition, the failure of an individual could be one of several distinct failure types, known as competing risks (events). Most methods and software packages for survival regression analysis assume that time is measured on a continuous scale. It is well-known that naively applying standard continuous-time models with discrete-time data may result in biased estimators of the discrete-time models. The Python package PyDTS, for simulating, estimating and evaluating semi-parametric competing-risks models for discrete-time survival data, is introduced. The package implements a fast procedure that enables including regularized regression methods, such as LASSO and elastic net, among others. A simulation study showcases flexibility and accuracy of the package. The utility of the package is demonstrated by analysing the Medical Information Mart for Intensive Care (MIMIC)-IV dataset for prediction of hospitalization length of stay.
Genetic Epidemiology, Jun 7, 2020
There are numerous statistical models used to identify individuals at high risk of cancer due to ... more There are numerous statistical models used to identify individuals at high risk of cancer due to inherited mutations. Mendelian models predict future risk of cancer by using family history with estimated cancer penetrances (age- and sex-specific risk of cancer given the genotype of the mutations) and mutation prevalences. However, there is often residual risk heterogeneity across families even after accounting for the mutations in the model, due to environmental or unobserved genetic risk factors. We aim to improve Mendelian risk prediction by incorporating a frailty model that contains a family-specific frailty vector, impacting the cancer hazard function, to account for this heterogeneity. We use a discrete uniform population frailty distribution and implement a marginalized approach that averages each family’s risk predictions over the family’s frailty distribution. We apply the proposed approach to improve breast cancer prediction in BRCAPRO, a Mendelian model that accounts for inherited mutations in the BRCA1 and BRCA2 genes to predict breast and ovarian cancer. We evaluate the proposed model’s performance in simulations and real data from the Cancer Genetics Network and show improvements in model calibration and discrimination. We also discuss alternative approaches for incorporating frailties and their strengths and limitations.
American Journal of Epidemiology, Feb 10, 2021
Research on mortality associated with exposure to the Holocaust is relevant for a better understa... more Research on mortality associated with exposure to the Holocaust is relevant for a better understanding of the effects of genocides on survivors. To our knowledge, previous studies have not investigated the long-term cause-specific mortality of Holocaust survivors. We compared mortality rates among Israelis born in European countries controlled by the Nazis during World War II with those among Israelis of European descent who did not have this exposure. Records of 22,671 people (45% women; 5,042 survivors) from the population-based Jerusalem Perinatal Study (1964–1976) were linked to the Israeli Population Registry, which was updated through 2016. Cox models were used for analysis, with 2-sided tests of statistical significance. Risk of all-cause mortality was higher among exposed women (hazard ratio (HR) = 1.15, 95% confidence interval (CI): 1.05, 1.27) than in unexposed women. No association was found between Holocaust exposure and male all-cause mortality. In both sexes, survivors had higher cancer-specific mortality (HR = 1.17 (95% CI: 1.01, 1.35) in women and HR = 1.14 (95% CI: 1.01, 1.28) in men). Exposed men also had excess mortality due to coronary heart disease (HR = 1.39, 95% CI: 1.09, 1.77) and lower mortality from other known causes combined (HR = 0.86, 95% CI: 0.75, 0.99). In summary, experiencing the Holocaust was associated with excess all-cause and cancer-specific mortality in women and cancer- and coronary heart disease–specific mortality in men.
Propensity score methods are widely used to analyze observational studies in which patient charac... more Propensity score methods are widely used to analyze observational studies in which patient characteristics might not be balanced by treatment group. These methods assume that exposure, or treatment assignment, is error-free, but in reality these variables can be subject to measurement error. This arises in the context of comparative effectiveness research, using observational administrative claims data in which accurate procedural codes are not always available. When using propensity score based methods, this error affects both the exposure variable directly, as well as the propensity score. We propose a two step maximum likelihood approach using validation data to adjust for the measurement error. First, we use a likelihood approach to estimate an adjusted propensity score. Using the adjusted propensity score, we then use a likelihood approach on the outcome model to adjust for measurement error in the exposure variable directly. In addition, we show the bias introduced when using error-prone treatment in the inverse probability weighting (IPW) estimator and propose an approach to eliminate this bias. Simulations show our proposed approaches reduce the bias and mean squared error (MSE) of the treatment effect estimator compared to using the error-prone treatment assignment.
