Prospective adverse event risk evaluation in clinical trials

2021 ◽  
Author(s):  
Abhishake Kundu ◽  
Felipe Feijoo ◽  
Diego A. Martinez ◽  
Manuel Hermosilla ◽  
Timothy Matis
Keyword(s):  
Clinical Trials ◽  
Adverse Event ◽  
Risk Evaluation ◽  
Event Risk

FP151SODIUM POLYSTYRENE SULFONATE, PRACTICE PATTERNS AND ASSOCIATED ADVERSE EVENT RISK; A NATIONWIDE ANALYSIS FROM THE SWEDISH RENAL REGISTER

2019 ◽  
Vol 34 (Supplement_1) ◽  
Author(s):  
Paola Laureati ◽  
Marie Evans ◽  
Marco Trevisan ◽  
Lovisa Schalin ◽  
Rino Bellocco ◽  
...  
Keyword(s):  
Adverse Event ◽  
Practice Patterns ◽  
Polystyrene Sulfonate ◽  
Event Risk

scholarly journals A Bayesian meta-analytic approach for safety signal detection in randomized clinical trials

2017 ◽  
Vol 14 (2) ◽  
pp. 192-200 ◽  
Author(s):  
Motoi Odani ◽  
Satoru Fukimbara ◽  
Tosiya Sato
Keyword(s):  
Clinical Trials ◽  
Adverse Event ◽  
Adverse Events ◽  
Hierarchical Structure ◽  
Meta Analysis ◽  
Event Data ◽  
Analysis Model ◽  
Exact Test ◽  
Adverse Event Data ◽  
Fisher’S Exact Test

Background/Aim: Meta-analyses are frequently performed on adverse event data and are primarily used for improving statistical power to detect safety signals. However, in the evaluation of drug safety for New Drug Applications, simple pooling of adverse event data from multiple clinical trials is still commonly used. We sought to propose a new Bayesian hierarchical meta-analytic approach based on consideration of a hierarchical structure of reported individual adverse event data from multiple randomized clinical trials. Methods: To develop our meta-analysis model, we extended an existing three-stage Bayesian hierarchical model by including an additional stage of the clinical trial level in the hierarchical model; this generated a four-stage Bayesian hierarchical model. We applied the proposed Bayesian meta-analysis models to published adverse event data from three premarketing randomized clinical trials of tadalafil and to a simulation study motivated by the case example to evaluate the characteristics of three alternative models. Results: Comparison of the results from the Bayesian meta-analysis model with those from Fisher’s exact test after simple pooling showed that 6 out of 10 adverse events were the same within a top 10 ranking of individual adverse events with regard to association with treatment. However, more individual adverse events were detected in the Bayesian meta-analysis model than in Fisher’s exact test under the body system “Musculoskeletal and connective tissue disorders.” Moreover, comparison of the overall trend of estimates between the Bayesian model and the standard approach (odds ratios after simple pooling methods) revealed that the posterior median odds ratios for the Bayesian model for most adverse events shrank toward values for no association. Based on the simulation results, the Bayesian meta-analysis model could balance the false detection rate and power to a better extent than Fisher’s exact test. For example, when the threshold value of the posterior probability for signal detection was set to 0.8, the false detection rate was 41% and power was 88% in the Bayesian meta-analysis model, whereas the false detection rate was 56% and power was 86% in Fisher’s exact test. Limitations: Adverse events under the same body system were not necessarily positively related when we used “system organ class” and “preferred term” in the Medical Dictionary for Regulatory Activities as a hierarchical structure of adverse events. For the Bayesian meta-analysis models to be effective, the validity of the hierarchical structure of adverse events and the grouping of adverse events are critical. Conclusion: Our proposed meta-analysis models considered trial effects to avoid confounding by trial and borrowed strength from both within and across body systems to obtain reasonable and stable estimates of an effect measure by considering a hierarchical structure of adverse events.

Analyzing inmuno-related adverse event: A real-world DATA study.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18751-e18751
Author(s):  
Blanca Cantos ◽  
Juan Cristobal Sanchez ◽  
Beatriz Nuñez García ◽  
Miriam Mendez ◽  
Aranzazu Gonzalez del Alba ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Adverse Event ◽  
Severe Toxicity ◽  
Toxicity Profile ◽  
Real World Data ◽  
Related Adverse Event ◽  
Appropriate Treatment ◽  
Initial Stage ◽  
Practice Methods ◽  
Grade Iii

