# Challenges in handling allogeneic stem cell transplantation in randomized clinical trials

Roxane Couturier, Loïc Vasseur, Nicolas Boissel, Hervé Dombret, Jérôme Lambert, Sylvie Chevret

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Roxane Couturier, Loïc Vasseur, Nicolas Boissel, Hervé Dombret, Jérôme Lambert, et al. Challenges in handling allogeneic stem cell transplantation in randomized clinical trials. Journal of Clinical Epidemiology, 2026, 191, pp.112132. 10.1016/j.jclinepi.2026.112132. hal-05512201

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COMMENTARY

Challenges in handling allogeneic stem cell transplantation in randomized
clinical trials

Roxane Couturier<sup>a,*</sup>, Loïc Vasseur<sup>a,b</sup>, Nicolas Boissel<sup>a,b</sup>, Hervé Dombret<sup>c</sup>, Jérôme Lambert<sup>a</sup>,
Sylvie Chevret<sup>a,d</sup>

Accepted 2 January 2026; Published online 6 January 2026

**Abstract**

**Background:** In randomized clinical trials (RCTs) for hematological malignancies, patients may undergo allogeneic hematopoietic stem cell transplantation (allo-HSCT) as part of standard clinical pathways. Allo-HSCT is a potentially curative but high-risk procedure performed after randomization and thus constitutes an important intercurrent event that can substantially influence survival outcomes. However, its handling in statistical analyses is not standardized.

**Objective:** To review current statistical methods used to handle postrandomization allo-HSCT as an intercurrent event in RCTs, and to highlight how each method corresponds to a different estimand, reflecting distinct clinical questions.

**Methods:** We reviewed 93 RCTs published between January 1, 2014, and April 1, 2024 that reported survival outcomes with postran-
domization allo-HSCT.

**Results:** Three different statistical methods were employed to estimate the treatment effects: censoring at the time of allo-HSCT (64 analyses), a time-dependent covariate in a Cox model (24 analyses), or ignoring allo-HSCT status (17 analyses). Each method estimates the treatment effect in response to a different clinical question and estimand, with specific assumptions that must be considered when interpreting the results. Censoring corresponds to the “hypothetical” estimand, but its validity requires 2 things: first, that the likelihood of receiving allo-HSCT is similar across treatment arms; and second, that patients who undergo transplantation have a similar prognosis to those who do not. Time-dependent covariate incorporates the effect of allo-HSCT but is not associated with a specific estimand and requires careful interpretation. Ignoring allo-HSCT corresponds to the “treatment policy” strategy, of comparing the treatment strategy, whichever allo-HSCT or not, without additional assumptions.

**Conclusion:** There is no consensus on handling allo-HSCT as an intercurrent event in survival analyses. Censoring, although common, may introduce bias if treatment or prognostic covariates influence allo-HSCT use. The treatment policy estimand should be preferred when allo-HSCT is part of the therapeutic strategy. © 2026 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Keywords:** Allo-HSCT; Censoring; Estimand; ICH E9 (E1); Intercurrent event; Strategy

# 1. Introduction

In hematological malignancies, such as in acute myeloid
leukemia (AML) or acute lymphoblastic leukemia (ALL),
treatment strategies are complex. They often involve

polychemotherapy, sometimes combined with immunother-
apies or targeted agents, administered across different
phases (induction, consolidation, and possibly intensifica-
tion and maintenance). Randomized clinical trials (RCTs)
address specific therapeutic questions of interest within this
overall strategy, such as comparing different drugs, adding
or omitting a drug, and increasing or modifying chemo-
therapy intensity.

Allogeneic hematopoietic stem cell transplantation (al-
lo-HSCT) plays a key role in the treatment strategy of
ALL and AML patients and is usually performed post

Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

E-mail address: roxane-.couturier@inserm.fr (R. Couturier).

