Risk-Based Monitoring of Survival Data

take chances-Based monitor of Survival infoZhang ZhizhuoSummaryIn clinical ladders, on-the-scene(prenominal) observe is traditionally used to validate tally selective information fictitious character, reveal kinky info and come in peril factors. But little evidence has arrange is has positive effect on bias decrease and preciseness improvement. important monitor is an alternative of on-the-scene(prenominal) supervise, which discount identify identifys with amply risks of bias, geological faults and deviations remotely and effectively.Time to event is commonly employed as final number curiously in tumor therapy effort. whatever factors that may reduce the accuracy and precision of selection data would lead to a biased trial result. So excerpt data abide be a potential organize for central risk-based supervise. By revealing unusual pattern or inaccuracy of natural selection data in site level, risk sites mint be identified.This psychoanalyse aims to s trand an algorithmic rule and a risk role model for supervise survival data and identifying risk sites, and to generate a reusable SAS platform for prison term to come application of the risk model.Metrics of atypical event count and likeness in each site go away be served as supervise target. testify for difference between symmetrys comparing each site with otherwise sites ordain be applied on proportion data. For rargon event, Poisson loglinear arrested development will be used for calculate relative risk of insane event occurrence between each site and other sites. Risk flag on particular site will be describe when a significant result occur.Table of ContentSummary1. compass3. Objectives4. Study Design5. Methodology5.1 Re social structure datasets according to CDISC5.2 algorithm5.3 cast validation and generalization5.4 SAS Programming5.5 infoset6. Expected outcomesReferences appendix AAppendix B1.BackgroundIn clinical trials, quality assurance including site surgical process 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, bosh and analytical error 1. Any factors pertain 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 come near which take approximate 30% of total trial be 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 prec ision improvement in clinical trials.Recently, Food and Drug disposition (FDA) 4 published Guidance for pains Oversight of clinical InvestigationsA Risk-Based attempt 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 sort of 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 economical 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 end point. 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 engageing a multicenter trial, it is of vital importance to check the validity of data updated at musical intervals, to identify the sites of concern and correct actions of risk. Factors involved with survival outcome including missing data, dislocated data and abnormal data, can be a potential targets for risk-based monitoring survival data.Presently, clinical Data Interchange Standards Consortium (CDISC) 11 provides standards to support the acquisition, exchange, debut and archive of clinical research data and metadata. In advantage of CDISC normative data structure, especially Study Dara Tabulation Model (SDTM) and outline Data Model (ADaM), a data template can be open while the multicenter trial is ongoing. every data generated in the trial can be updated a nd re structure 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.ObjectivesTo establish an algorithm and a risk model for monitoring survival data, which is infallible to be capable of identifying trial centers with risk factors by revealing abnormal dataTo derive the algorithm and the risk model for application on clinical trialsTo generate a reusable SAS program for application of the risk model.4.Study DesignChoose adequate metrics according to conventional monitoring targets, establish the algorithm and risk model, set appropriate criteria for risk flag. halt the risk model on a very clinical trial dataset, identify risk sites. Compare the sites identi fied by model and sites with high risk known in advance, calculate sensitivity and specificity of the risk model. reason the risk model according to validation result, generate reusable SAS program for the risk model.5.Methodology5.1 Restructure datasets according to CDISCBy implementation of Study Data Tabulation Model (SDTM), raw data will be select in formatted tabulations with observations of individual subjects. Attributes (name, label, type, length, description, etc.) of every metadata will be reset to carry through 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. peculiar(prenominal) 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 App endix A. All the original data will be structured in govern formats according to this template. And this CDISC template will be reusable for future application.5.2 AlgorithmThe statistical methods for different metrics to report risk flag are summarized in Table 1.Metrics Monitoring targets for the risk model is elect 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 turnaround For rare events (proportion metrics in sites are generally v ery low), Poisson loglinear infantile fixation will be implied to obtain point estimate and confidence interval (CI) of risk ratio (RR) in each site. CI of RR does not get hold of 1 will be considered as risk factor, and site will be marked by risk flag.5.3 Model validation and generalizationApply 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, tight-laced metrics and corresponding statistical methods will be chosen to strike 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 l ow, then missing data count will be considered as the appropriate metric and Poisson loglinear degeneration will be allocated.5.4 SAS ProgrammingStatistical software applied to this wander will be SAS, version 9.3. All the procedures will be touch by SAS program. Macros will be utilized to make the program reusable. operate charts of SAS programming logic are listed in Appendix B.5.5 DatasetDataset 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 outcomesEstablish 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.ReferencesBaigent 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 015(1)49-55.Eisenstein EL, Colli ns R, Cracknell BS, Podesta O, Reid ED, Sandercock P, et al. Sensible approaches for minify clinical trial costs. Clinical Trials 2008 February 015(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 018(3)342-349.FDA. Guidance for Industry Oversight of Clinical InvestigationsA 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 019(6)705-713.Pogue JM, Devereaux P, Thorlund K, Yusuf S. Central statistical monitoring Detecting fraud in clinical trials. Clinical Trials 2013 April 0110(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 3018(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 019(2)257-264.Kirkwood AA, cox T, Hackshaw A. Application of methods for central statistical monitoring in clinical trials. Clinical Trials 2013 October 0110(5)783-806.FDA. Guidance for Industry Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics. 2007 May. uncommitted at http//www.cdisc.org/CDISC-Vision-and-Mission.Appendix AAppendix B1