The International Journal of Biostatistics - new journal (fwd)

["Clarice G.B. Demetrio" <clarice@carpa.ciagri.usp.br>]

---------- Forwarded message ----------
Date: Tue, 10 Jan 2006 15:49:46 -0800 (PST)
From: Nicholas P. Jewell <mm-9510-505761@bepress.com>
To: clarice@carpa.ciagri.usp.br
Subject: The International Journal of Biostatistics - new journal



The Berkeley Electronic Press - together with editors Nicholas P. Jewell,
David Freedman, and Mark van der Laan of University of California,
Berkeley; Raymond Carroll of Texas A&M University; and James Robins of
Harvard University - is pleased to announce the launch of the International
Journal of Biostatistics (IJB). IJB is a peer-reviewed e-journal publishing
new biostatistical models and methods, new statistical theory, as well as
original applications of statistical methods, for important practical
problems arising from the biological, medical, public health, and
agricultural sciences. To view any of the recently published articles,
simply click on the links below. Full citations and abstracts, as well as
additional IJB details, follow at bottom of message.



Xiang Guo and Anastasios Tsiatis "A Weighted Risk Set Estimator for Survival
Distributions in Two-Stage Randomization Designs with Censored Survival Data".
http://www.bepress.com/ijb/vol1/iss1/1

Nicholas P. Jewell and Biao Wm. Lu "Some Variants of the Backcalculation
Method for Estimation of Disease Incidence: An Application to Multiple
Sclerosis Data from the Faroe Islands".
http://www.bepress.com/ijb/vol1/iss1/2

Moulinath Banerjee and Jon A. Wellner "Score Statistics for Current Status
Data: Comparisons with Likelihood Ratio and Wald Statistics".
http://www.bepress.com/ijb/vol1/iss1/3

Mark J. van der Laan, Maya L. Petersen, and Marshall M. Joffe
"History-Adjusted Marginal Structural Models and Statically-Optimal
Dynamic Treatment Regimens".
http://www.bepress.com/ijb/vol1/iss1/4

Ian W. McKeague and Yichuan Zhao "Comparing Distribution Functions Via
Empirical Likelihood".
http://www.bepress.com/ijb/vol1/iss1/5



__________________________
ABOUT INTERNATIONAL JOURNAL OF BIOSTATISTICS

The International Journal of Biostatistics (IJB) covers the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework, including advances in biostatistical computing. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems. For more details, or to submit your next paper, visit:

http://www.bepress.com/ijb


EDITORIAL BOARD

Ron Brookmeyer		Johns Hopkins University
Daniel Commenges	Inserm, Bordeaux
Christl Donnelly	Imperial College, London
Jack Kalbfleisch	University of Michigan
Charles Kooperberg	Fred Hutchinson Cancer Research Center
Michael R. Kosorok	University of Wisconsin, Madison
Ian McKeague		Columbia University
Stephan Morgenthaler	L'Ecole Polytechnique Fédérale de Lausanne
Patricia Solomon	University of Adelaide

__________________________
ABSTRACTS & CITATIONS OF NEWLY PUBLISHED ARTICLES


Xiang Guo and Anastasios Tsiatis (2005) "A Weighted Risk Set Estimator for Survival Distributions in Two-Stage Randomization Designs with Censored Survival Data", The International Journal of Biostatistics: Vol. 1: No. 1, Article 1.
http://www.bepress.com/ijb/vol1/iss1/1

ABSTRACT:
In many clinical trials related to diseases such as cancers and HIV, patients are treated by different combinations of therapies. This leads to two-stage designs, where patients are initially randomized to a primary therapy and then depending on disease remission and patients' consent, a maintenance therapy will be randomly assigned. In such designs, the effects of different treatment policies, i.e., combinations of primary and maintenance therapy are of great interest. In this paper, we propose an estimator for the survival distribution for each treatment policy in such two-stage studies with right-censoring using the method of weighted estimation equations within risk sets. We also derive the large-sample properties. The method is demonstrated and compared with other estimators through simulations and applied to analyze a two-stage randomized study with leukemia patients.


