Risk-Based Monitoring of Survival Data

Risk-Based Monitoring of Survival Data Zhang Zhizhuo Summary In clinical trials, on-site monitoring is traditionally used to validate trial data quality, reveal abnormal data and identify risk factors. But little evidence has found is has positive effect on bias reduction and precision improvement. Central monitoring is an alternative of on-site monitoring, which can identify sites with higher risks of bias, errors and deviations remotely and effectively. Time to event is commonly employed as endpoint especially in tumor therapy trial. Any factors that may reduce the accuracy and precision of survival data would lead to a biased trial result. So survival data can be a potential target for central risk-based monitoring. By revealing unusual pattern or inaccuracy of survival data in site level, risk sites can be identified. This study aims to establish an algorithm and a risk model for monitoring survival data and identifying risk sites, and to generate a reusable SAS program for future application of the risk model. Metrics of abnormal event count and proportion in each site will be served as monitoring target. Test for difference between proportions comparing each site with other sites will be applied on proportion data. For rare event, Poisson loglinear regression will be used for calculate relative risk of abnormal event occurrence between each site and other sites. Risk flag on particular site will be reported when a significant result occur. Table of Content Summary 1. Background 3. Objectives 4. Study Design 5. Methodology 5.1 Restructure datasets according to CDISC 5.2 Algorithm 5.3 Model validation and generalization 5.4 SAS Programming 5.5 Dataset 6. Expected outcomes References Appendix A Appendix B 1.Background In clinical trials, quality assurance including site performance and data validity is the essential foundation of maximizing precision of trial results. Varies types of error may occur in all aspect in clinical trials: design error, procedural error, recording error, fraud and analytical error [1]. Any factors involved with these errors are considered as risks. Different monitoring methods can be allocated to detect and reveal specific kinds of risks in clinical trials: trial oversight committee, on-site monitoring and central monitoring. Traditionally, data quality of clinical trials is validated by on-site monitoring. On-site visiting is an expensive monitoring approach which take approximate 30% of total trial costs in pharmaceutical industry [2]. However, 84% of the pharmaceutical industry and 89% of Contract Research Organizations (CRO) still rely heavily on practices of on-site visiting [3]. Despite this current situation, little evidence has found that on-site monitoring has significant positive effect on bias reduction and precision improvement in clinical trials. Recently, Food and Drug Administration (FDA) [4] published â€Å"Guidance for Industry: Oversight of Clinical Investigations—A Risk-Based Approach to Monitoring.† In this guidance, FDA encourages greater use of centralized monitoring practices. Using these approaches, sites with higher risks of bias, errors and deviations can be identified remotely. By only visiting sites of concerns instead of 100% source data verification, costs and time can be reduced effectively. So far, many statistical methods have been developed to be employed in centralized monitoring, which are proved to be efficient and reliable [5-9]. These statistical methods form the cornerstone of risk-based monitoring. In clinical trials, time to event is commonly employed as endpoint to evaluate the efficacy of the treatment. Especially in cancer therapy trials, time to progression is served as tumor-assessment endpoint (when majority of deaths are unrelated to the disease) [10] or even primary endpoint. Any factors that may reduce the accuracy and precision of this kind of data – survival data – would lead to a biased trial result, and the interpretation of the result might become inaccurate or of no value. While conducting a multicenter trial, it is of vital importance to check the validity of data updated at intervals, to identify the sites of concern and correct actions of risk. Factors involved with survival outcome including missing data, illogical data and abnormal data, can be a potential targets for risk-based monitoring survival data. Presently, Clinical Data Interchange Standards Consortium (CDISC) [11] provides â€Å"standards to support the acquisition, exchange, submission and archive of clinical research data and metadata.† In advantage of CDISC normative data structure, especially Study Dara Tabulation Model (SDTM) and Analysis Data Model (ADaM), a data template can be established while the multicenter trial is ongoing. All data generated in the trial can be updated and restructured on the basis of the data template. This kind of formatted data structure provides great convenience for routinely data monitoring and validation. Meanwhile, once an algorithm for risk-based monitoring is generated, statistical model is build and the corresponding SAS program is coded, they can be applied to several trials and datasets which sharing the same monitoring target. 