Analysis of safety data in clinical trials using a recurrent event approach

Early Adverse Event Derived Biomarkers in Predicting Clinical Outcomes in Patients with Advanced Non-Small Cell Lung Cancer Treated with Immunotherapy

RATIONALE

Adverse events (AEs) have been shown to have clinical associations, in addition to patient safety assessments of drugs of interest. However, due to their complex content and associated data structure, AE evaluation has been restricted to descriptive statistics and small AE subset for efficacy analysis, limiting the opportunity for global discovery. This study takes a unique approach to utilize AE-associated parameters to derive a set of innovative AE metrics. Comprehensive analysis of the AE-derived biomarkers enhances the chance of discovering new predictive AE biomarkers of clinical outcomes.

METHODS

We utilized a set of AE-associated parameters (grade, treatment relatedness, occurrence, frequency, and duration) to derive 24 AE biomarkers. We further innovatively defined early AE biomarkers by landmark analysis at an early time point to assess the predictive value. Statistical methods included the Cox proportional hazards model for progression-free survival (PFS) and overall survival (OS), two-sample t-test for mean difference of AE frequency and duration between disease control (DC: complete response (CR) + partial response (PR) + stable disease (SD)) versus progressive disease (PD), and Pearson correlation analysis for relationship of AE frequency and duration versus treatment duration. Two study cohorts (Cohort A: vorinostat + pembrolizumab, and B: Taminadenant) from two immunotherapy trials in late-stage non-small cell lung cancer were used to test the potential predictiveness of AE-derived biomarkers. Data from over 800 AEs were collected per standard operating procedure in a clinical trial using the Common Terminology Criteria for Adverse Events v5 (CTCAE). Clinical outcomes for statistical analysis included PFS, OS, and DC.

RESULTS

An early AE was defined as event occurrence at or prior to day 30 from initial treatment date. The early AEs were then used to calculate the 24 early AE biomarkers to assess overall AE, each toxicity category, and each individual AE. These early AE-derived biomarkers were evaluated for global discovery of clinical association. Both cohorts showed that early AE biomarkers were associated with clinical outcomes. Patients previously experienced with low-grade AEs (including treatment related AEs (TrAE)) had improved PFS, OS, and were associated with DC. The significant early AEs included low-grade TrAE in overall AE, endocrine disorders, hypothyroidism (pembrolizumab's immune-related adverse event (irAE)), and platelet count decreased (vorinostat related TrAE) for Cohort A and low-grade AE in overall AE, gastrointestinal disorders, and nausea for Cohort B. In contrast, patients with early development of high-grade AEs tended to have poorer PFS, OS, and correlated with PD. The associated early AEs included high-grade TrAE in overall AE, gastrointestinal disorders with two members, diarrhea and vomiting, for Cohort A and high-grade AE in overall AE, three toxicity categories, and five related individual AEs for Cohort B. One low-grade TrAE, alanine aminotransferase increased (vorinostat + pembrolizumab related), was an irAE and correlated with worse OS in Cohort A.

CONCLUSIONS

The study demonstrated the potential clinical utility of early AE-derived biomarkers in predicting positive and negative clinical outcomes. It could be TrAEs or combination of TrAEs and nonTrAEs from overall AEs, toxicity category AEs, to individual AEs with low-grade event leaning to encouraging effect and high-grade event to undesirable impact. Moreover, the methodology of the AE-derived biomarkers could change current AE analysis practice from a descriptive summary into modern informative statistics. It modernizes AE data analysis by helping clinicians discover novel AE biomarkers to predict clinical outcomes and facilitate the generation of vast clinically meaningful research hypotheses in a new AE content to fulfill the demands of precision medicine.

Statistical methods for the analysis of adverse event data in randomised controlled trials: a scoping review and taxonomy

BACKGROUND

Statistical methods for the analysis of harm outcomes in randomised controlled trials (RCTs) are rarely used, and there is a reliance on simple approaches to display information such as in frequency tables. We aimed to identify whether any statistical methods had been specifically developed to analyse prespecified secondary harm outcomes and non-specific emerging adverse events (AEs).

METHODS

A scoping review was undertaken to identify articles that proposed original methods or the original application of existing methods for the analysis of AEs that aimed to detect potential adverse drug reactions (ADRs) in phase II-IV parallel controlled group trials. Methods where harm outcomes were the (co)-primary outcome were excluded. Information was extracted on methodological characteristics such as: whether the method required the event to be prespecified or could be used to screen emerging events; and whether it was applied to individual events or the overall AE profile. Each statistical method was appraised and a taxonomy was developed for classification.