<footnote><sup>a</sup>ECSTRRA Team ISRL, INSERM UMR1342, University Paris Cité, Inserm, Paris, France</footnote>

<footnote><sup>b</sup>Adolescent and Young Adult Hematology Unit, Saint Louis University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France</footnote>

<footnote><sup>c</sup>Hematology Department, Saint Louis University Hospital, AP-HP, Paris, France</footnote>

<footnote><sup>d</sup>Institut Universitaire de France, Paris, France</footnote>

<footnote>* Corresponding author. Roxane Couturier ECSTRRA Team IRSL, INSERM UMR1342, University Paris Cité, 1 avenue Claude-Vellefaux, 75010, Paris, France.</footnote>
## What is new?

### Key findings

* No consensus exists on how to handle allogeneic hematopoietic stem cell transplantation (allo-HSCT) as an intercurrent event in the analysis of randomized trials conducted in patients with acute leukemias (acute myeloid leukemia/acute lymphoblastic leukemia).

* The chosen analytical method directly affects the treatment effect estimate (estimand) and the interpretability of trial results.

* Over 60% of trial protocols do not report how allo-HSCT is addressed in the analysis.

* When reported, censoring at HSCT is the most common approach but without specifying the clinical research question.

### What this adds to what is known?

* This study applies the estimand framework (International Council for Harmonization E9 R1) to allo-HSCT, an intercurrent event often inadequately handled in practice.

* It provides a detailed assessment of the assumptions underlying each analytical approach.

* It discusses the impact of each approach on the estimand, clarifying limitations of current practice.

* It translates analytical choices into clinically interpretable questions.

## What is the implication and what should change now?

* Methodological transparency is essential: protocols should explicitly state the estimand and the analytical approach for handling intercurrent events like allo-HSCT.

* Characterizing patients undergoing allo-HSCT is crucial for clinically meaningful interpretation.

* It is essential to distinguish sensitivity analyses (testing the robustness of the primary estimand) and complementary analyses (exploring a different estimand, using alternative handling methods, and providing a distinct interpretation of treatment effect).

* Adopting these practices will enhance the relevance of analyses and strengthen the clinical validity of trial conclusions.

first-line treatment in patients achieving complete remission, although it is often restricted to patients with a high risk of relapse and limited comorbidities [1,2]. When the RCT addresses a therapeutic question prior to transplantation, allo-HSCT is part of the trial follow-up. As an event occurring after the start of the trial intervention, allo-HSCT defines a so-called “intercurrent event” [3]. Allo-HSCT generally reduces relapse risk but increases nonrelapse mortality because of graft-versus-host disease, infections, or endothelial injury [4]. Consequently, allo-HSCT may alter observed event rates, and its handling in the analysis can substantially affect the estimated treatment effect. There is no consensus on the preferred analytical approach, and trialists lack specific guidance in this context.

This methodological review addresses this gap by providing a practical framework to align the clinical question with the appropriate handling of allo-HSCT in the estimand and analysis. While the use of estimands is becoming more widespread, there is still a large gap in our understanding of how they should be implemented in specific clinical areas. We specifically considered allo-HSCT as an intercurrent event, defined as any postrandomization event that may affect the presence or the interpretability of observed outcomes. This is a specific key attribute of the so-called “estimand,” defined by the International Council for Harmonization E9 Statistical Principles for Clinical Trials [3], through 5 key attributes: (1) the target population, (2) which intervention is specifically studied, (3) the outcome of interest (on which the treatment effect is measured), (4) which targeted measure of effect is used (eg, a ratio or a difference), and (5) definition and handling of intercurrent events. Such a setting would allow for connecting the handling of allo-HSCT in the analysis of the trial with regard to the treatment effect. This has been previously considered for other intercurrent events, such as the discontinuation of treatment or initiation of new anticancer therapy in oncology trials [5,6]. We focus here on hematology trials, where allo-HSCT is a specific intercurrent event.