Biometrics, Aug 24, 2006
The relationship between nutrient consumption and chronic disease risk is the focus of a large nu... more The relationship between nutrient consumption and chronic disease risk is the focus of a large number of epidemiological studies where food frequency questionnaires (FFQ) and food records are commonly used to assess dietary intake. However, these self-assessment tools are known to involve substantial random error for most nutrients, and probably important systematic error as well. Study subject selection in dietary intervention studies is sometimes conducted in two stages. At the first stage, FFQ-measured dietary intakes are observed and at the second stage another instrument, such as a 4-day food record, is administered only to participants who have fulfilled a prespecified criterion that is based on the baseline FFQ-measured dietary intake (e.g., only those reporting percent energy intake from fat above a prespecified quantity). Performing analysis without adjusting for this truncated sample design and for the measurement error in the nutrient consumption assessments will usually provide biased estimates for the population parameters. In this work we provide a general statistical analysis technique for such data with the classical additive measurement error that corrects for the two sources of bias. The proposed technique is based on multiple imputation for longitudinal data. Results of a simulation study along with a sensitivity analysis are presented, showing the performance of the proposed method under a simple linear regression model.
PubMed, Jun 1, 1995
Twenty-eight of 34 patients with major depression who completed a course of electroconvulsive the... more Twenty-eight of 34 patients with major depression who completed a course of electroconvulsive therapy (ECT) and were classified as responders were administered lithium carbonate (Li) continuation therapy in the context of an open, prospective study. Twenty-four patients were followed for 6 months or until relapse; four patients dropped out of follow-up while still in remission. The probability of completing 6 months without relapse (by survival analysis, including the patients who dropped out as censored observations) was 65%. The eight patients who relapsed into depression all did so within 13 weeks. They were characterized by a shorter duration of their index depressive episode, a greater likelihood of having suffered an additional depressive episode in the preceding 12 months, and failure of an adequate trial of antidepressant medication before the ECT course. Novel pharmacological strategies may be needed in the post-ECT continuation therapy of patients who have a prior history of relapse and are demonstrably resistant to antidepressant medication.
International Psychogeriatrics, Mar 1, 1996
Biometrics, Aug 5, 2010
In this work, we provide a new class of frailty-based competing risks models for clustered failur... more In this work, we provide a new class of frailty-based competing risks models for clustered failure times data. This class is based on expanding the competing risks model of Prentice et al. (1978, Biometrics 34, 541-554) to incorporate frailty variates, with the use of cause-specific proportional hazards frailty models for all the causes. Parametric and nonparametric maximum likelihood estimators are proposed. The main advantages of the proposed class of models, in contrast to the existing models, are: (1) the inclusion of covariates; (2) the flexible structure of the dependency among the various types of failure times within a cluster; and (3) the unspecified within-subject dependency structure. The proposed estimation procedures produce the most efficient parametric and semiparametric estimators and are easy to implement. Simulation studies show that the proposed methods perform very well in practical situations.
Statistical Methods in Medical Research, Mar 3, 2010
A longitudinal discriminant analysis is applied to build predictive models based on repeated meas... more A longitudinal discriminant analysis is applied to build predictive models based on repeated measurements of serum hepatitis C virus RNA. These models are evaluated through the partial area under the receiver operating curve index (PA index) and, the final selection of the best model is based on cross-validated estimates of the PA index. Models are compared by building 95% bootstrap confidence interval for the difference in PA index between two models. Data from a randomised trial, in which chronic HCV patients were enrolled, are used to illustrate the application of the proposed method to predict treatment outcome.
