Intercurrent Events Research Articles

External comparator studies and the joint application of the estimand and target trial emulation frameworks

The estimand framework (EF) and the target trial emulation framework (TTEF) are two important frameworks that can inform the design and analysis of external comparator (EC) studies. The EF helps clarifying the quantity to be estimated (the “estimand”), especially regarding the handling of post-baseline intercurrent events which interfere with the existence or interpretation of endpoints. Similarly, the TTEF is instrumental for specifying key design components of a hypothetical randomized trial and identifying which of these an EC study can and cannot emulate. We offer considerations about the joint application of both frameworks by combining the five EF attributes— treatment, population, endpoint, intercurrent events, and population-level summary—with the seven TTEF components— eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcomes, causal contrasts, and data analysis plan. Any overlap is identified, as well as omissions and unique contributions from both frameworks. Furthermore, we highlight specific considerations when applying these joint elements to EC studies.

Application of the estimand framework for an emulated trial using reference based multiple imputation to investigate informative censoring

BackgroundThe ICH E9 (R1) addendum on Estimands and Sensitivity analysis in Clinical trials proposes a framework for the design and analysis of clinical trials aimed at improving clarity around the definition of the targeted treatment effect (the estimand) of a study.MethodsWe adopt the estimand framework in the context of a study using “trial emulation” to estimate the risk of pneumocystis pneumonia, an opportunistic disease contracted by people living with HIV and AIDS having a weakened immune system, when considering two antibiotic treatment regimes for stopping antibiotic prophylaxis treatment against this disease. A “while on treatment” strategy has been implemented for post-randomisation (intercurrent) events. We then perform a sensitivity analysis using reference based multiple imputation to model a scenario in which patients lost to follow-up stop taking prophylaxis.ResultsThe primary analysis indicated a protective effect for the new regime which used viral suppression as prophylaxis stopping criteria (hazard ratio (HR) 0.78, 95% confidence interval [0.69, 0.89], p < 0.001). For the sensitivity analysis, when we apply the “jump to off prophylaxis” approach, the hazard ratio is almost the same compared to that from the primary analysis (HR 0.80 [0.69, 0.95], p = 0.009). The sensitivity analysis confirmed that the new regime exhibits a clear improvement over the existing guidelines for PcP prophylaxis when those lost to follow-up “jump to off prophylaxis”.ConclusionsOur application using reference based multiple imputation demonstrates the method’s flexibility and simplicity for sensitivity analyses in the context of the estimand framework for (emulated) trials.

Possible Data-Generating Models of Longitudinal Continuous Outcomes and Intercurrent Events to Investigate Estimands

We aim to explore data-generating models to jointly simulate outcomes and intercurrent events for randomized clinical trials to enable the investigation of estimands. We develop four possible data-generating models for the joint distribution of longitudinal continuous clinical outcomes and intercurrent events under the scenario where they are observable: a selection model, a pattern-mixture mixed model, a shared-parameter model, and a joint model of longitudinally observed outcomes and a survival model for intercurrent events. We present a case study in a short-term depression trial with repeated measurements of continuous outcomes and two types of intercurrent events, and evaluate the potential and challenges of such data-generating models. Simulating randomized trials with outcomes and intercurrent events is a complex undertaking. We found that the four possible data-generating models can simulate different types of intercurrent events and associated longitudinal outcomes. They can be used to emulate envisaged patterns of intercurrent events and outcomes informed by prior available trial data or expectations. Model and parameter choice for a given application require further development. The four possible data-generating models could be used to investigate different estimands and their properties in-depth in the design stage. Thereby they are useful tools for the selection of estimands a priori.

