Comparison of g-Estimation Approaches for Handling Symptomatic Medication at Multiple Timepoints in Alzheimer’s Disease with a Hypothetical Strategy

ESRD After Heart Failure, Myocardial Infarction, or Stroke in Type 2 Diabetic Patients With CKD

BackgroundPhase 3 randomized clinical trials within Alzheimer’s Disease (AD) typically last over 18 months. Post‐baseline participants can use additional treatment for Alzheimer’s disease, potentially impacting the cognitive ability as evaluated by the primary endpoint. Consequently, this could overestimate or underestimate the treatment effect, depending on the distribution of usage between treatment arms.MethodThe ICH E9 (R1) addendum’s estimand framework provide a means of precisely defining the clinical question of interest, in particular detailing how intercurrent events such as ‘use of additional AD medication’ should be handled and how to interpret the estimated treatment effect. Donohue et al (2020) suggested to use a treatment policy strategy for initiation of symptomatic treatments, based on the observation that placebo subjects did not experience an improvement after initiation of symptomatic treatment. The EMA guideline for Alzheimer’s disease (2018), recommends using a hypothetical strategy for initiation of symptomatic treatment.ResultUse of additional AD medication will qualify as an intercurrent event only as long as the medication is considered to have an effect. Symptomatic AD medication is assumed to have no effect when the patient is no longer exposed. Therefore, we suggest to allow for the intercurrent event ‘use of additional AD medication’ to have both a start and a stop date, and, when applying a hypothetical strategy, only measurements that are considered affected by the intercurrent event will be substituted by outcomes corresponding to the hypothetical scenario were additional AD medication has not been initiated.ConclusionUsing estimands clarifies the interpretation of treatment effects in clinical trials allowing usage of additional AD medication.

BackgroundThe Estimands framework, introduced in the Addendum to ICH E9, provides a structured method to define treatment effects in clinical trials. The main novelty of the framework is the discussion of intercurrent events as part of the treatment effect definition. It is widely believed that the application of the framework to non-inferiority and equivalence trials deserves specific consideration.MethodsTo examine the current practices of using the estimand framework in non-inferiority and equivalence trials, we reviewed the scientific advice provided by the European Medicines Agency to drug developers in 2022. This review aimed to determine how often the estimands framework is used by drug developers and/or recommended by EMA and to describe what intercurrent events and handling strategies are being proposed by drug developers and recommended by EMA.ResultsThe use of the framework varied substantially by clinical development phases. While it was used for phase 3 trials in 47% (25/53) by developers, it was used in 5% (1/19) of the phase 1 trials. For 39% (11/28) of the trials where developers did not use the estimands framework in phase 3, there was no regulatory recommendation to adopt the framework in the response. The most discussed intercurrent event in our sample was ‘treatment discontinuation’ (n = 47), for which developers most often proposed either a treatment policy strategy (17/47, 36%) or a hypothetical strategy (11/47, 23%). In contrast, EMA most often recommended the use of two co-primary estimands with two different strategies (22/47, 47%).ConclusionsGenerally, the proposed and recommended strategies depend on the clinical setting and the respective intercurrent event. Developers almost always proposed a single primary estimand, whereas EMA often recommended two co-primary estimands differing in the strategies used to handle some or all the intercurrent events. Further interaction between academia, industry and regulators is necessary to progress the implementation process of the estimands framework for non-inferiority and equivalence trials.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13063-025-09068-2.

