Quantitative assessment of adverse events in clinical trials: Comparison of methods at an interim and the final analysis

Hollaender, Norbert; Gonzalez-Maffe, Juan; Jehl, Valentine

doi:10.1002/bimj.201800234

Cited by 5 publications

(5 citation statements)

References 18 publications

(20 reference statements)

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“…The problems related to biased estimation of the AE probability have been previously described 1,3,4,11,12 . Here we extend the discussion by investigating the following three questions: (i) What is the impact of choosing different estimators of the AE probabilities on group comparisons in terms of bias and precision?…”

Section: Introductionmentioning

confidence: 71%

Estimating and comparing adverse event probabilities in the presence of varying follow‐up times and competing events

Stegherr¹,

Schmoor

Lübbert

et al. 2021

Pharmaceutical Statistics

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Safety analyses of adverse events (AEs) are important in assessing benefit–risk of therapies but are often rather simplistic compared to efficacy analyses. AE probabilities are typically estimated by incidence proportions, sometimes incidence densities or Kaplan–Meier estimation are proposed. These analyses either do not account for censoring, rely on a too restrictive parametric model, or ignore competing events. With the non‐parametric Aalen‐Johansen estimator as the “gold standard”, that is, reference estimator, potential sources of bias are investigated in an example from oncology and in simulations, for both one‐sample and two‐sample scenarios. The Aalen‐Johansen estimator serves as a reference, because it is the proper non‐parametric generalization of the Kaplan–Meier estimator to multiple outcomes. Because of potential large variances at the end of follow‐up, comparisons also consider further quantiles of the observed times. To date, consequences for safety comparisons have hardly been investigated, the impact of using different estimators for group comparisons being unclear. For example, the ratio of two both underestimating or overestimating estimators may not be comparable to the ratio of the reference, and our investigation also considers the ratio of AE probabilities. We find that ignoring competing events is more of a problem than falsely assuming constant hazards by the use of the incidence density and that the choice of the AE probability estimator is crucial for group comparisons.

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Section: Introductionmentioning

confidence: 71%

Estimating and comparing adverse event probabilities in the presence of varying follow‐up times and competing events

Stegherr¹,

Schmoor

Lübbert

et al. 2021

Pharmaceutical Statistics

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“…We will rank the severity of AEs according to the CTCAE version 5.0. The cause of the AEs will be ranked as unknown, definitely not related, probably not related, possibly related, probably related and definitely related 29…”

Section: Discussionmentioning

confidence: 99%

“…The cause of the AEs will be ranked as unknown, definitely not related, probably not related, possibly related, probably related and definitely related. 29 Moreover, we will report the mean change in the laboratory tests regarding AEs from the beginning to the end of the 12-week intervention: haemoglobin, AST, ALT, GGT, cystatin C, creatinine and eGFR.…”

Section: Safety Outcomesmentioning

confidence: 99%

β-hydroxy-β-methylbutyrate-enriched nutritional supplements for obese adults during weight loss: study protocol of a randomised, double-blind, placebo-controlled clinical trial

et al. 2022

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IntroductionObesity is a public health challenge worldwide. To maintain a healthy weight, dieting and lifestyle changes are the first-line interventions; however, these interventions are of poor compliance and may cause body composition changes, mainly skeletal muscle wasting (sarcopenia). Dietary supplements for improving body composition while inducing weight loss are therefore needed. β-hydroxy-β-methylbutyrate (HMB) has been proven to be effective for improving muscle mass and muscle strength in athletes, older adults and patients with cancer. We aim to evaluate the effectiveness and safety of HMB-enriched nutritional supplements for improving muscle mass and muscle function in obese adults during calorie restriction.Method and analysisA total of 72 Chinese adults with obesity will be randomised to receive HMB-enriched nutritional supplements (65 g/day) or a placebo for 12 weeks. Participants in both groups will also receive calorie restrictions based on the individualised nutrition guidance of dietitians. Participants and investigators will be blinded to the allocations. The primary outcome will be the mean change in whole-body skeletal muscle mass (measured by bioelectrical impedance analysis). The secondary outcomes will include the mean change of appendicular skeletal muscle mass, body fat mass, basal metabolic rate, phase angle, muscle function and serum biomarkers. The enrolment will commence in December 2021 and will proceed until March 2022.Ethics and disseminationThis protocol has been approved by the Biomedical Ethics Committee of West China Hospital (2021-771). All potential subjects will be required to sign a written informed consent. The results of this study will be reported in peer-reviewed academic journals and conferences.Trial registration numberNCT04953936.

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“…The proportion of censoring may be different at the time point of a safety interim analysis of trials which are typically presented to data safety monitoring boards. For this situation the different estimators may behave differently [20].…”

Section: Discussionmentioning

confidence: 99%

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- estimation of adverse event risks

Stegherr,

Schmoor,

Beyersmann

et al. 2020

Preprint

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Background: The SAVVY project aims to improve the analyses of adverse event (AE) data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events. Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density, or a nonparametric Kaplan-Meier estimator are used, which either ignore censoring or competing events. In an empirical study including randomized clinical trials from several sponsor organisations, these potential sources of bias are investigated. The main purpose of the empirical study is to compare the estimators that are typically used in AE analysis to the non-parametric Aalen-Johansen estimator as the gold-standard. The present paper reports on one-sample findings, while a companion paper considers consequences when comparing safety between treatment groups. Methods: Estimators are compared with descriptive statistics, graphical displays and with a more formal assessment using a random effects meta-analysis. The influence of different factors on the size of the bias is investigated in a meta-regression. Comparisons are conducted at the maximum follow-up time and at earlier evaluation time points. Competing events definition does not only include death before AE but also end of follow-up for AEs due to events possibly related to the disease course or safety of the treatment. Results: Ten sponsor organisations provided 17 clinical trials including 186 types of investigated AEs. The one minus Kaplan-Meier estimator was on average about 1.2-fold larger than the Aalen-Johansen estimator and the probability transform of the incidence density ignoring competing events overestimated the AE probability even more. Leading forces influencing bias were the amount of censoring and of competing events. The presence of many competing events in our study decreased the amount of censoring. As a consequence, the average bias using the incidence proportion was less than 5%. Assuming constant hazards using incidence densities was hardly an issue provided that competing events were accounted for. Conclusions: Both the choice of the estimator of the cumulative AE probability and a careful definition of competing events are crucial. There is an urgent need to improve the guidelines of reporting risks of AEs so that the Kaplan-Meier estimator and the incidence proportion are finally replaced by the Aalen-Johansen estimator with an appropriate definition of competing events.

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Quantitative assessment of adverse events in clinical trials: Comparison of methods at an interim and the final analysis

Cited by 5 publications

References 18 publications

Estimating and comparing adverse event probabilities in the presence of varying follow‐up times and competing events

Estimating and comparing adverse event probabilities in the presence of varying follow‐up times and competing events

β-hydroxy-β-methylbutyrate-enriched nutritional supplements for obese adults during weight loss: study protocol of a randomised, double-blind, placebo-controlled clinical trial

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- estimation of adverse event risks

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