Tailored guidance to apply the Estimand framework to Trials within Cohorts (TwiCs) studies

Objective: The estimand framework offers a structured approach to define the treatment effect to be estimated in a clinical study. Defining the estimand upfront helps formulating the research question and informs study design, data collection and statistical analysis methods. Since the Trials within Cohorts (TwiCs) design has unique characteristics, the objective of this study is to describe considerations and provide guidance for formulating estimands for TwiCs studies.Methods: The key attributes of an estimand are the target population, treatments that are compared, the endpoint, intercurrent events and their handling, and the population-level summary measure. The estimand framework was applied retrospectively to two TwiCs studies: the SPONGE and UMBRELLA Fit trial. The aim is to demonstrate how the estimand framework can be implemented in TwiCs studies, thereby focusing on considerations relevant for defining the estimand. Three estimands were defined for both studies. For the SPONGE trial, estimators were derived.Results: Intercurrent events considered to occur exclusively or more frequently in TwiCs studies compared to conventional randomized trials included intervention refusal after randomization, misalignment of timing of routine cohort measurements and the intervention period, and participants in the control arm initiating treatments similar to the studied intervention. Considerations for handling refusal after randomization related to decisions on whether the target population should include all eligible participants or the subpopulation that would accept (or undergo) the intervention when offered. Considerations for handling treatment initiation in the control arm and misalignments of timing related to decisions on whether such events should be considered part of treatment policy or whether interest is in a hypothetical scenario where such events do not occur.Conclusion: The TwiCs study design has unique features that pose specific considerations when formulating an estimand. The examples in this study can provide guidance in the definition of estimands in future TwiCs studies.

The UK resuscitative endovascular balloon occlusion of the aorta in trauma patients with life-threatening torso haemorrhage: the (UK-REBOA) multicentre RCT.

The most common cause of preventable death after injury is haemorrhage. Resuscitative endovascular balloon occlusion of the aorta is intended to provide earlier, temporary haemorrhage control, to facilitate transfer to an operating theatre or interventional radiology suite for definitive haemostasis. To compare standard care plus resuscitative endovascular balloon occlusion of the aorta versus standard care in patients with exsanguinating haemorrhage in the emergency department. Pragmatic, multicentre, Bayesian, group-sequential, registry-enabled, open-label, parallel-group randomised controlled trial to determine the clinical and cost-effectiveness of standard care plus resuscitative endovascular balloon occlusion of the aorta, compared to standard care alone. United Kingdom Major Trauma Centres. Trauma patients aged 16 years or older with confirmed or suspected life-threatening torso haemorrhage deemed amenable to adjunctive treatment with resuscitative endovascular balloon occlusion of the aorta. Participants were randomly assigned 1 : 1 to: standard care, as expected in a major trauma centre standard care plus resuscitative endovascular balloon occlusion of the aorta. Primary: Mortality at 90 days. Secondary: Mortality at 6 months, while in hospital, and within 24, 6 and 3 hours; need for haemorrhage control procedures, time to commencement of haemorrhage procedure, complications, length of stay (hospital and intensive care unit-free days), blood product use. Health economic: Expected United Kingdom National Health Service perspective costs, life-years and quality-adjusted life-years, modelled over a lifetime horizon. Case report forms, Trauma Audit and Research Network registry, NHS Digital (Hospital Episode Statistics and Office of National Statistics data). Ninety patients were enrolled: 46 were randomised to standard care plus resuscitative endovascular balloon occlusion of the aorta and 44 to standard care. Mortality at 90 days was higher in the standard care plus resuscitative endovascular balloon occlusion of the aorta group (54%) compared to the standard care group (42%). The odds ratio was 1.58 (95% credible interval 0.72 to 3.52). The posterior probability of an odds ratio > 1 (indicating increased odds of death with resuscitative endovascular balloon occlusion of the aorta) was 86.9%. The overall effect did not change when an enthusiastic prior was used or when the estimate was adjusted for baseline characteristics. For the secondary outcomes (3, 6 and 24 hours mortality), the posterior probability that standard care plus resuscitative endovascular balloon occlusion of the aorta was harmful was higher than for the primary outcome. Additional analyses to account for intercurrent events did not change the direction of the estimate for mortality at any time point. Death due to haemorrhage was more common in the standard care plus resuscitative endovascular balloon occlusion of the aorta group than in the standard care group. There were no serious adverse device effects. Resuscitative endovascular balloon occlusion of the aorta is less costly (probability 99%), due to the competing mortality risk but also substantially less effective in terms of lifetime quality-adjusted life-years (probability 91%). The size of the study reflects the relative infrequency of exsanguinating traumatic haemorrhage in the United Kingdom. There were some baseline imbalances between groups, but adjusted analyses had little effect on the estimates. This is the first randomised trial of the addition of resuscitative endovascular balloon occlusion of the aorta to standard care in the management of exsanguinating haemorrhage. All the analyses suggest that a strategy of standard care plus resuscitative endovascular balloon occlusion of the aorta is potentially harmful. The role (if any) of resuscitative endovascular balloon occlusion of the aorta in the pre-hospital setting remains unclear. Further research to clarify its potential (or not) may be required. This trial is registered as ISRCTN16184981. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 14/199/09) and is published in full in Health Technology Assessment; Vol. 28, No. 54. See the NIHR Funding and Awards website for further award information.