Journal of The Royal Statistical Society Series B-statistical Methodology, Jul 8, 2003
Psychopharmacology, Jun 1, 1995
Onset and time course of antidepressant effect were examined in 47 patients with major depressive... more Onset and time course of antidepressant effect were examined in 47 patients with major depressive disorder who had been randomly assigned to twice weekly bilateral, brief pulse electroconvulsive therapy plus one simulated treatment per week (ECTx2) or to a three times weekly schedule of administration (ECTx3). Rapid improvement was observed in the ECTx3 group in whom the number of real ECTs to 30% reduction on the Hamilton Depression Scale (HAM-D) was 3.2 +/- 1.90, administered over 7.3 +/- 4.43 days and to 60% reduction, 5.9 +/- 3.09 real ECTs over 13.7 +/- 7.21 days. Among the responders in both groups combined, 24.3 +/- 29.58% of the overall improvement in HAM-D was contributed by the first real ECT, 60.9 +/- 28.13% by the first four real ECTs and 91.6 +/- 25.82% by the first eight. Although 85.3% of the responders had reached 60% HAM-D improvement after eight ECTs, a clinically significant minority (14.7%) responded later in the course (ECT 9-12). However, response was predictable on the basis of symptomatic improvement (30% on the HAM-D) by the sixth real ECT. Thirty-three out of 34 responders would have been correctly identified by this criterion and only 2 out of 13 non-responders mis-identified (P < 0.000001). Once achieved, the antidepressant effect was stable, without continuation pharmacotherapy, until 1 week after the last treatment and on lithium carbonate (Li) or Li plus clomipramine for a further 3 weeks. These findings confirm the clinical impression that ECT is a rapidly effective treatment for major depression with a shorter latency than generally reported for antidepressant drugs.(ABSTRACT TRUNCATED AT 250 WORDS)
Applied statistics, Mar 13, 2023
Journal of the American Statistical Association, Jun 22, 2023
Journal of the American Statistical Association, Jan 2, 2018
Bulletin of Mathematical Biology, Nov 1, 2003
In this paper we introduce a simple framework which provides a basis for estimating parameters an... more In this paper we introduce a simple framework which provides a basis for estimating parameters and testing statistical hypotheses in complex models. The only assumption that is made in the model describing the process under study, is that the deviations of the observations from the model have a multivariate normal distribution. The application of the statistical techniques presented in this paper may have considerable utility in the analysis of a wide variety of complex biological and epidemiological models. To our knowledge, the model and methods described here have not previously been published in the area of theoretical immunology.
Psychiatric Genetics, 1997
arXiv (Cornell University), Apr 12, 2022
Time-to-event analysis (survival analysis) is used when the response of interest is the time unti... more Time-to-event analysis (survival analysis) is used when the response of interest is the time until a pre-specified event occurs. Time-to-event data are sometimes discrete either because time itself is discrete or due to grouping of failure times into intervals or rounding off measurements. In addition, the failure of an individual could be one of several distinct failure types, known as competing risks (events). Most methods and software packages for survival regression analysis assume that time is measured on a continuous scale. It is well-known that naively applying standard continuous-time models with discrete-time data may result in biased estimators of the discrete-time models. The Python package PyDTS, for simulating, estimating and evaluating semi-parametric competing-risks models for discrete-time survival data, is introduced. The package implements a fast procedure that enables including regularized regression methods, such as LASSO and elastic net, among others. A simulation study showcases flexibility and accuracy of the package. The utility of the package is demonstrated by analysing the Medical Information Mart for Intensive Care (MIMIC)-IV dataset for prediction of hospitalization length of stay.
Genetic Epidemiology, Jun 7, 2020
There are numerous statistical models used to identify individuals at high risk of cancer due to ... more There are numerous statistical models used to identify individuals at high risk of cancer due to inherited mutations. Mendelian models predict future risk of cancer by using family history with estimated cancer penetrances (age- and sex-specific risk of cancer given the genotype of the mutations) and mutation prevalences. However, there is often residual risk heterogeneity across families even after accounting for the mutations in the model, due to environmental or unobserved genetic risk factors. We aim to improve Mendelian risk prediction by incorporating a frailty model that contains a family-specific frailty vector, impacting the cancer hazard function, to account for this heterogeneity. We use a discrete uniform population frailty distribution and implement a marginalized approach that averages each family’s risk predictions over the family’s frailty distribution. We apply the proposed approach to improve breast cancer prediction in BRCAPRO, a Mendelian model that accounts for inherited mutations in the BRCA1 and BRCA2 genes to predict breast and ovarian cancer. We evaluate the proposed model’s performance in simulations and real data from the Cancer Genetics Network and show improvements in model calibration and discrimination. We also discuss alternative approaches for incorporating frailties and their strengths and limitations.
