artbin: Extended sample size for randomized trials with binary outcomes

We describe the command artbin, which offers various new facilities for the calculation of sample size for binary outcome variables that are not otherwise available in Stata. While artbin has been available since 2004, it has not been previously described in the Stata Journal. artbin has been recently updated to include new options for different statistical tests, methods and study designs, improved syntax, and better handling of noninferiority trials. In this article, we describe the updated version of artbin and detail the various formulas used within artbin in different settings

Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit

<p>Abstract</p> <p>background</p> <p>The pace of novel medical treatments and approaches to therapy has accelerated in recent years. Unfortunately, many potential therapeutic advances do not fulfil their promise when subjected to randomized controlled trials. It is therefore highly desirable to speed up the process of evaluating new treatment options, particularly in phase II and phase III trials. To help realize such an aim, in 2003, Royston and colleagues proposed a class of multi-arm, two-stage trial designs intended to eliminate poorly performing contenders at a first stage (point in time). Only treatments showing a predefined degree of advantage against a control treatment were allowed through to a second stage. Arms that survived the first-stage comparison on an intermediate outcome measure entered a second stage of patient accrual, culminating in comparisons against control on the definitive outcome measure. The intermediate outcome is typically on the causal pathway to the definitive outcome (i.e. the features that cause an intermediate event also tend to cause a definitive event), an example in cancer being progression-free and overall survival. Although the 2003 paper alluded to multi-arm trials, most of the essential design features concerned only two-arm trials. Here, we extend the two-arm designs to allow an arbitrary number of stages, thereby increasing flexibility by building in several 'looks' at the accumulating data. Such trials can terminate at any of the intermediate stages or the final stage.</p> <p>Methods</p> <p>We describe the trial design and the mathematics required to obtain the timing of the 'looks' and the overall significance level and power of the design. We support our results by extensive simulation studies. As an example, we discuss the design of the STAMPEDE trial in prostate cancer.</p> <p>Results</p> <p>The mathematical results on significance level and power are confirmed by the computer simulations. Our approach compares favourably with methodology based on beta spending functions and on monitoring only a primary outcome measure for lack of benefit of the new treatment.</p> <p>Conclusions</p> <p>The new designs are practical and are supported by theory. They hold considerable promise for speeding up the evaluation of new treatments in phase II and III trials.</p

A menu-driven facility for complex sample size calculation in randomized controlled trials with a survival or a binary outcome: Update

Royston and Babiker (2002) presented a menu-driven Stata program for the calculation of sample size or power for complex clinical trial designs under a survival time or binary outcome. In the present article, the package is updated to Stata 8 under the new name ART. Furthermore, the program has been extended to incorporate noninferiority designs and provides more detailed output. This package is the only realistic sample size tool for survival studies available in Stata. Copyright 2005 by StataCorp LP.sample size, power, randomized controlled trial, multiarm designs, survival analysis

artbin: Extended sample size for randomized trials with binary outcomes

We describe the command artbin, which offers various new facilities for the calculation of sample size for binary outcome variables that are not otherwise available in Stata. While artbin has been available since 2004, it has not been previously described in the Stata Journal. artbin has been recently updated to include new options for different statistical tests, methods and study designs, improved syntax, and better handling of noninferiority trials. In this article, we describe the updated version of artbin and detail the various formulas used within artbin in different settings

Applying the analytic network process to disclose knowledge assets value creation dynamics

The recent literature grounded on the resource and knowledge-based view of the firm, has widely outlined the importance of knowledge assets as well as of the management approaches of their development. However, only few contributions have investigated the mechanisms by which these resources interact to sustain company’s value creation dynamics. In particular, there is a lack of approaches suitable to disentangle those mechanisms and to explain how knowledge assets cluster and interplay in improving organisational performance. A clear understanding of how knowledge assets take part in value creation allows to identify those knowledge assets which, due to their critical role in achieving the company’s performance objectives, need to be managed and appropriately exploited. This paper proposes a model, based on the analytic network process (ANP) methodology, to disclose and assess how knowledge assets mutually interact and take part in company’s value creation dynamics. The application of the ANP allows to reveal and to evaluate the dependencies and inter-dependencies linking knowledge assets to organisational performance objectives and to set priorities among knowledge assets against targeted performance. The application of the model is tested by its application to the identification of the knowledge assets value drivers at the basis of NPD performances improvement within an engineering company located in South of Italy

Five Stages of the Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficacy (STAMPEDE) trial

IDMC  = Independent Data Monitoring Committee; FFS  = failure-free survival; HR  = hazard ratio, where 0 ≤ d ≤ c ≤ b ≤ a ≤ 5.<p><b>Copyright information:</b></p><p>Taken from "Speeding up the Evaluation of New Agents in Cancer"</p><p></p><p>JNCI Journal of the National Cancer Institute 2008;100(17):1204-1214.</p><p>Published online 3 Sep 2008</p><p>PMCID:PMC2528020.</p><p></p

Stopping guidelines on the hazard ratio scale for the Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficacy (STAMPEDE) trial

CI = confidence interval; HR = hazard ratio; Stop = stopping of accrual (rather than termination of follow up).<p><b>Copyright information:</b></p><p>Taken from "Speeding up the Evaluation of New Agents in Cancer"</p><p></p><p>JNCI Journal of the National Cancer Institute 2008;100(17):1204-1214.</p><p>Published online 3 Sep 2008</p><p>PMCID:PMC2528020.</p><p></p