Estimands in clinical trials of complex disease processes.

Clinical trials with random assignment of treatment provide evidence about causal effects of an experimental treatment compared to standard care. However, when disease processes involve multiple types of possibly semi-competing events, specification of target estimands and causal inferences can be challenging. Intercurrent events such as study withdrawal, the introduction of rescue medication, and death further complicate matters. There has been much discussion about these issues in recent years, but guidance remains ambiguous. Some recommended approaches are formulated in terms of hypothetical settings that have little bearing in the real world. We discuss issues in formulating estimands, beginning with intercurrent events in the context of a linear model and then move on to more complex disease history processes amenable to multistate modeling. We elucidate the meaning of estimands implicit in some recommended approaches for dealing with intercurrent events and highlight the disconnect between estimands formulated in terms of potential outcomes and the real world.

Principal quantile treatment effect estimation using principal scores.

Intercurrent events and estimands play a key role in defining the treatment effects of interest precisely. Sometimes the median or other quantiles of outcomes in a principal stratum according to potential occurrence of intercurrent events are of interest in randomized clinical trials. Naïve analyses such as those based on the observed occurrence of the intercurrent events lead to biased results. Therefore, we propose principal quantile treatment effect estimators that can nonparametrically estimate the distribution of potential outcomes by principal score weighting without relying on the exclusion restriction assumption. Our simulation studies show that the proposed method works in situations where the median or quantiles may be regarded as the preferred population-level summary over the mean. We illustrate our proposed method by using data from a randomized controlled trial conducted on patients with nonerosive reflux disease.

Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation.

The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline 'intercurrent' events (IEs) are to be handled. In late-stage clinical trials, it is common to handle IEs like 'treatment discontinuation' using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.

Defining estimand for the win ratio: Separate the true effect from censoring.

The win ratio has been increasingly used in trials with hierarchical composite endpoints. While the outcomes involved and the rule for their comparisons vary with the application, there is invariably little attention to the estimand of the resulting statistic, causing difficulties in interpretation and cross-trial comparison. We make the case for articulating the estimand as a first step to win ratio analysis and establish that the root cause for its elusiveness is its intrinsic dependency on the time frame of comparison, which, if left unspecified, is set haphazardly by trial-specific censoring. From the statistical literature, we summarize two general approaches to overcome this uncertainty-a nonparametric one that pre-specifies the time frame for all comparisons, and a semiparametric one that posits a constant win ratio across all times-each with publicly available software and real examples. Finally, we discuss unsolved challenges, such as estimand construction and inference in the presence of intercurrent events.

Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation.

The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on- and off-treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article introduces a novel approach to parameterising this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a retrieved dropout compliance model, using both on- and off-treatment data, to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable, or are poorly estimated leading to unrealistically large standard errors in the resulting analysis. We refer to this new approach as retrieved dropout reference-base centred multiple imputation.

Real-world effectiveness of belimumab in patients with lupus in China: RELIABLE observational cohort study protocol

IntroductionThe efficacy of belimumab in SLE has been demonstrated in randomised clinical trials, and its real-world effectiveness has been shown in studies in several countries. While belimumab was approved for...

Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World Intervention Graphs.

Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges in clarifying the estimand and underlying assumptions. This article proposes expressing causal structures using graphical tools-directed acyclic graphs (DAGs) and single-world intervention graphs (SWIGs)-in the planning phase of a clinical trial. It presents five rules for selecting a sufficient set of adjustment variables on the basis of a diagram representing the clinical trial, along with three case studies of randomized and single-arm trials and a brief tutorial on DAG and SWIG. Through the case studies, DAGs appear effective in clarifying assumptions for identifying causal effects, although SWIGs should complement DAGs due to their limitations in the presence of intercurrent events in oncology research.

