Explore the vast range of titles available.

In clinical trials, postrandomization events, such as treatment discontinuation or the use of rescue medication, can complicate the interpretation of results. An estimand is a precise description of the treatment effect that investigators wish to estimate. Estimands facilitate more straightforward interpretation of trial results by explicitly defining how postrandomization “intercurrent” events are incorporated into the research question. This article introduces the five key attributes of estimands (population, treatment conditions, endpoint, summary measure, and strategies for intercurrent events) and explains the five main strategies for managing intercurrent events (treatment policy, composite, while on treatment, hypothetical, and principal stratum). Using a practical example of a trial comparing cognitive behavioral therapy vs medication for mild anxiety, we demonstrate how different estimand choices lead to varying study designs, analyses, and interpretations. Understanding estimands helps researchers design better trials and enables stakeholders to determine if the results are relevant to their situation. We also explain how sensitivity analyses can be used to check the reliability of results by assessing how results change under different statistical assumptions.

To apply the estimand framework in time to deterioration (TTD) analysis of patient-reported outcomes (PROs), and identify the appropriate statistical methods to deal with intercurrent event (IEs) such as death. Data from phase II randomized trial were used. We estimated TTD using European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 questionnaire with death as the IE, by applying Kaplan–Meier (K.M.) estimator and Cox proportional hazards (PH) model. The Fine–Gray approach was explored, accounting for death as a competing risk. The estimands targeted by the aforementioned methods were defined. We analyzed the data of 64 patients with available questionnaires at baseline. The most notable differences in TTD estimates were observed for deterioration in physical functioning: the hazard ratios were 0.44 [95% CI 0.22–0.90] and 0.62 [95% CI 0.36–1.07] by either ignoring death (31 events) or considering it as deterioration (58 events), respectively (Cox-PH model). When considering death as a competing event (Fine–Gray model), the sub-HRs was 0.51 [95% CI 0.26–1.01]. Depending on the proportion and distribution of deaths occurring before deterioration between arms, the Fine–Gray competing risks model should be considered rather than KM estimator and Cox PH model to reflect the patient's experience of the disease and treatment burden. •Time to deterioration (TTD) of patient-reported outcomes is often used in cancer research.•TTD analyses should appropriately account for intercurrent events (IEs).•The use of IEs to define composite events should be carefully considered.•In case of IEs, the Fine–Gray approach could be recommended to estimate TTD.

Chronic pain trials commonly allow auxiliary pain medications such as rescue and concomitant analgesics in addition to the randomized treatment. Changes in auxiliary pain medications after randomization represent intercurrent events that may affect either the interpretation or the existence of the measurements associated with the clinical question of interest, complicating the assessment of treatment efficacy. In chronic pain trials, pain intensity typically varies and patients may take the auxiliary medications 1 day but not the next or increase and decrease the dosages temporarily while continuing their randomized study medication. This distinctive feature of auxiliary pain medications as an intercurrent event has received little attention in the literature. Further clarifications on how to manage these issues are therefore pressing. Here we provide perspectives on issues related to auxiliary pain medication-related intercurrent events in randomized controlled chronic pain trials considering the strategies suggested in the E9(R1) addendum to the ICH guideline on statistical principles for clinical trials.

Exposure–response analyses are central to dose selection in drug development. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. For simulated example studies inspired by real‐world scenarios, we define dose–response estimands of clinical interest. The estimands are formalized using the potential outcome notation. Assumptions on the setup of the studies and the relation between treatment, exposure and response are expressed as a directed acyclic graph (DAG). The estimand is transformed using the assumption into expressions to identify the estimand based on the observed data. Three types of expressions are obtained. First, a pooled dose–exposure–response (DER) analysis that corresponds to a standard DER analysis as executed for many projects. Second, a pooled, covariate adjusted dose–response (DR) analysis, and third summaries of the outcomes in each randomized cohort. In our example, DER provides more precise estimates than DR as judged by the mean square error (MSE) of repeated simulation estimation. This work advances methodological rigor in DER analyses by integrating with causal inference methodologies and the estimand framework, enabling clearer interpretation of modeling assumptions and results. This has important concrete advantages. We obtain different estimation methods for the same estimand that may be compared to validate them. The potential for bias in the different estimation methods can be formally assessed. The proposed approach provides a generalizable strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies. Study Highlights What is the current knowledge on the topic? ○The estimand framework and exposure–response analyses are central to drug development. Estimands formalize the clinical question of interest. Exposure–response analyses are key for clinical development and dose finding. What question did this study address? ○The estimand framework has the potential to foster better articulation of questions related to dose selection and to promote judicious navigation of related analysis options, including dose–response and dose–exposure–response. We explore this potential by way of a simulated example, defining several estimands that are relevant to support dose selection at the end of Phase 2 (EOP2). What does this study add to our knowledge? ○This work advances exposure–response methodology by framing analyses within the estimand framework and emphasizing causal inference tools to define relevant clinical questions and assess the relative merits of competing estimation methods. The work additionally clarifies the role of covariate adjustment in analyses that leverage data from multiple sources. How might this change drug discovery, development, and/or therapeutics? ○By formally evaluating analysis methodologies that leverage exposure as a mediating variable, we can better inform dose selection, enhance patient outcomes, and advance the science of therapeutic development.