Based on a methodological research of published trials, we first mapped how the allo-HSCT is handled in the analysis of the RCTs in ALL and AML. Then, we aimed to provide an in-depth insight into the measure of treatment effect achieved in each of these situations, along with the underlying assumptions required to ensure its validity. This would inform physicians on how to manage allo-HSCT in response to the key question of interest, which estimand might be most appropriate, and how the analysis should be done based on the chosen estimand.

## 2. Methods

This methodological research was based on a structured PubMed search to identify relevant English-language
randomized controlled trials published between January 1, 2014, and April 1, 2024. PubMed was used for its broad coverage of hematology journals and reproducible MeSH-based searching. Eligible studies included 2-arm randomized trials conducted in ALL or AML, reporting at least 1 time-to-event outcome, and involving allo-HSCT after randomization. Two authors (R.C. and L.V.) independently screened, reviewed, and extracted data using REDCap (https://www.project-redcap.org/); disagreements were resolved by consensus. Extracted data covered article characteristics, study design, baseline features, allo-HSCT information, time-to-event outcomes, and statistical methods accounting for allo-HSCT in primary or sensitivity analyses. Quantitative variables were summarized as median and IQR. Categorical variables were reported as counts and percentages. Comparisons used Fisher's exact test or Wilcoxon rank-sum test. Analyses were conducted using R, version 4.2.1 (https://www.R-project.org/).

# 3. Results

## 3.1. Methodological research

Of 739 identified articles, 556 were excluded based on title and abstract and 90 after full-text review (Supplementary Fig 1). A total of 93 RCTs were analyzed: 71 in AML and 22 in ALL (Supplementary Table 1).

Most were multicenter phase III RCTs (96%, 81%), double blind in 10%, with 1:1 allocation (84%) vs active control (85%); median sample size was 330 (IQR, 186–588). The median proportion of patients undergoing allo-HSCT was 25% (IQR, 9.6–39.9). Indications for allo-HSCT were clearly defined in 46 (49%) articles. Few studies reported the time from inclusion to allo-HSCT (n = 8) or characteristics of transplanted patients (n = 4).

Across the 93 publications, 278 time-to-event outcomes were reported: 75 primary (overall survival in 30) and 203 secondary (Supplementary Table 2). Only 54 (58%) articles detailed how allo-HSCT was analyzed. Reporting was associated with adult RCTs (P = .002), unbalanced arm allocation (P = .02), overall survival as the main outcome (P = .0007), and a higher proportion of allo-HSCT (P = .0003) (Table 1).

Among these 54 trials, 113 allo-HSCTs, related analyses were identified, 35 primary and 78 secondary, using 3 main strategies (Supplementary Table 2): (1) censoring at allo-HSCT (64 analyses from 40 trials), (2) inclusion as a time-dependent covariate (24 analyses from 11 trials), and (3) ignoring allo-HSCT (17 analyses from 10 trials). Other approaches became more frequent in recent years (P = .0009) and in AML studies (P = .008) (Supplementary Table 3). Among the 75 primary time-to-event outcomes, 34 (45%) reported at least 1 sensitivity analysis (37 in total): 62% censored allo-HSCT, 5% ignored it, 19% used a time-dependent variable, and 14%

another method. Sensitivity analyses were more frequent when overall survival was the primary endpoint (19/30; 63%) than for composite outcomes (15/45; 33%) (P = .02) (Supplementary Table 2). No study justified its analytical approach in the statistical methods or discussion.

## 3.2. Methodological insights on the different methods to consider allo-HSCT

Whether the observations that experience allo-HSCT should be analyzed differently from the others, and if so, how to deal with them, depends on the target population of the intervention effect.

### 3.2.1. Censoring observations at the time of allo-HSCT

The most common strategy, reported in 43% (40/93) of the articles, was censoring patients at the time of allo-HSCT. This is probably done in an attempt to remove these events from the therapeutic question of interest and isolate the effect of the treatment under study.