Application of the Estimand Framework to Anesthesia Trials.

Events occurring after randomization, such as use of rescue medication, treatment discontinuation, or death, are common in randomized trials. These events can change either the existence or interpretation of the outcome of interest. However, appropriate handling of these intercurrent events is often unclear. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9(R1) addendum introduced the estimand framework, which aligns trial objectives with the design, conduct, statistical analysis, and interpretation of results. This article describes how the estimand framework can be used in anesthesia trials to precisely define the treatment effect to be estimated, key attributes of an estimand, common intercurrent events in anesthesia trials with strategies for handling them, and use of the estimand framework in a hypothetical anesthesia trial on postoperative delirium. When planning anesthesia trials, clearly defining the estimand is vital to ensure that what is being estimated is clearly understood, is clinically relevant, and helps answer the clinical questions of interest.

Special issue PRO] A demonstration of estimands and sensitivity analyses for time-to-deterioration of patient reported outcomes

ABSTRACT In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation.

Current developments of the estimand concept.

Since the introduction of the estimand in therapeutical studies, several adaptions have been developed. This short article highlights the important aspects of the estimand concept. A literature research was conducted to identify different extensions to this framework. Different modified strategies for intercurrent events are presented, as well as examples of methods to implement the estimand in clinical studies. The article reflects that the estimand is an ongoing research field with further exploration.

Swissped-RECOVERY: masked independent adjudication for the interpretation of non-randomised treatment in a two-arm open-label randomised controlled trial (methylprednisolone vs immunoglobulins) in Paediatric Inflammatory Multisystem Syndrome Temporally Associated with SARS-CoV-2 (PIMS-TS) involving 10 secondary and tertiary paediatric hospitals in Switzerland

ObjectivesIn trials of acute severe infections or inflammations frequent administration of non-randomised treatment (ie, intercurrent event) in response to clinical events is expected. These events may affect the interpretation of...

Composite event-free-survival as an endpoint in oncology drug evaluation: Review and guidance perspectives from the Haute Autorité de Santé (HAS)

BackgroundThe use of right-censored composite endpoints, such as progression-free survival, has been questioned in haemato-oncology trials due to potential bias in estimated treatment effect. This may impact the accuracy of health technology evaluations. We hypothesized that there is heterogeneity and potential sources of bias in the reporting of composite endpoints to health technology assessment (HTA) bodies. MethodsWe reviewed the submissions for reimbursement of oncology drugs in 2021 and 2022 that used a composite endpoint in the pivotal trial, after appraisal by the French HTA body. The retrieved information included the clinical study report, protocol, and statistical analysis plan submitted by the industry. All events of the composite endpoint and all causes of censored observations were measured. The design characteristics and treatment effect estimates were recorded. FindingsSeventy-six submissions were selected, including seven without a right-censored endpoint and four evaluating associations, resulting in 65 analysed records: 17 for haematological and 48 for solid tumours. Out these 65 submissions, 47 (72·3%) used a randomized controlled design, and 18 (27·7%) a non-comparative design. The most frequently used composite endpoint was progression-free survival, used in 54 (83·1%) of the submissions. Censoring was possibly informative in 51 (92·7%) cases, mostly due to the onset of new treatment (44/51, 86·3%) and/or discontinuation of follow-up (33/51, 64·7%). In contrast, 38 (58·5%) trials reported a quantification of censored observations, with only 12/51 (23·5%) quantifying the informative ones. The estimated treatment effect on the composite outcome increased with the amount of censoring, suggesting a higher benefit of the drug, but remained below that on survival with poor evidence of surrogacy (R-squared=0·23). InterpretationClinical study reports should be improved in terms of reporting censoring, while stakeholders should be aware of this potential source of bias. At a minimum, sensitivity analysis that ignores intercurrent events should be requested.

Association of Acute Respiratory Disease Events with Quantitative Interstitial Abnormality Progression at CT in Individuals with a History of Smoking.