Nicholas P. Jewell and Biao Wm. Lu (2005) "Some Variants of the Backcalculation Method for Estimation of Disease Incidence: An Application to Multiple Sclerosis Data from the Faroe Islands", The International Journal of Biostatistics: Vol. 1: No. 1, Article 2.
http://www.bepress.com/ijb/vol1/iss1/2

ABSTRACT:
Backcalculation is a technique that was originally developed for the study of HIV incidence. Here we introduce some variants of the estimation technique that allow for (i) correlation of the unobserved disease incidence counts, and (ii) the use of a smoothing step as part of the maximizing step in the EM algorithm to reduce instability due to small diagnosis counts. Both of these issues can be important in the analysis of small "epidemics". In addition, identification of correlation between diagnosis counts provides indirect evidence of correlation among unobserved incidence counts, hinting at the possibility of an infectious agent. We illustrate the ideas by reconstructing an incidence intensity function for the onset of multiple sclerosis, using data from the Faroe Islands. Previously, this data had been examined statistically, by Joseph, Wolfson & Wolfson (1990), to address the issue of infectiousness of multiple sclerosis. We argue that the incidence function cannot directly shed light on the enigmatic origin of multiple sclerosis in the Faroe Islands during World War II, and, in particular, cannot discriminate between hypotheses of an infectious or environmental agent.


Moulinath Banerjee and Jon A. Wellner (2005) "Score Statistics for Current Status Data: Comparisons with Likelihood Ratio and Wald Statistics", The International Journal of Biostatistics: Vol. 1: No. 1, Article 3.
http://www.bepress.com/ijb/vol1/iss1/3

ABSTRACT:
In this paper we introduce three natural "score statistics" for testing the hypothesis that F(t_0)takes on a fixed value in the context of nonparametric inference with current status data. These three new test statistics have natural interpretations in terms of certain (weighted) L_2 distances, and are also connected to natural "one-sided" scores. We compare these new test statistics with the analogue of the classical Wald statistic and the likelihood ratio statistic introduced in Banerjee and Wellner (2001) for the same testing problem. Under classical "regular" statistical problems the likelihood ratio, score, and Wald statistics all have the same chi-squared limiting distribution under the null hypothesis. In sharp contrast, in this non-regular problem all three statistics have different limiting distributions under the null hypothesis. Thus we begin by establishing the limit distribution theory of the statistics under the null hypothesis, and discuss calculation of the relevant critical points for the test statistics. Once the null distribution theory is known, the immediate question becomes that of power. We establish the limiting behavior of the three types of statistics under local alternatives. We have also compared the power of these five different statistics via a limited Monte-Carlo study. Our conclusions are: (a) the Wald statistic is less powerful than the likelihood ratio and score statistics; and (b) one of the score statistics may have more power than the likelihood ratio statistic for some alternatives.


Mark J. van der Laan, Maya L. Petersen, and Marshall M. Joffe (2005) "History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens", The International Journal of Biostatistics: Vol. 1: No. 1, Article 4.
http://www.bepress.com/ijb/vol1/iss1/4

ABSTRACT:
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a treatment. These models, introduced by Robins, model the marginal distributions of treatment-specific counterfactual outcomes, possibly conditional on a subset of the baseline covariates. Marginal structural models are particularly useful in the context of longitudinal data structures, in which each subject's treatment and covariate history are measured over time, and an outcome is recorded at a final time point. However, the utility of these models for some applications has been limited by their inability to incorporate modification of the causal effect of treatment by time-varying covariates. Particularly in the context of clinical decision making, such time-varying effect modifiers are often of considerable or even primary interest, as they are used in practice to guide treatment decisions for an individual. In this article we propose a generalization of marginal structural models, which we call history-adjusted marginal structural models (HA-MSM). These models allow estimation of adjusted causal effects of treatment, given the observed past, and are therefore more suitable for making treatment decisions at the individual level and for identification of time-dependent effect modifiers. Specifically, a HA-MSM models the conditional distribution of treatment-specific counterfactual outcomes, conditional on the whole or a subset of the observed past up till a time-point, simultaneously for all time-points. Double robust inverse probability of treatment weighted estimators have been developed and studied in detail for standard MSM. We extend these results by proposing a class of double robust inverse probability of treatment weighted estimators for the unknown parameters of the HA-MSM. In addition, we show that HA-MSM provide a natural approach to identifying the dynamic treatment regimen which follows, at each time-point, the history-adjusted (up till the most recent time point) optimal static treatment regimen. We illustrate our results using an example drawn from the treatment of HIV infection.


Ian W. McKeague and Yichuan Zhao (2005) "Comparing Distribution Functions Via Empirical Likelihood", The International Journal of Biostatistics: Vol. 1: No. 1, Article 5.
http://www.bepress.com/ijb/vol1/iss1/5

ABSTRACT:
This paper develops empirical likelihood based simultaneous confidence bands for differences and ratios of two distribution functions from independent samples of right-censored survival data. The proposed confidence bands provide a flexible way of comparing treatments in biomedical settings, and bring empirical likelihood methods to bear on important target functions for which only Wald-type confidence bands have been available in the literature. The approach is illustrated with a real data example.





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