Background/Aims:Randomised clinical trials assessing treatment effects on health outcomes (e.g. quality of life) can be affected by data truncation by death, where some patients die before their outcome measure is assessed and their data become undefined after death. The ICH E9(R1) addendum on estimands discusses four strategies for handling such terminal intercurrent events: hypothetical, composite, while-alive, and principal stratum. While the addendum emphasises the importance of aligning statistical methods of analysis (i.e. estimators) with estimands, it does not provide specific guidance and consideration on the choice of estimators in practice. We aim to (1) demonstrate how some statistical methods commonly used in trials can be used to estimate different intercurrent event strategies for handling data truncation by death; and (2) describe how missing outcome data (e.g. due to missed assessments or loss to follow-up) can be handled for each estimator.Method:We use data from SCORAD, a non-inferiority randomised trial comparing single-fraction versus multifraction radiotherapy on ambulatory status at 8 weeks (primary outcome) among patients with spinal canal compression from metastatic cancer. Here, we estimate the effect of radiotherapy on quality of life (secondary outcome), quantified by the difference in mean global health status between the two groups at 8 weeks. We outline the strategies for handling death and describe a selection of commonly used estimators corresponding to each strategy. The handling of missing data is considered and demonstrated as part of the estimation process.Results:The hypothetical strategy, targeting a treatment effect assuming patients had not died, can be estimated using linear mixed models (a likelihood approach) or multiple imputation (a method commonly used for handling missing data). The composite and while-alive strategies relate to the ‘outcome’ attribute of the estimand; the former incorporates death into the definition of the primary outcome, the latter only uses outcome data before death. These can be estimated by re-defining the outcome, for example, assigning a value reflecting poor global health status post-death, or using the last global health status observed before death. The principal stratum strategy, targeting a treatment effect among patients who would not die under either treatment, can be estimated by an analysis of survivors under specific assumptions. Missing data can be handled with linear mixed models or multiple imputation.Conclusions:Regarding death as an intercurrent event in the process of defining the estimand for the trial will help clarify the choice of suitable estimators. When choosing the estimators, it is important to consider the assumptions required by the estimators as well as their plausibility given the setting of the trial.

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called intercurrent events in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions.

BackgroundChronic kidney disease (CKD) is a global public health concern, with 50–70% of the burden attributed to non-diabetic aetiology. To expand CKD research, there is a need to identify novel surrogate endpoints preceding cardiorenal outcomes that are commonly used in CKD trials. This study explored and quantified associations between intercurrent events and clinical outcomes in patients with non-diabetic CKD to inform potential surrogate endpoints.MethodsIn this retrospective observational cohort study, adults with non-diabetic, moderate-to-severe CKD (stage 3/4) were identified in the US Optum Clinformatics® Data Mart healthcare claims database. Key outcomes were hospitalization for heart failure, kidney failure/need for dialysis, and worsening of CKD stage from baseline. Intercurrent events were defined as events observed in patient medical or pharmacy claims after the cohort inclusion date that either precluded a clinical outcome of interest or were associated with a modified risk of the respective outcome. Intercurrent events were selected a priori or by a data-driven exploratory approach. Associations between intercurrent events and clinical outcomes were explored and quantified using a Cox proportional hazards regression model.ResultsThe study cohort included 504,924 patients. An outpatient heart failure diagnosis code was associated with an increased risk of consequent hospitalization for heart failure (hazard ratio [HR]: 12.92, 95% confidence interval [CI]: 12.67–13.17). CKD stage 4 diagnosis code was associated with an increased risk of kidney failure/need for dialysis (HR: 3.75, 95% CI: 3.69–3.81). Dispensation of potassium-removing resins and potassium-binding agents as an intercurrent event was associated with an increased risk of consequent worsening of CKD stage (HR: 4.83, 95% CI: 4.51–5.17). The estimated glomerular filtration rate decline in 295,174 patients with available laboratory data was associated with progressively increased risk of hospitalization for heart failure and kidney failure/need for dialysis.ConclusionsAssociations between intercurrent events and clinical outcomes in patients with non-diabetic CKD were investigated, quantified, and ranked using a large set of routinely collected data from a US claims database. Our approach may help identify novel surrogate endpoints that occur earlier in the disease course and could be leveraged as indicators of clinical outcomes in CKD research.

The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but does not suggest statistical methods for estimation. In this article we focus on the hypothetical strategy, where the treatment effect is defined under the hypothetical scenario in which the intercurrent event is prevented. For its estimation, we consider causal inference and missing data methods. We establish that certain “causal inference estimators” are identical to certain “missing data estimators.” These links may help those familiar with one set of methods but not the other. Moreover, using potential outcome notation allows us to state more clearly the assumptions on which missing data methods rely to estimate hypothetical estimands. This helps to indicate whether estimating a hypothetical estimand is reasonable, and what data should be used in the analysis. We show that hypothetical estimands can be estimated by exploiting data after intercurrent event occurrence, which is typically not used. Supplementary materials for this article are available online.