Background Acute respiratory disease (ARD) events are often thought to be airway-disease related, but some may be related to quantitative interstitial abnormalities (QIAs), which are subtle parenchymal abnormalities on CT scans associated with morbidity and mortality in individuals with a smoking history. Purpose To determine whether QIA progression at CT is associated with ARD and severe ARD events in individuals with a history of smoking. Materials and Methods This secondary analysis of a prospective study included individuals with a 10 pack-years or greater smoking history recruited from multiple centers between November 2007 and July 2017. QIA progression was assessed between baseline (visit 1) and 5-year follow-up (visit 2) chest CT scans. Episodes of ARD were defined as increased cough or dyspnea lasting 48 hours and requiring antibiotics or corticosteroids, whereas severe ARD episodes were those requiring an emergency room visit or hospitalization. Episodes were recorded via questionnaires completed every 3 to 6 months. Multivariable logistic regression and zero-inflated negative binomial regression models adjusted for comorbidities (eg, emphysema, small airway disease) were used to assess the association between QIA progression and episodes between visits 1 and 2 (intercurrent) and after visit 2 (subsequent). Results A total of 3972 participants (mean age at baseline, 60.7 years ± 8.6 [SD]; 2120 [53.4%] women) were included. Annual percentage QIA progression was associated with increased odds of one or more intercurrent (odds ratio [OR] = 1.29 [95% CI: 1.06, 1.56]; P = .01) and subsequent (OR = 1.26 [95% CI: 1.05, 1.52]; P = .02) severe ARD events. Participants in the highest quartile of QIA progression (≥1.2%) had more frequent intercurrent ARD (incidence rate ratio [IRR] = 1.46 [95% CI: 1.14, 1.86]; P = .003) and severe ARD (IRR = 1.79 [95% CI: 1.18, 2.73]; P = .006) events than those in the lowest quartile (≤-1.7%). Conclusion QIA progression was independently associated with higher odds of severe ARD events during and after radiographic progression, with higher frequency of intercurrent severe events in those with faster progression. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Little in this issue.

Time-to-event estimands and loss to follow-up in oncology in light of the estimands guidance.

Time-to-event estimands are central to many oncology clinical trials. The estimands framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events (ICEs) that occur after treatment initiation and "affect either the interpretation or the existence of the measurements associated with the clinical question of interest." We discuss a practical problem in clinical trial design and execution, that is, in some clinical contexts it is not feasible to systematically follow patients to an event of interest. Loss to follow-up in the presence of intercurrent events can affect the meaning and interpretation of the study results. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest and study design, impact on data collection, and other practical implications. When patients cannot be systematically followed, compromise may be necessary to select the best available estimand that can be feasibly estimated under the circumstances. We discuss the use of sensitivity and supplementary analyses to examine assumptions of interest.

Estimands in CNS trials – A review of strategies for addressing intercurrent events

BackgroundThe estimands framework represents a significant innovation for the design, conduct, analysis, and interpretation of clinical trials. An aim of the framework is to increase precision and transparency on the handling of intercurrent events (IEs), defined as events occurring after treatment initiation and affecting the endpoint. While the experience in constructing and reporting estimands in the published literature is limited, developers performing confirmatory studies are already making use of the new paradigm, allowing to survey the strategies proposed by applicants and endorsed by regulators. MethodsTo identify strategies for handling IEs in confirmatory central nervous system (CNS) trials, we searched scientific advice letters issued by the European Medicines Agency (EMA) between 2017 and 2022. We developed a categorisation of the IEs and classified, according to the strategies defined in the framework, the strategies proposed by the Applicants and recommended by the agency. Strategies proposed and recommended were summarised by category of IEs, and the rationale for the choices was analysed qualitatively. ResultsIn total, 170 IEs were identified in 52 confirmatory trials. A clear preference for the treatment policy strategy for treatment discontinuation and for the hypothetical strategy for pandemic-related disruptions was identified. For other categories of IEs, there are more mixed patterns. DiscussionThis study highlights the multidimensional nature of choosing a strategy for an IE. For different occurring IEs in confirmatory CNS trials different strategies are of regulatory interest, depending on the trial objective, underlying disease properties, rarity of disease, as well as frequency and timing of IEs and their relatedness to the disease.