This objective perfectly fits the “hypothetical strategy” estimand, that is, targeting the treatment effect that would be observed in a theoretical population in which no one could receive allo-HSCT. This may be unrealistic, as up to 60% of the population may undergo allo-HSCT [7]. However, simple censoring of patients at the time of allo-HSCT is only valid in this context if 2 main assumptions are met. First, the likelihood of receiving allo-HSCT must be similar across treatment arms; second, patients undergoing transplantation must have a similar prognosis to those who do not (eg, their outcome would have been similar to that of patients without allo-HSCT, had they not received it). This is rather unlikely as patients are obviously selected not only on the basis of baseline information but also on the basis of their general health status at the time of transplantation, which may per se influence their outcome (even if they had not received allo-HSCT). Thus, censoring these patients in standard survival analyses is likely to be informative of the outcome and introduces a selection bias in the estimation of the treatment effect. To avoid this bias, complex counterfactual approaches are necessary, such as Inverse Probability of Censoring Weighting [8] or Rank-Preserving Structural Failure Time (RPSFT) [9], under strong assumptions.

Note that censoring by truncating data at the time of allo-HSCT may appear to estimate the treatment effect while the patients are allo-HSCT free, which could be considered to reach a “while-on-treatment” [10] estimand. In this context, it considers that any event occurring after allo-HSCT is outside the scope of the clinical question of interest. However, this estimand requires very strong assumptions, and particular care is needed if allo-HSCT occurrence differs between treatments being compared, requiring additional information on the time to allo-HSCT. While it has been reported that a competing risk model could be implemented [11], this is a matter of debate
<table><caption>**Table 1.** Comparison of the characteristics of articles and trials included in the review according to the report of considering allo-HSCT in the analysis</caption><thead><tr><th>Study characteristics</th><th>With report (N = 54)</th><th>Without (N = 39)</th><th>P</th></tr></thead><tbody><tr><td>Publication year, median (IQR)</td><td>2018 (2016–2022)</td><td>2019 (2016–2021)</td><td>0.75</td></tr><tr><td>Impact factor</td><td>14.1 (7.4–45.3)</td><td>12.8 (8.2–45.3)</td><td>0.67</td></tr><tr><td>Top 5 journals</td><td></td><td></td><td>0.56</td></tr><tr><td>Journal of Clinical Oncology</td><td>6 (11%)</td><td>10 (26%)</td><td></td></tr><tr><td>Blood</td><td>2 (4%)</td><td>7 (18%)</td><td></td></tr><tr><td>Blood Advances</td><td>2 (4%)</td><td>7 (18%)</td><td></td></tr><tr><td>Hematologica</td><td>4 (7%)</td><td>5 (13%)</td><td></td></tr><tr><td>Leukemia</td><td>5 (9%)</td><td>4 (10%)</td><td></td></tr><tr><td>Continent of first author</td><td></td><td></td><td>0.62</td></tr><tr><td>Europe</td><td>30 (56%)</td><td>19 (49%)</td><td></td></tr><tr><td>America</td><td>18 (33%)</td><td>13 (33%)</td><td></td></tr><tr><td>Other</td><td>6 (11%)</td><td>7 (18%)</td><td></td></tr><tr><td>At least one author is a statistician</td><td>48 (89%)</td><td>33 (85%)</td><td>0.55</td></tr><tr><td>At least one author works in the pharmaceutical industry</td><td>11 (20%)</td><td>6 (15%)</td><td>0.60</td></tr><tr><td>Acute myeloid leukemia</td><td>45 (83%)</td><td>26 (67%)</td><td>0.08</td></tr><tr><td>Adults (≥18)</td><td>49 (91%)</td><td>24 (62%)</td><td><strong>0.002</strong></td></tr><tr><td>Criteria for transplant</td><td></td><td></td><td>0.91</td></tr><tr><td>Clearly defined</td><td>28 (52%)</td><td>18 (46%)</td><td></td></tr><tr><td>At the clinician's discretion</td><td>7 (13%)</td><td>5 (13%)</td><td></td></tr><tr><td>Not specified</td><td>19 (35%)</td><td>16 (41%)</td><td></td></tr><tr><td>Follow-up, median (IQR)</td><td>49.1 (28.0–60.7)</td><td>41.2 (25.2–68.0)</td><td>0.96</td></tr><tr><td>Phase III</td><td>45 (83%)</td><td>30 (78%)</td><td>0.60</td></tr><tr><td>Multicenter</td><td>52 (96%)</td><td>37 (95%)</td><td>1</td></tr><tr><td>Double-blind</td><td>7 (13%)</td><td>2 (5%)</td><td>0.29</td></tr><tr><td>1:1 ratio of allocation</td><td>41 (76%)</td><td>37 (95%)</td><td><strong>0.02</strong></td></tr><tr><td>Comparator</td><td></td><td></td><td>0.45</td></tr><tr><td>Active standard</td><td>44 (81%)</td><td>35 (90%)</td><td></td></tr><tr><td>Another dose of the same treatment</td><td>3 (6%)</td><td>2 (5%)</td><td></td></tr><tr><td>Placebo</td><td>7 (13%)</td><td>2 (5%)</td><td></td></tr><tr><td>Number of time-to-event outcomes, median (IQR)</td><td>3 (2–4)</td><td>3 (2–3)</td><td>0.13</td></tr><tr><td>Main outcome</td><td></td><td></td><td><strong>0.0007</strong></td></tr><tr><td>Overall survival</td><td>24 (42%)</td><td>6 (15%)</td><td></td></tr><tr><td>Other right-censored</td><td>27 (47%)</td><td>18 (45%)</td><td></td></tr><tr><td>Not survival outcome</td><td>6 (11%)</td><td>16 (40%)</td><td></td></tr><tr><td>Analysis on the intent-to-treat population</td><td>35 (65%)</td><td>20 (51%)</td><td>0.21</td></tr><tr><td>Sample size, median (IQR)</td><td>333 (210–576)</td><td>319 (149–597)</td><td>0.79</td></tr><tr><td>Proportion of allo-HSCT, median (IQR)</td><td>29.5 (18.5–41.9)</td><td>10.9 (5.1–26.1)</td><td><strong>0.0003</strong></td></tr></tbody></table>