RESULTS

Forty-four eligible articles proposing 73 individual methods were included. A taxonomy was developed and articles were categorised as: visual summary methods (8 articles proposing 20 methods); hypothesis testing methods (11 articles proposing 16 methods); estimation methods (15 articles proposing 24 methods); or methods that provide decision-making probabilities (10 articles proposing 13 methods). Methods were further classified according to whether they required a prespecified event (9 articles proposing 12 methods), or could be applied to emerging events (35 articles proposing 61 methods); and if they were (group) sequential methods (10 articles proposing 12 methods) or methods to perform final/one analyses (34 articles proposing 61 methods).

CONCLUSIONS

This review highlighted that a broad range of methods exist for AE analysis. Immediate implementation of some of these could lead to improved inference for AE data in RCTs. For example, a well-designed graphic can be an effective means to communicate complex AE data and methods appropriate for counts, time-to-event data and that avoid dichotomising continuous outcomes can improve efficiencies in analysis. Previous research has shown that adoption of such methods in the scientific press is limited and that strategies to support change are needed.

TRIAL REGISTRATION

PROSPERO registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97442.

Modernizing adverse events analysis in oncology clinical trials using alternative approaches: rationale and design of the MOTIVATE trial

In oncology clinical research, the analysis and reporting of adverse events is of major interest. A consistent depiction of the safety profile of a new treatment is as crucial in establishing how to use it as its antitumor activity. The advent of new therapeutics has led to major changes in the management of patients and targeted therapies or immune checkpoint inhibitors are administered continuously for months or even years. However, the classical methods of adverse events analysis are no longer adequate to properly assess their safety profile. Indeed, the worst grade method and time-to-event analysis cannot capture the duration or the evolution of adverse events induced by extended treatment durations. Many authors have highlighted this issue and argue that the analysis of safety data from clinical trials should be modernized by considering the dimension of time and the recurrent nature of adverse events. This paper aims to illustrate the limitations of current methods and discusses the value of alternative approaches such as the prevalence function, Q-TWiST, the ToxT and the recurrent event approaches. The rationale and design of the MOTIVATE trial, which aims to model the evolution of toxicities over time using the prevalence function in patients treated by immunotherapy, is also presented ( ClinicalTrials.gov Identifier: NCT03447483; Date of registration: 27 February 2018).

Adverse events during a disorder-specific psychotherapy compared to a nonspecific psychotherapy in patients with chronic depression

OBJECTIVES

A recent trial comparing Cognitive Behavioral Analysis System of Psychotherapy (CBASP) and supportive psychotherapy in chronic depression found CBASP to be more effective in treating depressive symptoms. We aimed to evaluate adverse events that occurred during this trial.

MATERIALS AND METHOD

A randomized trial of chronically depressed outpatients was performed. The treatment included 32 sessions of CBASP or supportive psychotherapy. Therapists asked patients about adverse events and their intensity in each session using a standardized checklist. We analyzed the mean number of (severe) adverse events per patient up to Session 32 with gamma frailty recurrent event models.

RESULTS

Two hundred and sixty patients were included in the analyses (66% female, mean age 45 years). Patients in the supportive psychotherapy group reported less severe adverse events in general, and less severe adverse events related to personal life and to occupational life than patients in the CBASP group. Less adverse events related to suicidal thoughts were reported in the CBASP compared with the supportive psychotherapy group.

CONCLUSIONS

Differences in the adverse events profile may be explained by the treatment elements. Adverse events related to personal and occupational life for example might be considered a necessary and expected yet temporary adverse treatment outcome of an effective CBASP treatment.

Biometrical issues in the analysis of adverse events within the benefit assessment of drugs

The analysis of adverse events plays an important role in the benefit assessment of drugs. Consequently, results on adverse events are an integral part of reimbursement dossiers submitted by pharmaceutical companies to health policy decision-makers. Methods applied in the analysis of adverse events commonly include simple standard methods for contingency tables. However, the results produced may be misleading if observations are censored at the time of discontinuation due to treatment switching or noncompliance, resulting in unequal follow-up periods. In this paper, we present examples to show that the application of inadequate methods for the analysis of adverse events in the reimbursement dossier can lead to a downgrading of the evidence on a drug's benefit in the subsequent assessment, as greater harm from the drug cannot be excluded with sufficient certainty. Legal regulations on the benefit assessment of drugs in Germany are presented, in particular, with regard to the analysis of adverse events. Differences in safety considerations between the drug approval process and the benefit assessment are discussed. We show that the naive application of simple proportions in reimbursement dossiers frequently leads to uninterpretable results if observations are censored and the average follow-up periods differ between treatment groups. Likewise, the application of incidence rates may be misleading in the case of recurrent events and unequal follow-up periods. To allow for an appropriate benefit assessment of drugs, adequate survival time methods accounting for time dependencies and duration of follow-up are required, not only for time-to-event efficacy endpoints but also for adverse events. © 2016 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.