3.Objectives To establish an algorithm and a risk model for monitoring survival data, which is required to be capable of identifying trial centers with risk factors by revealing abnormal data; To generalize the algorithm and the risk model for application on clinical trials; To generate a reusable SAS program for application of the risk model. 4.Study Design Choose adequate metrics according to conventional monitoring targets, establish the algorithm and risk model, set appropriate criteria for risk flag. Apply the risk model on a real clinical trial dataset, identify risk sites. Compare the sites identified by model and sites with high risk known in advance, calculate sensitivity and specificity of the risk model. Generalize the risk model according to validation result, generate reusable SAS program for the risk model. 5.Methodology 5.1 Restructure datasets according to CDISC By implementation of Study Data Tabulation Model (SDTM), raw data will be sorted in formatted tabulations with observations of individual subjects. Attributes (name, label, type, length, description, etc.) of every metadata will be reset to meet SDTM conventions. And variables will be classified into corresponding domains. By implementation of Analysis Data Model (ADaM), data will first be structured into the subject-level analysis dataset (ADSL) formats. Subject-level variables will be specified to be ready for analysis. Specific variables will be calculated and formatted into Basic Data Structure (BDS) for site-level data analysis. CDISC template for risk model establishment is listed in Appendix A. All the original data will be structured in standardized formats according to this template. And this CDISC template will be reusable for future application. 5.2 Algorithm The statistical methods for different metrics to report risk flag are summarized in Table 1. Metrics: Monitoring targets for the risk model is chosen according to conventional monitoring practice. They will be missing randomization date, missing screening date, illogical date, censoring, death and tumor response. These kinds of data is involved with data integrity and data accuracy, and may has potential effect on survival data. Abnormal events in each target of every site will be counted and corresponding proportion will be calculated. Test for difference between proportions: Proportion metrics of each site will be compared with other sites by calculating t statistics and corresponding p-value. Sites with p-value (two-tailed) Poisson loglinear regression: For rare events (proportion metrics in sites are generally very low), Poisson loglinear regression will be implied to obtain point estimate and confidence interval (CI) of risk ratio (RR) in each site. CI of RR does not contain 1 will be considered as risk factor, and site will be marked by risk flag. 5.3 Model validation and generalization Apply the monitoring model on a real clinical trial dataset of which the risks have already known. Risk sites are expected to be marked with risk flag, and the opposite for sites without risks. Accuracy of the model will be tested by calculating sensitivity and specificity. In order to generalize the risk model for application on clinical trial data, proper metrics and corresponding statistical methods will be chosen to acquire higher accuracy and balance sensitivity and specificity. For example, if missing data proportions in sites are generally high, test for comparison between proportions will be used to identify risk site; however, if missing data proportion in each site is generally low, then missing data count will be considered as the appropriate metric and Poisson loglinear regression will be allocated. 5.4 SAS Programming Statistical software applied to this project will be SAS, version 9.3. All the procedures will be processed by SAS program. Macros will be utilized to make the program reusable. Flow charts of SAS programming logic are listed in Appendix B. 5.5 Dataset Dataset is from a real clinical trial data. Risk information of dataset is already known. Dataset will be used for external validation of the model. 6.Expected outcomes Establish a risk model for central statistical monitoring of survival data in clinical trials. Generate a SAS program reusable and applicable in pharmaceutical industries and CROs. Write an article for graduation. References Baigent C, Harrell FE, Buyse M, Emberson JR, Altman DG. Ensuring trial validity by data quality assurance and diversification of monitoring methods. Clinical Trials 2008 February 01;5(1):49-55. Eisenstein EL, Collins R, Cracknell BS, Podesta O, Reid ED, Sandercock P, et al. Sensible approaches for reducing clinical trial costs. Clinical Trials 2008 February 01;5(1):75-84. Morrison BW, Cochran CJ, White JG, Harley J, Kleppinger CF, Liu A, et al. Monitoring the quality of conduct of clinical trials: a survey of current practices. Clinical Trials 2011 June 01;8(3):342-349. FDA. Guidance for Industry: Oversight of Clinical Investigations—A Risk-Based Approach to Monitoring. 