Significant *P* values (*P*-value < 5%) are indicated in bold.

Allo-HSCT, allogeneic hematopoietic stem cell transplantation.

[5]. Indeed, in the time-to-event context, patients may appear to have a lower cumulative incidence of death simply by receiving allo-HSCT earlier. Nevertheless, we think that the while-on-treatment strategy does not provide the answer to the main question of interest in the setting of allo-HSCT, which refers to the hypothetical framework detailed above.

### 3.2.2. Introducing allo-HSCT as a time-dependent co-variate in the analyses

This approach complicates the interpretation of a treatment effect by providing an “adjusted” treatment effect, that is, for patients with similar values for the adjusted covariates. However, because of the time dependency of the adjusted covariate (the allo-HSCT), the targeted treatment
effect is difficult to interpret, being conditional on an event that is not clearly dated in the course of the trial. Moreover, the temporal sequence of causal relationships is often difficult to disentangle [12]. In particular, if the decision to allo-HSCT may change in relation to some other prognostic characteristics that vary over time, then interpretation is further exposed to time-confounding bias, unless these prognostic variables are also included in the analysis.

No established estimand corresponds to handling allo-HSCT via a time-dependent covariate.

### 3.2.3. Ignoring the allo-HSCT status in the analyses

The third most commonly used approach consists of ignoring the occurrence of allo-HSCT. This provides an estimate of the treatment effect regardless of whether patients received allo-HSCT or not. The corresponding estimand is the “treatment policy strategy” estimand, targeting the effect of treatment decision, regardless of the occurrence of allo-HSCT or not. This strategy is consistent with the intention-to-treat principle, which ensures that comparisons between treatment arms reflect the clinical decisions that would be made in routine practice. This does not introduce any selection bias but provides a global effect of the treatment, integrating the consequences of allo-HSCT as part of the treatment pathway rather than isolating it. Notably, the direct effect of the treatment cannot be disentangled from its indirect effect via allo-HSCT. Nevertheless, this is particularly relevant in pragmatic RCTs, where the aim is to assess the overall effectiveness of a treatment strategy in real-world conditions. It avoids the bias introduced by conditioning on postrandomization events such as allo-HSCT and provides results that are more generalizable to clinical practice.

