Dataset Information

Optimizing collection of adverse event data in cancer clinical trials supporting supplemental indications.

ABSTRACT: PURPOSE: Although much is known about the safety of an anticancer agent at the time of initial marketing approval, sponsors customarily collect comprehensive safety data for studies that support supplemental indications. This adds significant cost and complexity to the study but may not provide useful new information. The main purpose of this analysis was to assess the amount of safety and concomitant medication data collected to determine a more optimal approach in the collection of these data when used in support of supplemental applications. METHODS: Following a prospectively developed statistical analysis plan, we reanalyzed safety data from eight previously completed prospective randomized trials. RESULTS: A total of 107,884 adverse events and 136,608 concomitant medication records were reviewed for the analysis. Of these, four grade 1 to 2 and nine grade 3 and higher events were identified as drug effects that were not included in the previously established safety profiles and could potentially have been missed using subsampling. These events were frequently detected in subsamples of 400 patients or larger. Furthermore, none of the concomitant medication records contributed to labeling changes for the supplemental indications. CONCLUSION: Our study found that applying the optimized methodologic approach, described herein, has a high probability of detecting new drug safety signals. Focusing data collection on signals that cause physicians to modify or discontinue treatment ensures that safety issues of the highest concern for patients and regulators are captured and has significant potential to relieve strain on the clinical trials system.

SUBMITTER: Kaiser LD

PROVIDER: S-EPMC3018355 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Optimizing collection of adverse event data in cancer clinical trials supporting supplemental indications.

Kaiser Lee D LD Melemed Allen S AS Preston Alaknanda J AJ Chaudri Ross Hilary A HA Niedzwiecki Donna D Fyfe Gwendolyn A GA Gough Jacqueline M JM Bushnell William D WD Stephens Cynthia L CL Mace M Kelsey MK Abrams Jeffrey S JS Schilsky Richard L RL

Journal of clinical oncology : official journal of the American Society of Clinical Oncology 20101004 34

<h4>Purpose</h4>Although much is known about the safety of an anticancer agent at the time of initial marketing approval, sponsors customarily collect comprehensive safety data for studies that support supplemental indications. This adds significant cost and complexity to the study but may not provide useful new information. The main purpose of this analysis was to assess the amount of safety and concomitant medication data collected to determine a more optimal approach in the collection of thes ...[more]

PMID: 20921453

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Project description:INTRODUCTION:Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The current clinical Quality Assurance (QA) practices used to detect AE under-reporting rely heavily on investigator site and study audits. Yet several sponsors and institutions have had repeated findings related to safety reporting, and this has led to delays in regulatory submissions. Recent developments in data management and IT systems allow data scientists to apply techniques such as machine learning to detect AE under-reporting in an automated fashion. OBJECTIVE:In this project, we developed a predictive model that enables Roche/Genentech Quality Program Leads oversight of AE reporting at the program, study, site, and patient level. This project was part of a broader effort at Roche/Genentech Product Development Quality to apply advanced analytics to augment and complement traditional clinical QA approaches. METHOD:We used a curated data set from 104 completed Roche/Genentech sponsored clinical studies to train a machine learning model to predict the expected number of AEs. Our final model used 54 features built on patient (e.g., demographics, vitals) and study attributes (e.g., molecule class, disease area). RESULTS:In order to evaluate model performance, we tested how well it would detect simulated test cases based on data not used for model training. For relevant simulation scenarios of 25%, 50%, and 75% under-reporting on the site level, our model scored an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.62, 0.79, and 0.92, respectively. CONCLUSION:The model has been deployed to evaluate safety reporting performance in a set of ongoing studies in the form of a QA/dashboard cockpit available to Roche Quality Program Leads. Applicability and production performance will be assessed over the next 12-24 months in which we will develop a validation strategy to fully integrate our model into Roche QA processes.

Project description:BackgroundCurrent adverse event reporting practices do not document longitudinal characteristics of adverse effects, and alternative methods are not easily interpretable and have not been employed by clinical trials. Introducing time parameters in the evaluation of safety that are comprehensive yet easily interpretable could allow for a better understanding of treatment quality. In this study, we developed and applied a novel adverse event reporting method based on longitudinal adverse event changes to aid describing, summarizing, and presenting adverse event profile. We termed it the "Adverse Event Load, Onset, and Maximum Grade" method.MethodsWe developed two adverse event summary metrics to complement the traditional maximum grade report. Onset time indicates the time period in which the maximum grade for a specific adverse event occurred and was defined as "early" (i.e. maximum grade happened for the first time before 6 weeks) or "late" (i.e. after the 6th week). Adverse event load indicates the overall severity of a specific adverse event over the entire treatment. Higher adverse event load indicates a worse overall experience. These metrics can be calculated for adverse events with different maximum grades, in treatments with planned changes (e.g. dosage changes), using data sets with different number of adverse event data points between treatments (e.g. treatments with longer cycle lengths may have less adverse event data points) and on data sets with different adverse event data availability (e.g. cycle basis and patient-outcome reports). We tested the utility of this method using individual patient data from two major backbone therapies ("Irinotecan" and "Oxaliplatin") from the N9741 trial available in the Fondation ARCAD database (fondationarcad.org). We investigated profiles of diarrhea, neutropenia/leukopenia, and nausea/vomiting.ResultsOur method provided additional information compared to traditional adverse event reports. For example, for nausea/vomiting, while patients in Irinotecan had a higher risk of experiencing maximum grade 3-4 (15.6% vs 7.6%, respectively; p < 0.001), patients in both groups experienced similar severity over time (adverse effect load = 0.102 and 0.096, respectively; p = 0.26), suggesting that patients in Oxaliplatin experienced a lower-grade but more persistent nausea/vomiting. For neutropenia/leukopenia, more patients in Irinotecan experienced their maximum grade for the first time early in the treatment compared to patients in Oxaliplatin (67.9% vs 41.7%; p < 0.001), regardless of maximum grade. Longitudinal information can help compare treatments or guide clinicians on choosing appropriate interventions for low-grade but persistent adverse event or early adverse event onset.ConclusionWe developed an adverse event reporting method that provides clinically relevant information about treatment toxicity by incorporating two longitudinal adverse event metrics to the traditional maximum grade approach. Future research should establish clinical benchmarks for metrics included in this adverse event reporting method.

Dataset Information

Optimizing collection of adverse event data in cancer clinical trials supporting supplemental indications.

Publications

Optimizing collection of adverse event data in cancer clinical trials supporting supplemental indications.

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