2013 August. Venet D, Doffagne E, Burzykowski T, Beckers F, Tellier Y, Genevois-Marlin E, et al. A statistical approach to central monitoring of data quality in clinical trials. Clinical Trials 2012 December 01;9(6):705-713. Pogue JM, Devereaux P, Thorlund K, Yusuf S. Central statistical monitoring: Detecting fraud in clinical trials. Clinical Trials 2013 April 01;10(2):225-235. Buyse M, George SL, Evans S, Geller NL, Ranstam J, Scherrer B, et al. The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials. Stat Med 1999 Dec 30;18(24):3435-3451. Bakobaki JM, Rauchenberger M, Joffe N, McCormack S, Stenning S, Meredith S. The potential for central monitoring techniques to replace on-site monitoring: findings from an international multi-centre clinical trial. Clinical Trials 2012 April 01;9(2):257-264. Kirkwood AA, Cox T, Hackshaw A. Application of methods for central statistical monitoring in clinical trials. Clinical Trials 2013 October 01;10(5):783-806. FDA. Guidance for Industry: Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics. 2007 May. Available at: http://www.cdisc.org/CDISC-Vision-and-Mission. Appendix A Appendix B 1

Organizational Project Management Maturity Model Essay

1. Introduction Successful implementation of a new organizational strategy can turn a good organization into a great one. Conversely, strategies that fail or generate poor results can quickly damage the organization’s reputation and brand, internally and externally. Effective strategy execution is the responsibility of all levels of management, who must be involved actively and consistently to orchestrate required organizational changes and to manage the portfolio of investments that underpin these change initiatives. The Organizational Project Management Maturity Model is a framework that provides an organization wide view of portfolio management, program management, and project management to support achieving best Practices within each of these domains. This holistic perspective is a powerful tool enabling successful execution of organizational strategies, portfolios, programs, and projects, especially when these transcend functional and hierarchical boundaries. Moreover, OPM3 global best P ractices, applied to the execution of strategy, can drive superior and sustainable results. Effective strategy execution is the responsibility of the organization’s strategic planning and governance structures, which must be involved accurately and consistently to orchestrate required organizational changes. They manage the portfolio of investments that underpin these change initiatives. 2. Organizational project management Organizational project management is the systematic management of projects, programs and portfolios in allignment with the achievements of strategic goals. The concept of organizational project management is based on the idea that there is correlation between organization’s capabilities in project management, program management and portfolio management and its effectiveness implementing strategy. Organizational Project Management Project Portfolio Program Strategic Goals * Project – A temporary endeavor undertaken to create a unique product, service or result. * Program – A group of realated projects managed in a coordinated way to obtain benefits and control not available from managing them individually. * Portfolio – Acolloection of projects or programs and other work that are grouped together to facilitate effective management to meet strategic business objectives. 3. OPM3 Model OPM3 is an acronym for the Organizational Project Management Maturity Model, a standard wich is developed under the stewardship of the project management institute. The purpose of this standard is to provide a way for organizations to understand organizational project management and to measure their maturity against a comprehensive and broad based set of organizational project management best practices. OPM3 also helps organizations to increase their organizational project management maturity to plan for improvement. 