Some considerations on target estimands for health technology assessment.

Draft ICH E9(R1) Addendum on “Estimands and Sensitivity Analysis in Clinical Trials” provides different strategies for addressing intercurrent events in defining an estimand and describing the treatment effect that is targeted. The set of considered intercurrent events will depend on the specific therapeutic setting and trial objectives. This article considers a case study of a long-term prevention trial investigating the treatment effect of a new drug in asymptomatic subjects who are at risk for developing Alzheimer’s dementia to illustrate the definition of different estimands, which correspond to different scientific questions of interest. The potential intercurrent events are identified. This article shows how the selection of various strategies for intercurrent events translates into different estimators. A simulation investigation is included, which explores the properties of several estimators aligned with estimands that apply a treatment policy strategy for the intercurrent event of treatment discontinuation. Different scenarios are considered for the on-treatment versus the off-treatment mean efficacy trajectory, under a potential range of retrieval rates of off-treatment data. This simulation exercise illustrates how the selection of the estimators for an estimand could have a strong impact on the estimates of the treatment effect and, consequently, on the decision making in a clinical trial.

BackgroundOverall survival is used to assess clinical effectiveness in cancer clinical trials. In practice, it may be influenced by intercurrent events post-randomisation. The decisions made on how to address intercurrent events, change the interpretation of the results.An example is when participants stop their trial intervention and start subsequent anti-cancer interventions (treatment lines) during trial follow-up. At present, there is no evidence on the views of all stakeholders about this intercurrent event or consensus on how it should be addressed. The aim of this work was to understand the perspectives of all stakeholders and to obtain consensus through a qualitative study to guide future methodological work.MethodsA modified Rand/UCLA appropriateness method was implemented. Stakeholder views were collected using an online questionnaire and discussed at a focus group. The questionnaire included items on, the different methods for addressing an intercurrent event, data collection following an intercurrent event, statistical assumptions, and data presentation. Analysis was descriptive incorporating a conventional content approach. Consensus was defined a priori.ResultsOne hundred three stakeholders (30 statisticians or other data analysts, 6 payers or industry partners, 22 healthcare professionals and 45 patient, carer or members of the public) completed the questionnaire between 3/8/2022 and 30/9/2022. Seventy-nine percent of respondents thought it important to consider the potential effect of subsequent treatment lines.Consensus was reached on most questionnaire items. Stakeholders agreed that statistical assumptions were applicable only in “Some Scenarios” and that results should be presented using both a visual and summary measure. The focus group discussed different methods for addressing an intercurrent event and items around data collection where consensus was unclear. Seven participants attended (two patients/carers, one healthcare professional, three statisticians and one payer) with K-LR and PW. Attendees agreed that the treatment policy approach should be considered in future work as it was the most realistic, and that data collection was acceptable with informed consent.DiscussionThis work demonstrates that all stakeholder groups are interested in how subsequent treatment lines may impact overall survival and provides evidence on what future methodological work in the area should consider. The next step of this work will investigate whether it is possible to estimate the overall survival treatment effect in a hypothetical scenario where participants who received second-line therapy all received the same second-line therapy. This will aim to complement the existing treatment policy approach and quantify the impact of subsequent treatments.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13063-025-09148-3.

Inverse probability of censoring weighting is an approach used to estimate the hypothetical treatment effect that would have been observed in a clinical trial if certain intercurrent events had not occurred. Despite the unbiased estimates obtained by inverse probability of censoring weighting when its key assumptions are satisfied, large standard errors and wide confidence intervals can be potential concerns. Inverse probability of censoring weighting with unstabilised weights can be simply implemented by calculating the reciprocal of the probability of being uncensored by the intercurrent events. To improve precision, stabilisation can be realised by replacing the numerator in the unstabilised weights with functions of the time and baseline covariates. Here, we aim to investigate whether stabilised weight is a preferred choice and if so how we should specify the numerator. In a simulation study, we assessed the performance of inverse probability of censoring weighting implementations with unstabilised weights and with different forms of stabilisation when the outcome analysis model was correctly specified or mis-specified. Scenarios were designed to vary the prevalence of the intercurrent event in one or both randomised arms, the existence of a deterministic intercurrent event, the indirect effect through baseline covariates and overall treatment effect, the existence and the pattern of time-varying effect and sample size. Results show that compared with unstabilised weights, stabilisation improves the efficiency of the inverse probability of censoring weighting estimator in most cases and the improvement is obvious when we stabilise for the baseline covariates. However, stabilisation risks increasing the bias when the outcome analysis model is mis-specified.