American Journal of Epidemiology, Feb 10, 2021
Research on mortality associated with exposure to the Holocaust is relevant for a better understa... more Research on mortality associated with exposure to the Holocaust is relevant for a better understanding of the effects of genocides on survivors. To our knowledge, previous studies have not investigated the long-term cause-specific mortality of Holocaust survivors. We compared mortality rates among Israelis born in European countries controlled by the Nazis during World War II with those among Israelis of European descent who did not have this exposure. Records of 22,671 people (45% women; 5,042 survivors) from the population-based Jerusalem Perinatal Study (1964–1976) were linked to the Israeli Population Registry, which was updated through 2016. Cox models were used for analysis, with 2-sided tests of statistical significance. Risk of all-cause mortality was higher among exposed women (hazard ratio (HR) = 1.15, 95% confidence interval (CI): 1.05, 1.27) than in unexposed women. No association was found between Holocaust exposure and male all-cause mortality. In both sexes, survivors had higher cancer-specific mortality (HR = 1.17 (95% CI: 1.01, 1.35) in women and HR = 1.14 (95% CI: 1.01, 1.28) in men). Exposed men also had excess mortality due to coronary heart disease (HR = 1.39, 95% CI: 1.09, 1.77) and lower mortality from other known causes combined (HR = 0.86, 95% CI: 0.75, 0.99). In summary, experiencing the Holocaust was associated with excess all-cause and cancer-specific mortality in women and cancer- and coronary heart disease–specific mortality in men.
Propensity score methods are widely used to analyze observational studies in which patient charac... more Propensity score methods are widely used to analyze observational studies in which patient characteristics might not be balanced by treatment group. These methods assume that exposure, or treatment assignment, is error-free, but in reality these variables can be subject to measurement error. This arises in the context of comparative effectiveness research, using observational administrative claims data in which accurate procedural codes are not always available. When using propensity score based methods, this error affects both the exposure variable directly, as well as the propensity score. We propose a two step maximum likelihood approach using validation data to adjust for the measurement error. First, we use a likelihood approach to estimate an adjusted propensity score. Using the adjusted propensity score, we then use a likelihood approach on the outcome model to adjust for measurement error in the exposure variable directly. In addition, we show the bias introduced when using error-prone treatment in the inverse probability weighting (IPW) estimator and propose an approach to eliminate this bias. Simulations show our proposed approaches reduce the bias and mean squared error (MSE) of the treatment effect estimator compared to using the error-prone treatment assignment.
Biometrics, Aug 24, 2006
The relationship between nutrient consumption and chronic disease risk is the focus of a large nu... more The relationship between nutrient consumption and chronic disease risk is the focus of a large number of epidemiological studies where food frequency questionnaires (FFQ) and food records are commonly used to assess dietary intake. However, these self-assessment tools are known to involve substantial random error for most nutrients, and probably important systematic error as well. Study subject selection in dietary intervention studies is sometimes conducted in two stages. At the first stage, FFQ-measured dietary intakes are observed and at the second stage another instrument, such as a 4-day food record, is administered only to participants who have fulfilled a prespecified criterion that is based on the baseline FFQ-measured dietary intake (e.g., only those reporting percent energy intake from fat above a prespecified quantity). Performing analysis without adjusting for this truncated sample design and for the measurement error in the nutrient consumption assessments will usually provide biased estimates for the population parameters. In this work we provide a general statistical analysis technique for such data with the classical additive measurement error that corrects for the two sources of bias. The proposed technique is based on multiple imputation for longitudinal data. Results of a simulation study along with a sensitivity analysis are presented, showing the performance of the proposed method under a simple linear regression model.
PubMed, Jun 1, 1995
Twenty-eight of 34 patients with major depression who completed a course of electroconvulsive the... more Twenty-eight of 34 patients with major depression who completed a course of electroconvulsive therapy (ECT) and were classified as responders were administered lithium carbonate (Li) continuation therapy in the context of an open, prospective study. Twenty-four patients were followed for 6 months or until relapse; four patients dropped out of follow-up while still in remission. The probability of completing 6 months without relapse (by survival analysis, including the patients who dropped out as censored observations) was 65%. The eight patients who relapsed into depression all did so within 13 weeks. They were characterized by a shorter duration of their index depressive episode, a greater likelihood of having suffered an additional depressive episode in the preceding 12 months, and failure of an adequate trial of antidepressant medication before the ECT course. Novel pharmacological strategies may be needed in the post-ECT continuation therapy of patients who have a prior history of relapse and are demonstrably resistant to antidepressant medication.