Design and analysis of randomized clinical trials requiring prolonged observation of each patient. I. Introduction and design.

The Medical Research Council has for some years encouraged collaborative clinical trials in leukaemia and other cancers, reporting the results in the medical literature. One unreported result which deserves such publication is the development of the expertise to design and analyse such trials. This report was prepared by a group of British and American statisticians, but it is intended for people without any statistical expertise. Part I, which appears in this issue, discusses the design of such trials; Part II, which will appear separately in the January 1977 issue of the Journal, gives full instructions for the statistical analysis of such trials by means of life tables and the logrank test, including a worked example, and discusses the interpretation of trial results, including brief reports of 2 particular trials. Both parts of this report are relevant to all clinical trials which study time to death, and wound be equally relevant to clinical trials which study time to other particular classes of untoward event: first stroke, perhaps, or first relapse, metastasis, disease recurrence, thrombosis, transplant rejection, or death from a particular cause. Part I, in this issue, collects together ideas that have mostly already appeared in the medical literature, but Part II, next month, is the first simple account yet published for non-statistical physicians of how to analyse efficiently data from clinical trials of survival duration. Such trials include the majority of all clinical trials of cancer therapy; in cancer trials,however, it may be preferable to use these statistical methods to study time to local recurrence of tumour, or to study time to detectable metastatic spread, in addition to studying total survival. Solid tumours can be staged at diagnosis; if this, or any other available information in some other disease is an important determinant of outcome, it can be used to make the overall logrank test for the whole heterogeneous trial population more sensitive, and more intuitively satisfactory, for it will then only be necessary to compare like with like, and not, by chance, Stage I with Stage III

Ethnicity, socioeconomic status, transfusions and risk of hepatitis B and hepatitis C infection

This study identifies the risk factors for hepatitis B virus (HBV) and hepatitis C virus (HCV) and measures the prevalence of hepatitis B surface antigen (HBsAg) and antibody to hepatitis C (anti-HCV) in the general population of Jakarta. A population-based sample of 985 people aged 15 and above was surveyed. Risk factors were identified through questionnaires and home visits. Serum was analysed for HBsAg, antibody to hepatitis B surface antigen (anti-HBs), anti-HCV, aspartate aminotransferase (AST) and alanine aminotransferase (ALT). The seroprevalence was: 4.0% (39/985) for HBsAg, 17.2% (170/985) for anti-HBs, and 3.9% (38/985) for anti-HCV. The risk factors for hepatitis B and hepatitis C infection had little in common. Low socioeconomic status was a strong risk factor for HBsAg (adjusted odds ratio (OR) 18.09; 95% confidence interval (CI) 2.35–139.50). In addition, the Chinese group has 2.97 higher risk of having HBV infection compared with the Malayan ethnic group (adjusted OR 2.97; 95% CI 1.22–7.83). There was moderate positive trend between family size and risk of HBsAg positivity ( P = 0.130). Age over 50 (adjusted OR 14.72; 95% CI 4.35–49.89) and history of transfusion were significant risk factors for hepatitis C (adjusted OR 3.03; 95% CI 1.25–7.33). Hepatitis B and hepatitis C infections have different risk factors in Jakarta, a high risk in population for both diseases. Hepatitis B transmission is associated with low socioeconomic status, Chinese ethnic group and large family size, while hepatitis C is associated with an older age and a history of transfusions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72591/1/j.1440-1746.1997.tb00365.x.pd

Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. analysis and examples.

Part I of this report appeared in the previous issue (Br. J. Cancer (1976) 34,585), and discussed the design of randomized clinical trials. Part II now describes efficient methods of analysis of randomized clinical trials in which we wish to compare the duration of survival (or the time until some other untoward event first occurs) among different groups of patients. It is intended to enable physicians without statistical training either to analyse such data themselves using life tables, the logrank test and retrospective stratification, or, when such analyses are presented, to appreciate them more critically, but the discussion may also be of interest to statisticians who have not yet specialized in clinical trial analyses

Enabling fair pricing on HPC systems with node sharing

Abstract not provide

Highly Conducting pi-Conjugated Molecular Junctions Covalently Bonded to Gold Electrodes

We measure electronic conductance through single conjugated molecules bonded to Au metal electrodes with direct Au-C covalent bonds using the scanning tunneling microscope based break-junction technique. We start with molecules terminated with trimethyltin end groups that cleave off in situ resulting in formation of a direct covalent sigma bond between the carbon backbone and the gold metal electrodes. The molecular carbon backbone used in this study consist of a conjugated pi-system that has one terminal methylene group on each end, which bonds to the electrodes, achieving large electronic coupling of the electrodes to the pi-system. The junctions formed with the prototypical example of 1,4-dimethylenebenzene show a conductance approaching one conductance quantum (G0 = 2e2/h). Junctions formed with methylene terminated oligophenyls with two to four phenyl units show a hundred-fold increase in conductance compared with junctions formed with amine-linked oligophenyls. The conduction mechanism for these longer oligophenyls is tunneling as they exhibit an exponential dependence of conductance with oligomer length. In addition, density functional theory based calculations for the Au-xylylene-Au junction show near-resonant transmission with a cross-over to tunneling for the longer oligomers.Comment: Accepted to the Journal of the American Chemical Society as a Communication

Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models

<p>Abstract</p> <p>Background</p> <p>Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.</p> <p>Results</p> <p>In this paper we propose a logistic kernel machine regression model for binary outcomes. This model relates the disease risk to covariates parametrically, and to genes within a genetic pathway parametrically or nonparametrically using kernel machines. The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome. We show that kernel machine estimation of the model components can be formulated using a logistic mixed model. Estimation hence can proceed within a mixed model framework using standard statistical software. A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect. The methods are illustrated using a prostate cancer data set and evaluated using simulations. An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed.</p> <p>Conclusion</p> <p>Logistic kernel machine regression and its extension generalized kernel machine regression provide a novel and flexible statistical tool for modeling pathway effects on discrete and continuous outcomes. Their close connection to mixed models and attractive performance make them have promising wide applications in bioinformatics and other biomedical areas.</p

The yeast P5 type ATPase, Spf1, regulates manganese transport into the endoplasmic reticulum

The endoplasmic reticulum (ER) is a large, multifunctional and essential organelle. Despite intense research, the function of more than a third of ER proteins remains unknown even in the well-studied model organism Saccharomyces cerevisiae. One such protein is Spf1, which is a highly conserved, ER localized, putative P-type ATPase. Deletion of SPF1 causes a wide variety of phenotypes including severe ER stress suggesting that this protein is essential for the normal function of the ER. The closest homologue of Spf1 is the vacuolar P-type ATPase Ypk9 that influences Mn2+ homeostasis. However in vitro reconstitution assays with Spf1 have not yielded insight into its transport specificity. Here we took an in vivo approach to detect the direct and indirect effects of deleting SPF1. We found a specific reduction in the luminal concentration of Mn2+ in ∆spf1 cells and an increase following it’s overexpression. In agreement with the observed loss of luminal Mn2+ we could observe concurrent reduction in many Mn2+-related process in the ER lumen. Conversely, cytosolic Mn2+-dependent processes were increased. Together, these data support a role for Spf1p in Mn2+ transport in the cell. We also demonstrate that the human sequence homologue, ATP13A1, is a functionally conserved orthologue. Since ATP13A1 is highly expressed in developing neuronal tissues and in the brain, this should help in the study of Mn2+-dependent neurological disorders