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  • Articles  (101,738)
  • 2005-2009  (79,345)
  • 1965-1969  (19,445)
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  • Articles  (101,738)
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  • 1
    Electronic Resource
    Electronic Resource
    A Regression Model for Dependent Gap Times (2006)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 2 (2006), S. 1 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: A natural choice of time scale for analyzing recurrent event data is the ``gap" (or soujourn) time between successive events. In many situations it is reasonable to assume correlation exists between the successive events experienced by a given subject. This paper looks at the problem of extending the accelerated failure time (AFT) model to the case of dependent recurrent event data via intensity modeling. Specifically, the accelerated gap times model of Strawderman (2005), a semiparametric intensity model for independent gap time data, is extended to the case of multiplicative gamma frailty. As argued in Aalen & Husebye (1991), incorporating frailty captures the heterogeneity between subjects and the ``hazard" portion of the intensity model captures gap time variation within a subject. Estimators are motivated using semiparametric efficiency theory and lead to useful generalizations of the rank statistics considered in Strawderman (2005). Several interesting distinctions arise in comparison to the Cox-Andersen-Gill frailty model (e.g., Nielsen et al, 1992; Klein, 1992). The proposed methodology is illustrated by simulation and data analysis.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol2/iss1/1
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  • 2
    Electronic Resource
    Electronic Resource
    Relationship between Derivatives of the Observed and Full Loglikelihoods and Application to Newton-Raphson Algorithm (2006)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 2 (2006), S. 4 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: In the case of incomplete data we give general relationships between the first and second derivatives of the loglikelihood relative to the full and the incomplete observation set-ups. In the case where these quantities are easy to compute for the full observation set-up we propose to compute their analogue for the incomplete observation set-up using the above mentioned relationships: this involves numerical integrations. Once we are able to compute these quantities, Newton-Raphson type algorithms can be applied to find the maximum likelihood estimators, together with estimates of their variances. We detail the application of this approach to parametric multiplicative frailty models and we show that the method works well in practice using both a real data and a simulated example. The proposed algorithm outperforms a Newton-Raphson type algorithm using numerical derivatives.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol2/iss1/4
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  • 3
    Electronic Resource
    Electronic Resource
    Survival Instantaneous Log-odds Ratio from Empirical Functions (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 7 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: The objective of this work is to introduce a new method called the Survivorship Instantaneous Log-odds Ratios (SILOR); to illustrate the creation of SILOR from empirical bivariate survival functions; to also derive standard errors of estimation; to compare results with those derived from logistic regression. Hip fracture, AGE and BMI from the Third National Health and Nutritional Examination Survey (NHANES III) were used to calculate empirical survival functions for the adverse health outcome (AHO) and non-AHO. A stable copula was used to create a parametric bivariate survival function, that was fitted to the empirical bivariate survival function. The bivariate survival function had SILOR contours which are not constant. The proposed method has better advantages than logistic regression by following two reasons. The comparison deals with (i) the shapes of the survival surfaces, S(X1, X2), and (ii) the isobols of the log-odds ratios. When using logistic regression the survival surface is either a hyper plane or at most a conic section. Our approach preserves the shape of the survival surface in two dimensions, and the isobols are observed in every detail instead of being overly smoothed by a regression with no more than a second degree polynomial. The present method is straightforward, and it captures all but random variability of the data.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/7
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  • 4
    Electronic Resource
    Electronic Resource
    Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 3 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: Marginal structural models (MSM) are an important class of models in causal inference. Given a longitudinal data structure observed on a sample of n independent and identically distributed experimental units, MSM model the counterfactual outcome distribution corresponding with a static treatment intervention, conditional on user-supplied baseline covariates. Identification of a static treatment regimen-specific outcome distribution based on observational data requires, beyond the standard sequential randomization assumption, the assumption that each experimental unit has positive probability of following the static treatment regimen. The latter assumption is called the experimental treatment assignment (ETA) assumption, and is parameter-specific. In many studies the ETA is violated because some of the static treatment interventions to be compared cannot be followed by all experimental units, due either to baseline characteristics or to the occurrence of certain events over time. For example, the development of adverse effects or contraindications can force a subject to stop an assigned treatment regimen.In this article we propose causal effect models for a user-supplied set of realistic individualized treatment rules. Realistic individualized treatment rules are defined as treatment rules which always map into the set of possible treatment options. Thus, causal effect models for realistic treatment rules do not rely on the ETA assumption and are fully identifiable from the data. Further, these models can be chosen to generalize marginal structural models for static treatment interventions. The estimating function methodology of Robins and Rotnitzky (1992) (analogue to its application in Murphy, et. al. (2001) for a single treatment rule) provides us with the corresponding locally efficient double robust inverse probability of treatment weighted estimator. In addition, we define causal effect models for "intention-to-treat" regimens. The proposed intention-to-treat interventions enforce a static intervention until the time point at which the next treatment does not belong to the set of possible treatment options, at which point the intervention is stopped. We provide locally efficient estimators of such intention-to-treat causal effects.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/3