4.1. Primary physical parts of the standard There are three parts; i. Narrative text – presents the OPM3 foundational concepts, with various appendices and glossary ii. Self assessment – provide a tool in support of the assessment step out lined in OPM3 iii. Directories – contain data on nearly 600 organizational project management best practices and their constituent capabilities 4.2. OPM3 Stages There are four sequential stages of process improvement; STANDARDIZE| MEASURE| CONTROL| CONTINUOUSLY IMPROVEMENT| 4. How does the OPM3 work? OPM3 offers the key to organizational project management maturity with three interlocking elements; i. The KNOWLEDGE element lets organizations uncover hundreds of Best Practices and shows them how to use the information available in OPM3. ii. The self ASSESSMENT element is an interactive database tool that lets organizations evaluate their current situation and identify their areas in need of improvement should an organization decided to embark on the path to higher maturity. iii. The IMPROVEMENT element will help map out the steps needed to achieve their goals. * KNOWLEDGE element which drives Assessment * ASSESSMENT element which in turn drives Improvement * IMPROVEMENT element. 5. Benefits of OPM3 to the organization I. It bridges the gap between strategy and individual project. II. It provides a comprehensive body of knowledge regarding what constitutes best practices in organizational project management. III. By using OPM3, an organization can determine exactly which organizational project management best practices and capabilities it does and does not have. IV. If the organization decides to pursue improvements, OPM3 provides guidance on prioritizing and planning. 6. What kind of commitment is required to launch OPM3 in an organization? The process of applying OPM3 in an organization is difficult to quantify. It depends on factors such as the size, complexity and initial maturity of the organization, the thoroughness of the assessment, the nature of the organization’s strategic objectives, and the level of resources available also impact any estimate. However, the assessment portion of such an initiative is most likely to take from several weeks to several months. Should an organization decide to embark upon improvements, the planning and implementation steps are likely to take longer, depending on how many best practices and related capabilities an organization decides to work on at one time. 7. Importance of OPM3 to the project management profession The Project Manager Competency Development Framework is the standard to guide the professional development of project of project managers and those aspiring to be project managers. OPM3 is the first iteration of a standard for organizations. It has the potential to create a new environment for those who are working in the project management profession, by illuminating the important link between projects and organizational strategy and the importance of organizational support to project management practices. The information in OPM3 is based on very broad based input from project management practitioners and consultants. 8. Summary The current global economic climate has ushered in an era of uncertainty that throws the importance of Organizational Project Management (OPM) maturity into sharp relief. It is critical for organizations to renew their ability to create cost efficiencies, economies of scale and agility to adapt to the changing business environment through projects. Companies need the organizational capability to choose the right projects, manage costs, and innovate. They need the organizational capability to delivery projects successfully, consistently, and predictably. The Project Management Institute’s OPM3 Standard was developed with input from thousands of project practitioners and represents best practices in Project, Program, and Portfolio Management. OPM3 incorporates the PMI’s PMBOK Guide, the most widely adopted standard for managing individual projects, and expands this into the domains of Program Management and Portfolio Management. OPM3 emphasizes choosing the right projects to advance organizational strategies and implementing the processes, structures, and behaviors necessary to deliver projects successfully, consistently , and predictably. Standardization of project work methods lays the foundation for achieving higher levels of maturity and excellence to create the organizational agility and resilience you need in today’s marketplace. Leading organizations of all types and sizes across multiple industries are adopting OPM3 to transform their ability to close the gap between strategic intent and tactical outcomes through successful project selection and delivery. The preferred method for implementing OPM3 begins with an OPM3 Assessment by a PMI certified OPM3 Professional. 9. References * Project Management Institute, Inc. (2003). Organizational Project Management Maturity Model (OPM3). Organizational Project Management Maturity Model (OPM3). 1 (1), 1-195. * Project Management Institute, Inc. (2004). An executive’s guide to OPM3. An executive’s guide to OPM3. 1 (1), 1-5.

Children s Manifest Anxiety Scale - 1757 Words

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Microeconomics Basic Concepts - 1055 Words

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