The attributable estimand: A new approach to account for intercurrent events

Application of hypothetical strategies in acute pain

Since the publication of ICH E9 (R1), "Addendum to statistical principles for clinical trials: on choosing appropriate estimands and defining sensitivity analyses in clinical trials," there has been a lot of debate about the hypothetical strategy for handling intercurrent events. Arguments against the hypothetical strategy are twofold: (1) the clinical question has limited clinical/regulatory interest; (2) the estimation may need strong statistical assumptions. In this article, we provide an example of a hypothetical strategy handling use of rescue medications in the acute pain setting. We argue that the treatment effect of a drug that is attributable to the treatment alone is the clinical question of interest and is important to regulators. The hypothetical strategy is important when developing non-opioid treatment as it estimates the treatment effect due to treatment during the pre-specified evaluation period whereas the treatment policy strategy does not. Two widely acceptable and non-controversial clinical inputs are required to construct a reasonable estimator. More importantly, this estimator does not rely on additional strong statistical assumptions and is considered reasonable for regulatory decision making. In this article, we point out examples where estimators for a hypothetical strategy can be constructed without any strong additional statistical assumptions besides acceptable clinical inputs. We also showcase a new way to obtain estimation based on disease specific clinical knowledge instead of strong statistical assumptions. In the example presented, we clearly demonstrate the advantages of the hypothetical strategy compared to alternative strategies including the treatment policy strategy and a composite variable strategy.

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.

Validity and Utility of a Hierarchical Composite End Point for Clinical Trials of Kidney Disease Progression: A Review

Clinical trials in nephrology often use composite end points comprising clinical events, such as onset of ESKD and initiation of kidney function replacement therapy, along with a sustained large ( e.g. , ≥50%) decrease in GFR. Such events typically occur late in the disease course, resulting in large trials in which most participants do not contribute clinical events. In addition, components of the end point are considered of equal importance; however, their clinical significance varies. For example, kidney function replacement therapy initiation is likely to be clinically more meaningful than GFR decline of ≥50%. By contrast, hierarchical composite end points (HCEs) combine multiple outcomes and prioritize each patient's most clinically relevant outcome for inclusion in analysis. In this review, we consider the use of HCEs in clinical trials of CKD progression, emphasizing the potential to combine dichotomous clinical events such as those typically used in CKD progression trials, with the continuous variable of GFR over time, while ranking all components according to clinical significance. We consider maraca plots to visualize overall treatment effects and the contributions of individual components, discuss the application of win odds in kidney HCE trials, and review general design considerations for clinical trials for CKD progression with kidney HCE as an efficacy end point.

Adherence, per-protocol effects, and the estimands framework

In the statistical literature, treatment effects in clinical trials are frequently described as either ITT or per-protocol effects. The estimand given for the per-protocol effect is the effect in adherers, where adherers are typically defined as adhering to the intervention as specified in the trial protocol. This dichotomy of treatment effects is unhelpful when there are in reality multiple treatment effects that can be of clinical interest and relevance. The terms "per-protocol" and "adherence" are confusing to non-statisticians. Protocols always allow for discontinuation of randomized treatment so participants discontinuing have actually followed the protocol. When rescue or additional medication is available, the effect in adherers could mean the effect regardless of use of these medications or the effect in a counterfactual world where the participant did not take the medication. Adherence can mean continuing to be prescribed a treatment or some arbitrary level of compliance with a medication that has been prescribed. The ICH E9 (R1) estimands framework provides an improved alternative for the description of treatment effects in clinical trials. Identification of important intercurrent events and the strategy used to handle these events is key to determining the treatment effect. When designing a trial, estimands should be properly defined according to this framework. It is time the statistical literature abandoned describing treatment effects as the effect in adherers or the per-protocol effect.

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.

Why estimands are needed to define treatment effects in clinical trials

BACKGROUND

The estimand for a clinical trial is a precise definition of the treatment effect to be estimated. Traditionally, estimates of treatment effects are based on either an ITT analysis or a per-protocol analysis. However, there are important clinical questions which are not addressed by either of these analyses. For example, consider a trial where patients take a rescue medication. The ITT analysis includes data after use of rescue, while the per-protocol analysis excludes these patients altogether. Neither of these analyses addresses the important question of what the treatment effect would have been if patients did not take rescue medication.

MAIN TEXT

Trial estimands provide a broader perspective compared to the limitations of ITT and per-protocol analysis. Trial treatment effects depend on how events occurring after treatment initiation such as use of alternative medication or discontinuation of the intervention are included in the definition. These events can be accounted for in different ways, depending on the clinical question of interest.

CONCLUSION

The estimand framework is an important step forward in improving the clarity and transparency of clinical trials. The centrality of estimands to clinical trials is currently not reflected in methods recommended by the Cochrane group or the CONSORT statement, the current standard for reporting clinical trials in medical journals. We encourage revisions to these guidelines.