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  • 5
    Electronic Resource
    Electronic Resource
    Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 6 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: Consider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. A statically optimal individualized treatment rule (as introduced in van der Laan et. al. (2005), Petersen et. al. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. In particular, Petersen et. al. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. Petersen et. al. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. The current article provides an important advance on Petersen et. al. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. However, failure to capture all confounding comes at a price; the static optimality of the resulting rules becomes origin-specific. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/6
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  • 6
    Electronic Resource
    Electronic Resource
    The Analysis of Bivariate Truncated Data Using the Clayton Copula Model (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 8 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: In individuals infected with human immunodeficiency virus (HIV), distributions of quantitative HIV RNA measurements may be highly left-censored due to values falling below assay detection limits (DL). It is of the interest to find the relationship between plasma and semen viral loads. To address this type of problem, we developed an empirical goodness-of-fit test to check the Clayton model assumption for bivariate truncated data. We also used truncated tau to estimate the dependence parameter in the Clayton model for this type of data. It turns out that the proposed methodology works for both truncated and fixed left censored bivariate data. The proposed test procedure is demonstrated using an HIV data set, and statistical inference is drawn based on corresponding test result.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/8
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  • 7
    Electronic Resource
    Electronic Resource
    A Doubly Robust Censoring Unbiased Transformation (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 4 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/4
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  • 8
    Electronic Resource
    Electronic Resource
    Modeling the Effect of Alzheimer's Disease on Mortality (2007)
    Berkeley, Calif. : Berkeley Electronic Press (now: De Gruyter)
    The @international journal of biostatistics 3 (2007), S. 13 
    Details
    ISSN: 1557-4679
    Source: Berkeley Electronic Press Academic Journals
    Topics: Biology , Mathematics , Medicine
    Notes: Mortality rate ratios and the associated proportional hazards models have been used to summarize the effect of Alzheimer's disease on longevity. However, the mortality rate ratios vary by age and therefore do not provide a simple parsimonious summary of the effect of the disease on lifespan. Instead, we propose a new parameter that is defined by an additive multistate model. The proposed multistate model accounts for different stages of disease progression. The underlying assumption of the model is that the effect of disease on mortality is to add a constant amount to death rates once the disease progresses from an early to late stage. We explored the properties of the proposed model; in particular the behavior of the mortality rate ratio and median survival that is induced by the model. We combined information from several data sources to estimate the parameter in our model. We found that the effect of Alzheimer's disease on longevity is to increase the absolute annual risk of death by about 8% once a person progressed to late stage disease. Most importantly, we find that this additive effect is the same regardless of the patients' age or gender. Thus, the proposed additive multi-state model provides a parsimonious and clinically interpretable description of the effects of Alzheimer's disease on mortality.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/ijb/vol3/iss1/13
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  • 9
    Electronic Resource
    Electronic Resource
    Maximum Likelihood Estimation of a Unit Root Bilinear Model with an Application to Prices (2005)
    Cambridge, Mass. : Berkeley Electronic Press (now: De Gruyter)
    Studies in nonlinear dynamics and econometrics 9.2005, 1, art4 
    Details
    ISSN: 1081-1826
    Source: Berkeley Electronic Press Academic Journals
    Topics: Mathematics , Economics
    Notes: We estimate a unit root bilinear process using the Maximum Likelihood method with log-likelihood function constructed by means of the Kalman filter, and evaluate the finite sample properties of this estimator.One hundred and five world-wide price series are tested for unit root bilinearity applying the test suggested by Charemza et al. (forthcoming). Applying the Maximum Likelihood estimator based on the Kalman filter, the null hypothesis of no bilinearity is rejected for 39 out of 105 series at the 5% level of significance. Most of the significant unit root bilinear coefficient estimates are explosive.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/snde/vol9/iss1/art4
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  • 10
    Electronic Resource
    Electronic Resource
    Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model (2005)
    Cambridge, Mass. : Berkeley Electronic Press (now: De Gruyter)
    Studies in nonlinear dynamics and econometrics 9.2005, 2, art5 
    Details
    ISSN: 1081-1826
    Source: Berkeley Electronic Press Academic Journals
    Topics: Mathematics , Economics
    Notes: We develop a new Markov Chain Monte Carlo procedure for a time series regression model truncated by upper and lower bounds. The regression error term is assumed to follow an ARMA--GARCH process. We use a convergence diagnostics with a simultaneous test of mean and covariance stationarity and discuss model selection criteria. Using MCMC procedure we test the purchasing power parity theory for the Japanese yen controlled to fluctuate in a narrow band and find that the theory is supported if double truncation is incorporated in estimation.
    Type of Medium: Electronic Resource
    URL: http://www.bepress.com/snde/vol9/iss2/art5
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