Handling of Allograft As an Intercurrent Event in Randomized Controlled Trials in Acute Leukemias: A Systematic Review

ABSTRACTSince the first decentralized clinical trial (DCT) was conducted in 2011, there has been an increased usage of DCT due to its benefits of patient‐centricity and generalizability of findings. This trend was further expedited by the global COVID‐19 pandemic. We identified 23 case studies across various therapeutic areas and grouped them into different categories according to their purposes—by necessity, for operational benefits, to address unique research questions, to validate innovative digital endpoints, or to validate decentralization as a clinical research platform. We leveraged the estimand framework from ICH E9(R1) including its five attributes (population, treatment, variable, intercurrent event, and summary measure) to critically assess their design and conduct. Common trends, opportunities, and challenges were reported along with recommendations for future DCT. Of note, intercurrent events and associated handling strategies are largely not present when reporting DCT. This is an area that can impact study conclusions and require more dedicated efforts when designing new DCTs.

The estimand framework proposes different strategies to address intercurrent events. The treatment policy strategy seems to be the most favoured as it is closely aligned with the pre-addendum intention-to-treat principle. All data for all patients should ideally be collected; however, in reality patients may withdraw from a study leading to missing data. This needs to be dealt with as part of the estimation. A common intercurrent event we focus on is treatment discontinuation. Several areas of research have been conducted exploring models to estimate the estimand when intercurrent events are handled using a treatment policy strategy; however, the research is limited for binary endpoints. We explore different retrieved dropout models, where post-intercurrent event, the observed data can be used to multiply impute the missing post-intercurrent event data. We compare our proposed models to a simple imputation model that makes no distinction between the pre- and post-intercurrent event data, and assess varying statistical properties through a simulation study. We then provide an example of how retrieved dropout models were used in practice for Phase 3 clinical trials in rheumatoid arthritis. From the models explored, we conclude that a simple retrieved dropout model including an indicator for whether or not the intercurrent event occurred is the most pragmatic choice. However, at least 50% of observed post-intercurrent event data is required for these models to work well. Therefore, the suitability of implementing this model in practice will depend on the amount of observed post-intercurrent event data available and missing data.

BackgroundPatient-reported outcomes (PROs) play an increasing role in the evaluation of oncology treatments. At the same time, single-arm trials are commonly included in regulatory approval submissions. Because of the high risk of biases, results from single-arm trials require careful interpretation. This benefits from a clearly defined estimand, or target of estimation. In this case study, we demonstrated how the ICH E9 (R1) estimand framework can be implemented in SATs with PRO endpoints.MethodsFor the global quality of life outcome in a real single-arm lung cancer trial, a range of possible estimands was defined. We focused on the choice of the variable of interest and strategies to deal with intercurrent events (death, treatment discontinuation and disease progression). Statistical methods were described for each estimand and the corresponding results on the trial data were shown.ResultsEach intercurrent event handling strategy resulted in its own estimated mean global quality of life over time, with a specific interpretation, suitable for a corresponding clinical research aim. In the setting of this case study, a ‘while alive’ strategy for death and a ‘treatment policy’ strategy for non-terminal intercurrent events were deemed aligned with a descriptive research aim to inform clinicians and patients about expected quality of life after the start of treatment.ConclusionsThe results show that decisions made in the estimand framework are not trivial. Trial results and their interpretation strongly depend on the chosen estimand. The estimand framework provides a structure to match a research question with a clear target of estimation, supporting specific clinical decisions. Adherence to this framework can help improve the quality of data collection, analysis and reporting of PROs in SATs, impacting decision making in clinical practice.