Defining Clinical Trial Estimands: A Practical Guide for Study Teams with Examples Based on a Psychiatric Disorder

While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework.

Accurate Collection of Reasons for Treatment Discontinuation to Better Define Estimands in Clinical Trials

BACKGROUND

Reasons for treatment discontinuation are important not only to understand the benefit and risk profile of experimental treatments, but also to help choose appropriate strategies to handle intercurrent events in defining estimands. The current case report form (CRF) commonly in use mixes the underlying reasons for treatment discontinuation and who makes the decision for treatment discontinuation, often resulting in an inaccurate collection of reasons for treatment discontinuation.

METHODS AND RESULTS

We systematically reviewed and analyzed treatment discontinuation data from nine phase 2 and phase 3 studies for insulin peglispro. A total of 857 participants with treatment discontinuation were included in the analysis. Our review suggested that, due to the vague multiple-choice options for treatment discontinuation present in the CRF, different reasons were sometimes recorded for the same underlying reason for treatment discontinuation. Based on our review and analysis, we suggest an intermediate solution and a more systematic way to improve the current CRF for treatment discontinuations.

CONCLUSION

This research provides insight and directions on how to optimize the CRF for recording treatment discontinuation. Further work needs to be done to build the learning into Clinical Data Interchange Standards Consortium standards.

CLINICAL TRIALS

Clinicaltrials.gov numbers: NCT01027871 (Phase 2 for type 2 diabetes), NCT01049412 (Phase 2 for type 1 diabetes), NCT01481779 (IMAGINE 1 Study), NCT01435616 (IMAGINE 2 Study), NCT01454284 (IMAGINE 3 Study), NCT01468987 (IMAGINE 4 Study), NCT01582451 (IMAGINE 5 Study), NCT01790438 (IMAGINE 6 Study), NCT01792284 (IMAGINE 7 Study).

Principles and recommendations for incorporating estimands into clinical study protocol templates

Clinical study protocols are the foundation of good clinical studies. Prospective and multidisciplinary collaboration that pays attention to the design of all components of the study protocol can ensure that a clinical study will answer the research questions posed in a reliable manner that is meaningful for decision-makers and patients. The ICH E9(R1) addendum on estimands and sensitivity analysis in clinical trials provides a framework for clinical study planning to ensure alignment between study objectives, design, conduct, and analysis. The estimand or clinical question posed can be regarded as the backbone of the study and the clinical study protocol should reflect estimands accordingly. In practice, stakeholders are still learning how to embrace the estimand framework and how it impacts studies and study documents. In this paper, we anticipate that a protocol structure centred around estimands, or objectives rather than endpoints alone will prevail for all types of studies. To assist sponsors during this paradigm shift, this paper provides discussion and guidance for implementing the estimand framework in protocol templates.

Marking 2-Years of New Thinking in Clinical Trials: The Estimand Journey

The ICH E9(R1) addendum on Estimands and Sensitivity Analyses in Clinical Trials has introduced a new estimand framework for the design, conduct, analysis, and interpretation of clinical trials. We share Pharmaceutical Industry experiences of implementing the estimand framework in the first two years since the final guidance became available with key lessons learned and highlight what else needs to be done to continue the journey in embedding the estimand framework in clinical trials. Emerging best practices and points to consider on strategies for implementing a new estimand thinking process are provided. Whilst much of the focus of implementing ICH E9(R1) to date has been on defining estimands, we highlight some of the important aspects relating to the choice of statistical analysis methods and sensitivity analyses to ensure estimands can be estimated robustly with minimal bias. In particular, we discuss the implications if complete follow-up is not possible when the treatment policy strategy is being used to handle intercurrent events. ICH E9(R1) was introduced just before the start of the COVID-19 pandemic, but a positive outcome from the pandemic has been an acceleration in the adoption of the estimand framework, including differentiating intercurrent events related or not related to the pandemic. In summary, much has been learned on the estimand journey and continued sharing of case studies will help to further advance the understanding and increase awareness across all clinical researchers of the estimand framework.

Estimands-a Basic Element for Clinical Trials. Part 29 of a Series on Evaluation of Scientific Publications

BACKGROUND

Clinical trials are of central importance for the evaluation and comparison of treatments. The transparency and intelligibility of the treatment effect under investigation is an essential matter for physicians, patients, and health-care authorities. The estimand framework has been introduced because many trials are deficient in this respect.

METHODS

Introduction, definition, and application of the estimand framework on the basis of an example and a selective review of the literature.

