1,721,349 research outputs found
Semi‐parametric accelerated failure‐time model: A useful alternative to the proportional‐hazards model in cancer clinical trials
No full textThe accelerated failure-time (AFT) model has been long recognized as a useful alternative to the proportional-hazards (PH) model. Semi-parametric AFT model has been known since 1981. Its use has been hampered by the difficulty in solving the estimating equations for the model's coefficients. In recent years, however, important developments have taken place regarding the methods of solving the equations. In this article, we briefly review the developments, focusing mainly on rank-based estimation. We conduct a simulation study that directly focuses on the applicability of the model in the context of (cancer) clinical trials. We also investigate the robustness of the AFT model to the omission of covariates. Finally, we conduct a meta-analysis of multiple clinical trials in gastric cancer to illustrate the benefits of the use of the model in practice.The author thanks the GASTRIC (Global Advanced/Adjuvant Stomach Tumor Research International Collaboration) Group for permission to use their data. The investigators who contributed to GASTRIC are listed in References 26,30. The author thanks the anonymous referees for comments that helped in improving the contents of the manuscript. The author declares no conflict of interes
Survival analysis: Methods for analyzing data with censored observations
No full textCensoring occurs when we do not observe exactly the value that we are interested in, but we only learn about some bounds for it. For instance, an observation is right-censored (left-censored) when it is smaller (larger) than the true value. Censoring is most often encountered when observing a time to event, i.e., the time that elapses between a welldefined starting moment until a particular event of interest (for example, the age until the first dental caries). However, it may apply to any measurement or observation. For instance, left- and right-censoring applies to diagnostic assays with, respectively, a lower and an upper limit of detection. The presence of censored observations has important consequences for the statistical analysis. This is because, in such a case, the use of classical statistics (such as, e.g., the sample mean) or statistical models (such as, e.g., linear regression) will result in biased results. Analysis of data that include censored observations requires the use of methods that take explicitly into account censoring. Collectively, in medicine, these methods are referred to as survival analysis. In this article, we provide a review of the basic (parametric and non-parametric) statistical methods of survival analysis
Validation of surrogate endpoints from multiple randomized clinical trials with a failure-time true endpoint
No full textnot availablenot availabl
Over-accrual in Bayesian adaptive trials with continuous futility stopping
No full textBackground: We explore frequentist operating characteristics of a Bayesian adaptive design that allows continuous early stopping for futility. In particular, we focus on the power versus sample size relationship when more patients are accrued than originally planned. Methods: We consider the case of a phase II single-arm study and a Bayesian phase II outcome-adaptive randomization design. For the former, analytical calculations are possible; for the latter, simulations are conducted. Results: Results for both cases show a decrease in power with an increasing sample size. It appears that this effect is due to the increasing cumulative probability of incorrectly stopping for futility. Conclusion: The increase in cumulative probability of incorrectly stopping for futility is related to the continuous nature of the early stopping, which increases the number of interim analyses with accrual. The issue can be addressed by, for instance, delaying the start of testing for futility, reducing the number of futility tests to be performed or by setting stricter criteria for concluding futility.The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Hercules Foundation and the Flemish Government – Department EWI
A hidden Markov-model for gene mapping based on whole-genome next generation sequencing data
No full textThe analysis of polygenic, phenotypic characteristics such as quantitative traits or inheritable diseases requires reliable scoring of many genetic markers covering the entire genome. The advent of high-throughput sequencing technologies provides a new way to evaluate large numbers of single nucleotide polymorphisms as genetic markers. Combining the technologies with pooling of segregants, as performed in bulk segregant analysis, should, in principle, allow the simultaneous mapping of multiple genetic loci present throughout the genome. We propose a hidden Markov-model to analyze the marker data obtained by the bulk segregant next generation sequencing. The model includes several states, each associated with a different probability of observing the same/different nucleotide in an offspring as compared to the parent. The transitions between the molecular markers imply transitions between the states of the model. After estimating the transition probabilities and state-related probabilities of nucleotide (dis)similarity, the most probable state for each SNP is selected. The most probable states can then be used to indicate which genomic regions may be likely to contain trait-related genes. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast. Software is written in R. R-functions, R-scripts and documentation are available on www.ibiostat.be/software/bioinformatics.The authors are grateful to Steve Swinnen, Thiago Pais, Maria R. Foulquie-Moreno, and Johan M. Thevelein of the Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU Leuven and Department of Molecular Microbiology, VIB for providing the data. This work was supported by University Hasselt [B09N106 to J.C.] and the IAP Research Network of the Belgian state (Belgian Science Policy) [P7/06 to J.C. and T.B.]
Computational methods in HDXMS
No full textHydrogen/Deuterium exchange (HDX) has been applied, since the 1930s, as an analytical tool to study the structure and dynamics of (small) biomolecules. The popularity of using HDX to study proteins increased drastically in the last two decades due to the successful combination with mass spectrometry (MS). Together with this growth in popularity, several technological advances have been made, such as improved quenching and fragmentation. As a consequence of these experimental improvements and the increased use of protein-HDXMS, large amounts of complex data are generated, which require appropriate analysis. Computational analysis of HDXMS requires several steps. A typical workflow for proteins consists of identification of (non-)deuterated peptides or fragments of the protein under study (local analysis), or identification of the deuterated protein as a whole (global analysis); determination of the deuteration level; estimation of the protection extent or exchange rates of the labile backbone amide hydrogen atoms; and a statistically sound interpretation of the estimated protection extent or exchange rates. Several algorithms, specifically designed for HDX analysis, have been proposed. They range from procedures that focus on one specific step in the analysis of HDX data to complete HDX workflow analysis tools. In this review, we provide an overview of the computational methods and discuss outstanding challenges
- …