Introduction: In causal inference, the correct formulation of the scientific question of interest is a crucial step. The purpose of this study was to apply causal inference principles to external control analysis using observational data and illustrate the process to define the estimand attributes. Methods: This study compared long-term survival outcomes of a pooled set of three previously reported randomized phase 3 trials studying patients with metastatic non-small cell lung cancer receiving front-line chemotherapy and similar patients treated with front-line chemotherapy as part of routine clinical care. Causal inference frameworks were applied to define the estimand aligned with the research question and select the estimator to estimate the estimand of interest. Results: The estimand attributes of the ideal trial were defined using the estimand framework. The target trial framework was used to address specific issues in defining the estimand attributes using observational data from a nationwide electronic health record-derived de-identified database. The two frameworks combined allow to clearly define the estimand and the aligned estimator while accounting for key baseline confounders, index date, and receipt of subsequent therapies. The hazard ratio estimate (point estimate with 95% confidence interval) comparing the randomized clinical trial pooled control arm with the external control was close to 1, which is indicative of similar survival between the two arms. Discussion: The proposed combined framework provides clarity on the causal contrast of interest and the estimator to adopt, and thus facilitates design and interpretation of the analyses.

Background Although there are discussions regarding standards of the analysis of patient-reported outcomes and quality of life (QOL) in oncology clinical trials, that of QOL with death events is not within their scope. For example, ignoring death can lead to bias in the QOL analysis for patients with moderate or high mortality rates in the palliative care setting. This is discussed in the estimand framework but is controversial. Information loss by summary measures under the estimand framework may make it challenging for clinicians to interpret the QOL analysis results. This study illustrated the use of graphical displays in the framework. They can be helpful for discussions between clinicians and statisticians and decision-making by stakeholders. Methods We reviewed the time-to-deterioration analysis, prioritized composite outcome approach, semi-competing risk analysis, survivor analysis, linear mixed model for repeated measures, and principal stratification approach. We summarized attributes of estimands and graphs in the statistical analysis and evaluated them in various hypothetical randomized controlled trials. Results Graphs for each analysis method provide different information and impressions. In the time-to-deterioration analysis, it was not easy to interpret the difference in the curves as an effect on QOL. The prioritized composite outcome approach provided new insights for QOL considering death by defining better conditions based on the distinction of OS and QOL. The semi-competing risk analysis provided different insights compared with the time-to-deterioration analysis and prioritized composite outcome approach. Due to the missing assumption, graphs by the linear mixed model for repeated measures should be carefully interpreted, even for descriptive purposes. The principal stratification approach provided pure comparison, but the interpretation was difficult because the target population was unknown. Conclusions Graphical displays can capture different aspects of treatment effects that should be described in the estimand framework.

Background The 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. Methods We 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. Results The 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”. Conclusions Our 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.

Background Type 2 diabetes and prediabetes represent significant global health challenges, with physical activity (PA) being essential for disease management and prevention. Despite the well-documented benefits, many individuals with (pre)diabetes remain insufficiently active. General practitioners (GP) provide an accessible platform for delivering interventions; however, integrating PA interventions into routine care is hindered by resource constraints. Objectives The ENERGISED trial aims to address these barriers through an innovative GP-initiated mHealth intervention combining wearable technology and just-in-time adaptive interventions. Methods The ENERGISED trial is a pragmatic, 12-month, multicentre, randomised controlled trial, assessing a GP-initiated mHealth intervention to increase PA and reduce sedentary behaviour in patients with type 2 diabetes and prediabetes. The primary outcome is daily step count, assessed via wrist-worn accelerometry. The primary analysis follows the intention-to-treat principle, using mixed models for repeated measures. Missing data will be handled under the missing-at-random assumption, with sensitivity analyses exploring robustness through reference-based multiple imputation. The trial incorporates the estimand framework to provide transparent and structured treatment effect estimation. Discussion This statistical analysis plan outlines a robust approach to addressing participant non-adherence, protocol violations, and missing data. By adopting the estimand framework and pre-specified sensitivity analyses, the plan ensures methodological rigour while enhancing the interpretability and applicability of results. Conclusions The ENERGISED trial leverages innovative mHealth strategies within primary care to promote PA in individuals with (pre)diabetes. The pre-specified statistical framework provides a comprehensive guide for analysing trial data and contributes to advancing best practices in behavioural intervention trials for public health. Trial registration ClinicalTrials.gov NCT05351359 . Registered on April 28, 2022.

Background Missing data is a common challenge in structured datasets, and numerous methods are available for imputing these missing values. While all of these imputation methods address the issue of incomplete data, it is important to note that some methods perform better than others in terms of their effectiveness. A thorough data analysis can help a researcher identify a given dataset’s most appropriate imputation approach, leading to more reliable analytical results. The primary objective of this study is to develop a conceptual framework that integrates various data imputation methods. Methods This study was conducted in two main steps. First, we defined the conceptual components and their interrelationships by identifying and categorizing primary concepts through a secondary analysis of our previous systematic review, which examined 58 studies to uncover influential factors for selecting optimal imputation methods. Second, we analyzed the implementation process, focusing on the properties of missing values and selecting appropriate imputation techniques while verifying the underlying assumptions according to the estimand framework from the ICH E9(R1) Guideline to ensure unbiased estimates and enhance the credibility of our findings. Results The findings from the secondary analysis suggest that the primary concepts of the developed conceptual framework directly influence the selection of appropriate imputation methods. Conclusions This integrated structure will enable researchers to select the most suitable imputation method based on the specific characteristics and conditions of the dataset under investigation. By employing the appropriate imputation method, the study aims to enhance the overall quality and trustworthiness of the analytical outcomes derived from the research dataset.