RESULTS

The estimand framework provides a systematic approach to the definition of the treatment effect under investigation in a clinical trial. An estimand consists of five attributes: treatment, population, variable, population-level summary, and handling of intercurrent events. Each of these attributes is defined in an interdisciplinary discussion during the trial planning phase, based on the clinical question being asked. Special attention is given to the handling of intercurrent events (ICEs): these are events-e.g., discontinuation or modification of treatment or the use of emergency medication-that can occur once the treatment has begun and might affect the possibility of observing the endpoints or their interpretability. There are various strategies for the handling of ICEs; these can, for example, also reflect the existing intentionto- treat (ITT) principle. Per-protocol analyses, in contrast, are prone to bias and cannot be represented in a sensible manner by an estimand, although they may be performed as a supplementary analysis. The discussion of potential intercurrent events and how they should appropriately be handled in view of the aim of the trial must already take place in the planning phase.

CONCLUSION

Use of the estimand framework should make it easier for both physicians and patients to understand what trials reveal about the efficacy of treatment, and to compare the results of different trials.

Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic

The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article, we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic.

Why ITT analysis is not always the answer for estimating treatment effects in clinical trials

For many years there has been a consensus among the Clinical Research community that ITT analysis represents the correct approach for the vast majority of trials. Recent worldwide regulatory guidance for pharmaceutical industry trials has allowed discussion of alternatives to the ITT approach to analysis; different treatment effects can be considered which may be more clinically meaningful and more relevant to patients and prescribers. The key concept is of a trial "estimand", a precise description of the estimated treatment effect. The strategy chosen to account for patients who discontinue treatment or take alternative medications which are not part of the randomised treatment regimen are important determinants of this treatment effect. One strategy to account for these events is treatment policy, which corresponds to an ITT approach. Alternative equally valid strategies address what the treatment effect is if the patient actually takes the treatment or does not use specific alternative medication. There is no single right answer to which strategy is most appropriate, the solution depends on the key clinical question of interest. The estimands framework discussed in the new guidance has been particularly useful in the context of the current COVID-19 pandemic and has clarified what choices are available to account for the impact of COVID-19 on clinical trials. Specifically, an ITT approach addresses a treatment effect that may not be generalisable beyond the current pandemic.

Triple Inhaled Therapy at Two Glucocorticoid Doses in Moderate-to-Very-Severe COPD

BACKGROUND

Triple fixed-dose regimens of an inhaled glucocorticoid, a long-acting muscarinic antagonist (LAMA), and a long-acting β₂-agonist (LABA) for chronic obstructive pulmonary disease (COPD) have been studied at single dose levels of inhaled glucocorticoid, but studies at two dose levels are lacking.

METHODS

In a 52-week, phase 3, randomized trial to evaluate the efficacy and safety of triple therapy at two dose levels of inhaled glucocorticoid in patients with moderate-to-very-severe COPD and at least one exacerbation in the past year, we assigned patients in a 1:1:1:1 ratio to receive twice-daily inhaled doses of triple therapy (inhaled glucocorticoid [320 μg or 160 μg of budesonide], a LAMA [18 μg of glycopyrrolate], and a LABA [9.6 μg of formoterol]) or one of two dual therapies (18 μg of glycopyrrolate plus 9.6 μg of formoterol or 320 μg of budesonide plus 9.6 μg of formoterol). The primary end point was the annual rate (the estimated mean number per patient per year) of moderate or severe COPD exacerbations, as analyzed in the modified intention-to-treat population with the use of on-treatment data only.

RESULTS

The modified intention-to-treat population comprised 8509 patients. The annual rates of moderate or severe exacerbations were 1.08 in the 320-μg-budesonide triple-therapy group (2137 patients), 1.07 in the 160-μg-budesonide triple-therapy group (2121 patients), 1.42 in the glycopyrrolate-formoterol group (2120 patients), and 1.24 in the budesonide-formoterol group (2131 patients). The rate was significantly lower with 320-μg-budesonide triple therapy than with glycopyrrolate-formoterol (24% lower: rate ratio, 0.76; 95% confidence interval [CI], 0.69 to 0.83; P<0.001) or budesonide-formoterol (13% lower: rate ratio, 0.87; 95% CI, 0.79 to 0.95; P = 0.003). Similarly, the rate was significantly lower with 160-μg-budesonide triple therapy than with glycopyrrolate-formoterol (25% lower: rate ratio, 0.75; 95% CI, 0.69 to 0.83; P<0.001) or budesonide-formoterol (14% lower: rate ratio, 0.86; 95% CI, 0.79 to 0.95; P = 0.002). The incidence of any adverse event was similar across the treatment groups (range, 61.7 to 64.5%); the incidence of confirmed pneumonia ranged from 3.5 to 4.5% in the groups that included inhaled glucocorticoid use and was 2.3% in the glycopyrrolate-formoterol group.

CONCLUSIONS

Triple therapy with twice-daily budesonide (at either the 160-μg or 320-μg dose), glycopyrrolate, and formoterol resulted in a lower rate of moderate or severe COPD exacerbations than glycopyrrolate-formoterol or budesonide-formoterol. (Funded by AstraZeneca, ETHOS ClinicalTrials.gov number, NCT02465567.).