Besides these 3 mainly used approaches, others were scarcely used, which target other estimands.

### 3.2.4. Stratifying population (allo-HSCT vs non)

Another approach, which was scarcely used, is to stratify the analysis on the allo-HSCT population and report an estimate of treatment effect only in those who actually received allo-HSCT. This was found in only three analyses from three articles. However, defining strata at baseline based on a future event introduces survival/immortal time bias [13] and should be avoided. Instead, stratification can rely on the baseline probability of undergoing allo-HSCT to identify the relevant target population (eg, patients unlikely to receive transplantation). In this setting, the appropriate estimand is the Principal Stratum strategy, which evaluates treatment only in patients for whom allo-HSCT is not expected to occur [14].

### 3.2.5. Incorporating allo-HSCT in a composite outcome

Another approach is to include allo-HSCT in the patient outcome using a composite outcome. This follows a

“composite treatment strategy,” similar to progression-free survival, and assumes that undergoing allo-HSCT represents failure of the tested intervention. Allo-HSCT is therefore treated as part of the endpoint rather than a separate postrandomization event. This avoids censoring bias and provides an overall assessment of treatment efficacy, capturing both survival and the need for additional interventions. This strategy is relevant when allo-HSCT is considered an adverse event or marker of treatment failure, but its interpretability depends on context, as transplantation may also represent a planned step toward long-term remission.

All these issues are summarized in Table 2, with suggestions for appropriate methods of analysis and practical recommendations.

## 4. Discussion

Allo-HSCT is specific to patients with hematological disorders, such as acute leukemias, where it forms part of a complex treatment strategy. In RCTs estimating the effect of a treatment given before allo-HSCT, it is often mistreated in the analyses, with more than 60% of trial protocols that did not even explain the characteristics of these events and how they were taken into account [15].

Our research of AML and ALL trials revealed 2 main findings. First, 65% (180/278) of outcomes lacked justification for the method used to include allo-HSCT [16], limiting interpretability. Second, when reported, censoring at HSCT was the most common approach (64/113; 56%), without specifying the target population. This research confirms the frequent lack of information on allo-HSCT treatment in statistical analyses and argues for a deep insight into the answers provided by these approaches regarding the intervention effect on the trial outcome. Indeed, the way allo-HSCT is considered in the analysis of the intervention effect on the outcome depends on the clinical question and influences the resulting estimate.

We hypothesized that this could be more clearly considered in the setting of estimands. The interest in estimands initially concerned drug development and regulatory assessment. Thus, most of the literature evaluated the extent to which current practice fits within the estimand framework [16,17]. Moreover, most of these works have been published in biostatistical journals, which are poorly disseminated among practitioners. It has mostly been illustrated in chronic diseases with longitudinal outcome measures, such as in major depressive disorder [18], and COVID-19 [19]. In contrast, only 2 recent articles have focused their interest on the oncological setting [5,6].

