Harvard University

About: Harvard University is a education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 208150 authors who have published 530388 publications receiving 38152182 citations. The organization is also known as: Harvard & University of Harvard.

Efficacy of Gefitinib, an Inhibitor of the Epidermal Growth Factor Receptor Tyrosine Kinase, in Symptomatic Patients with Non-Small Cell Lung Cancer: A Randomized Trial

Abstract: ContextMore persons in the United States die from non–small cell lung cancer (NSCLC) than from breast, colorectal, and prostate cancer combined. In preclinical testing, oral gefitinib inhibited the growth of NSCLC tumors that express the epidermal growth factor receptor (EGFR), a mediator of cell signaling, and phase 1 trials have demonstrated that a fraction of patients with NSCLC progressing after chemotherapy experience both a decrease in lung cancer symptoms and radiographic tumor shrinkages with gefitinib.ObjectiveTo assess differences in symptomatic and radiographic response among patients with NSCLC receiving 250-mg and 500-mg daily doses of gefitinib.Design, Setting, and PatientsDouble-blind, randomized phase 2 trial conducted from November 2000 to April 2001 in 30 US academic and community oncology centers. Patients (N = 221) had either stage IIIB or IV NSCLC for which they had received at least 2 chemotherapy regimens.InterventionDaily oral gefitinib, either 500 mg (administered as two 250-mg gefitinib tablets) or 250 mg (administered as one 250-mg gefitinib tablet and 1 matching placebo).Main Outcome MeasuresImprovement of NSCLC symptoms (2-point or greater increase in score on the summed lung cancer subscale of the Functional Assessment of Cancer Therapy-Lung [FACT-L] instrument) and tumor regression (>50% decrease in lesion size on imaging studies).ResultsOf 221 patients enrolled, 216 received gefitinib as randomized. Symptoms of NSCLC improved in 43% (95% confidence interval [CI], 33%-53%) of patients receiving 250 mg of gefitinib and in 35% (95% CI, 26%-45%) of patients receiving 500 mg. These benefits were observed within 3 weeks in 75% of patients. Partial radiographic responses occurred in 12% (95% CI, 6%-20%) of individuals receiving 250 mg of gefitinib and in 9% (95% CI, 4%-16%) of those receiving 500 mg. Symptoms improved in 96% of patients with partial radiographic responses. The overall survival at 1 year was 25%. There were no significant differences between the 250-mg and 500-mg doses in rates of symptom improvement (P = .26), radiographic tumor regression (P = .51), and projected 1-year survival (P = .54). The 500-mg dose was associated more frequently with transient acne-like rash (P = .04) and diarrhea (P = .006).ConclusionsGefitinib, a well-tolerated oral EGFR-tyrosine kinase inhibitor, improved disease-related symptoms and induced radiographic tumor regressions in patients with NSCLC persisting after chemotherapy.

CLARIFY: Software for Interpreting and Presenting Statistical Results

Abstract: Clarify is a program that uses Monte Carlo simulation to convert the raw output of statistical procedures into results that are of direct interest to researchers, without changing statistical assumptions or requiring new statistical models. The program, designed for use with the Stata statistics package, offers a convenient way to implement the techniques described in: Gary King, Michael Tomz, and Jason Wittenberg (2000). "Making the Most of Statistical Analyses: Improving Interpretation and Presentation." American Journal of Political Science 44, no. 2 (April 2000): 347-61. We recommend that you read this article before using the software. Clarify simulates quantities of interest for the most commonly used statistical models, including linear regression, binary logit, binary probit, ordered logit, ordered probit, multinomial logit, Poisson regression, negative binomial regression, weibull regression, seemingly unrelated regression equations, and the additive logistic normal model for compositional data. Clarify Version 2.1 is forthcoming (2003) in Journal of Statistical Software.

mediation: R Package for Causal Mediation Analysis

Abstract: In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.

Handbook of Markov Chain Monte Carlo

Abstract: Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella Reversible jump Markov chain Monte Carlo, Yanan Fan and Scott A. Sisson Optimal proposal distributions and adaptive MCMC, Jeffrey S. Rosenthal MCMC using Hamiltonian dynamics, Radford M. Neal Inference and Monitoring Convergence, Andrew Gelman and Kenneth Shirley Implementing MCMC: Estimating with confidence, James M. Flegal and Galin L. Jones Perfection within reach: Exact MCMC sampling, Radu V. Craiu and Xiao-Li Meng Spatial point processes, Mark Huber The data augmentation algorithm: Theory and methodology, James P. Hobert Importance sampling, simulated tempering and umbrella sampling, Charles J.Geyer Likelihood-free Markov chain Monte Carlo, Scott A. Sisson and Yanan Fan MCMC in the analysis of genetic data on related individuals, Elizabeth Thompson A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data, Brian Caffo, DuBois Bowman, Lynn Eberly, and Susan Spear Bassett Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics, David van Dyk and Taeyoung Park Posterior exploration for computationally intensive forward models, Dave Higdon, C. Shane Reese, J. David Moulton, Jasper A. Vrugt and Colin Fox Statistical ecology, Ruth King Gaussian random field models for spatial data, Murali Haran Modeling preference changes via a hidden Markov item response theory model, Jong Hee Park Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology, Brian Caffo, Roger Peng, Francesca Dominici, Thomas A. Louis, and Scott Zeger MCMC for state space models, Paul Fearnhead MCMC in educational research, Roy Levy, Robert J. Mislevy, and John T. Behrens Applications of MCMC in fisheries science, Russell B. Millar Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand, Filiz Garip and Bruce Western