e18751 Background: In the last years, Immunotherapy (IT) has emerged as a standard treatment in an increasing number of tumors. This type of treatment has a specific toxicity profile which is clearly different from chemotherapy, known as an Immuno-related Adverse Event (AEir). We know the data from clinical trials, but little about the incidence and impact of this EAir in our clinical practice. Methods: A retrospective observational study was carried out including all patients from our institution (HUPHM in Madrid) who had received IT, either in monotherapy or in combination between January 2014 and December 2019. A total of 279 patients were included and data were collected between January and July 2020, guaranteeing a minimum 6-month follow-up after receiving the first dose of immunotherapy. The toxicities found were classified into four categories: pulmonary, digestive, endocrine and others, and have been graded according to CTCEA v.5 (Common Terminology Criteria for Adverse Event) published in November 2017 and analyzed according to drug and tumor. Results: The most frequent diagnoses in our patients were: 60% lung carcinoma, 15% melanoma, 8% kidney carcinoma, and 6% bladder carcinoma. 76% of the patients received IT as first or second line in a metastatic context, 6% in the initial stage (clinical trials) and the rest in more advanced lines of treatment (3 or more). 67% received anti-PD1 drug, 6% anti-PDL1, 4% anti-CTL4 monotherapy, 10% a combination of several IT drugs, and 14% an IT combination and chemotherapy. 45% of the total presented EAir (16% grade I, 14% grade II, 11% grade III and 4% grade IV). 1/5 of the patients had manifestations in more than one organ. The incidence of the different toxicities in our population was listed in the table below. These patients reported 8% dermatological toxicities, 6% had renal toxicity (most of them grade III or IV), only 2% had arthralgia or myalgia, and 3% asthenia. Combined IT treatment had significantly higher rates of pneumonitis, colitis, and endocrine toxicities. These differences were not observed between the monotherapy treatment and the combination of immunotherapy plus chemotherapy. Conclusions: Immunotherapy has represented an important advance in oncology, achieving long survivals in a growing group of tumors. Immunotherapy has a unique toxicity profile that is very different from chemotherapy and with which we must become familiar. Most of the adverse events are mild and if they are diagnosed early and with the appropriate treatment, maintenance of IT is possible. Severe toxicity (III-IV) means in most cases the suspension of treatment, compromising its efficacy. Therefore, we must learn to recognize these toxicities early and apply the recommended treatments as soon as possible.[Table: see text]

scholarly journals Adverse Event Burden Score—A Versatile Summary Measure for Cancer Clinical Trials

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3251
Author(s):  
Jennifer G. Le-Rademacher ◽  
Shauna Hillman ◽  
Elizabeth Storrick ◽  
Michelle R. Mahoney ◽  
Peter F. Thall ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Adverse Event ◽  
Treatment Cycle ◽  
Controlled Trials ◽  
Cancer Clinical Trials ◽  
Weighted Sum ◽  
Summary Measure ◽  
Double Blind ◽  
Double Blind Placebo ◽  
Over Time

This article introduces the adverse event (AE) burden score. The AE burden by treatment cycle is a weighted sum of all grades and AEs that the patient experienced in a cycle. The overall AE burden score is the total AE burden the patient experienced across all treatment cycles. AE data from two completed Alliance multi-center randomized double-blind placebo-controlled trials, with different AE profiles (NCCTG 97-24-51: 176 patients, and A091105: 83 patients), were utilized for illustration. Results of the AE burden score analyses corroborated the trials’ primary results. In 97-24-51, the overall AE burden for patients on the treatment arm was 2.2 points higher than those on the placebo arm, with a higher AE burden for patients who went off treatment early due to AE. Similarly, in A091105, the overall AE burden was 1.6 points higher on the treatment arm. On the placebo arms, the AE burden in 97-24-51 remained constant over time; and increased in later cycles in A091105, likely attributable to the increase in disease morbidity. The AE burden score enables statistical comparisons analogous to other quantitative endpoints in clinical trials, and can readily accommodate different trial settings, diseases, and treatments, with diverse AE profiles.

Dealing With a Deluge of Data: An Assessment of Adverse Event Data on North Central Cancer Treatment Group Trials

2005 ◽  
Vol 23 (36) ◽  
pp. 9275-9281 ◽  
Author(s):  
Michelle R. Mahoney ◽  
Daniel J. Sargent ◽  
Michael J. O'Connell ◽  
Richard M. Goldberg ◽  
Paul Schaefer ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Adverse Event ◽  
Patient Safety ◽  
Treatment Group ◽  
Cancer Treatment ◽  
Phase Iii ◽  
Small Subset ◽  
North Central ◽  
Normal Limits ◽  
Life Threatening

Purpose Adverse events (AEs) are monitored in clinical trials for patient safety, to satisfy reporting requirements, and develop safety profiles. Recently, much attention has been placed on the reporting of serious AEs (SAEs) that are either life threatening or lethal in clinical trials. However, SAEs comprise a small subset of all AE data collected for trials; the majority of AE data collected are routine AEs (RAEs) regarding non–life-threatening events. We assessed the utility of the RAE data collected, relative to the volume. Patients and Methods We surveyed the RAE data from 26 North Central Cancer Treatment Group coordinated trials. Results A total of 8,318 (11%) of 75,598 of RAEs required queries. Of these, 86% were protocol-required RAEs, 83% of RAEs required per protocol were within normal limits (eg, platelets) or not present, and 61% of extra AEs were mild. One fifth of RAEs were considered unlikely to be related or unrelated to treatment. Overall, 3% of events were severe, life threatening, or caused death. Only 1% of RAE data reported required expedited reporting (eg, via Adverse Event Expedited Reporting System). Results indicate that 72% of RAEs would be eliminated if only the maximum severity per patient and type were required. These results were validated in a large phase III trial. Conclusion The majority of RAEs identified, transcribed, and entered are not clinically important. Our data suggest that reducing the number of AEs monitored will affect substantially neither overall patient safety nor compromise evaluation of regimens undergoing testing. We present several considerations for such a reduction in data collection, as well as a policy that we have used to address the deluge of RAE data.

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