The standard intent-to-treat (ITT) analysis of the RCT data focuses on estimating the effect of the intervention, without taking into account whether the assigned treatment was actually received and whether other protocol
<table><caption>**Table 2.** Differences in estimands according to the use of allo-HSCT in the analysis of treatment effect, with related issues and practical recommendations</caption><thead><tr><td>Estimand</td><td>Targeted effect</td><td>Analysis</td><td>Issues</td><td>Practical implications</td></tr></thead><tbody><tr><td>Hypothetical</td><td>Effect without allo-HSCT</td><td>The approach consists of censoring observations after allo-HSCT and correcting potential bias using weights or rank-preserving structural failure time</td><td>Complex model and unrealistic population where allo-HSCT does not exist</td><td>Estimates the effect of the initial treatment in an idealized scenario without allo-HSCT. Methodological complexity and unrealistic hypothesis (allo-HSCT is often an important therapeutic option)</td></tr><tr><td>Treatment policy</td><td>Effect whichever allo-HSCT</td><td>The approach consists of ignoring allo-HSCT</td><td>None</td><td>Easy to interpret, estimates the overall effect of treatment in a pragmatic approach. Suitable for clinical decision support. Does not distinguish between the effect of the randomized treatment and that of the allo-HSCT</td></tr><tr><td>While on treatment</td><td>Effect before allo-HSCT</td><td>The approach consists in truncating data after allo-HSCT</td><td>Invalid question</td><td>Requires very strong assumptions that the occurrence of allo-HSCT is independent of treatment, conditional on patient prognostic factors</td></tr><tr><td>Principal stratum</td><td>Effect in patients who would not receive allo-HSCT under either treatment</td><td>Stratify analysis on the population likely not to receive allo-HSCT</td><td>Rely on a prediction model of allo-HSCT decision (at time of randomization)</td><td>Targets a specific subpopulation (patients unlikely to undergo allo-HSCT), but it is highly dependent on the quality of the predictive model. May limit the generalizability of results</td></tr><tr><td>Composite</td><td>Effect on the outcome including allo-HSCT</td><td>Include the occurrence of allo-HSCT in a composite outcome</td><td>May be nonclinically relevant</td><td>Simple and clinically interpretable but loses granularity by combining heterogeneous events and treating equally death and allo-HSCT as “failure.” May not reflect the specific effect of the initial treatment</td></tr></tbody></table>

Allo-HSCT, allogeneic hematopoietic stem cell transplantation.

requirements, such as allo-HSCT, were met in eligible patients. It seeks to answer: What is the effect of treatment assignment? In other words, how does the specific intervention targeted by the RCT, even if allo-HSCT, which is an optional part of the strategy, affect outcomes?

By contrast, treating allo-HSCT observations differently from the rest moves away from the ITT strategy, probably with the desire to measure a more explanatory effect, which is not contaminated by the allo-HSCT effect on the trial outcome. The hypothetical strategy appears to be the most appropriate estimand for answering this question. However, it cannot be achieved through simple data censoring at the time of allo-HSCT; it requires complex statistical techniques. Obviously, each of the alternative

ways of handling these events has its own impact on the measurement of the true benefit of the intervention being evaluated, with implications for the interpretation of the trial results.

In conclusion, whether to distinguish allo-HSCT from the overall treatment strategy depends on the context. Censoring is valid only for estimating the hypothetical effect, assuming treatment does not influence allo-HSCT decisions and patient outcomes. Otherwise, in a standard ITT analysis, ignoring allo-HSCT status is appropriate, possibly complemented by additional analyses to explore the intervention's role when accounting for allo-HSCT. These recommendations may also be applied to other types of intercurrent events in clinical trials.
**CRediT authorship contribution statement**

**Roxane Couturier:** Writing — review & editing, Writing — original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Loïc Vasseur:** Methodology, Data curation, Conceptualization. **Nicolas Boissel:** Writing — review & editing, Conceptualization. **Hervé Dombret:** Writing — review & editing, Conceptualization. **Jérôme Lambert:** Writing — review & editing, Writing — original draft, Validation, Supervision, Methodology, Investigation, Conceptualization. **Sylvie Chevret:** Writing — review & editing, Writing — original draft, Validation, Supervision, Methodology, Investigation, Conceptualization.

**Declaration of competing interest**

The authors have no conflicts of interest to disclose.

**Supplementary data**

Supplementary data related to this article can be found at
https://doi.org/10.1016/j.jclinepi.2026.112132.

**Data availability**

No data was used for the research described in the
article.

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