Analysing adverse events by time-to-event models: the CLEOPATRA study

When analysing primary and secondary endpoints in a clinical trial with patients suffering from a chronic disease, statistical models for time-to-event data are commonly used and accepted. This is in contrast to the analysis of data on adverse events where often only a table with observed frequencies and corresponding test statistics is reported. An example is the recently published CLEOPATRA study where a three-drug regimen is compared with a two-drug regimen in patients with HER2-positive first-line metastatic breast cancer. Here, as described earlier, primary and secondary endpoints (progression-free and overall survival) are analysed using time-to-event models, whereas adverse events are summarized in a simple frequency table, although the duration of study treatment differs substantially. In this paper, we demonstrate the application of time-to-event models to first serious adverse events using the data of the CLEOPATRA study. This will cover the broad range between a simple incidence rate approach over survival and competing risks models (with death as a competing event) to multi-state models. We illustrate all approaches by means of graphical displays highlighting the temporal dynamics and compare the obtained results. For the CLEOPATRA study, the resulting hazard ratios are all in the same order of magnitude. But the use of time-to-event models provides valuable and additional information that would potentially be overlooked by only presenting incidence proportions. These models adequately address the temporal dynamics of serious adverse events as well as death of patients. Copyright © 2016 John Wiley & Sons, Ltd.

Safety data from randomized controlled trials: applying models for recurrent events

Simple descriptive listings and inference statistics based on 2×2 tables are still the most common way of summarizing and reporting adverse events data from randomized controlled trials, although these methods do not account for differences in observation times between treatment groups. Using standard methods from survival analysis such as the Cox model or Kaplan-Meier estimates would overcome this problem but limit the analysis to the first safety-related event of each subject. As an alternative, we discuss two models for recurrent events data-the Andersen-Gill and Prentice-Williams-Peterson model-regarding their applicability to safety data from randomized controlled trials. We argue that these models can be used to estimate two different quantities: a direct treatment effect on the risk of an event (Prentice-Williams-Peterson) and a total treatment effect as sum of the direct effect and the treatment's indirect effect via the event history (Anderson-Gill). Using simulated data, we illustrate the difference between these treatment effects and analyze the performance of both models in different scenarios. Because both models are limited to the analysis of cause-specific hazards if competing risks are present, we suggest to incorporate estimates of the mean frequency of events in the analysis to additionally allow the comparison of treatment effects on absolute event probabilities. We demonstrate the application of both models and the mean frequency function to safety endpoints with an illustrative analysis of data from a randomized phase-III study. Copyright © 2016 John Wiley & Sons, Ltd.

Statistical issues in the analysis of adverse events in time-to-event data

The aim of this work is to shed some light on common issues in the statistical analysis of adverse events (AEs) in clinical trials, when the main outcome is a time-to-event endpoint. To begin, we show that AEs are always subject to competing risks. That is, the occurrence of a certain AE may be precluded by occurrence of the main time-to-event outcome or by occurrence of another (fatal) AE. This has raised concerns on 'informative' censoring. We show that, in general, neither simple proportions nor Kaplan-Meier estimates of AE occurrence should be used, but common survival techniques for hazards that censor the competing event are still valid, but incomplete analyses. They must be complemented by an analogous analysis of the competing event for inference on the cumulative AE probability. The commonly used incidence rate (or incidence density) is a valid estimator of the AE hazard assuming it to be time constant. An estimator of the cumulative AE probability can be derived if the incidence rate of AE is combined with an estimator of the competing hazard. We discuss less restrictive analyses using non-parametric and semi-parametric approaches. We first consider time-to-first-AE analyses and then briefly discuss how they can be extended to the analysis of recurrent AEs. We will give a practical presentation with illustration of the methods by a simple example. Copyright © 2016 John Wiley & Sons, Ltd.