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. Author manuscript; available in PMC: 2014 Jun 1.
Published in final edited form as: Biol Blood Marrow Transplant. 2013 Jan 7;19(6):860–866. doi: 10.1016/j.bbmt.2013.01.003

Clinical Endpoints in Allogeneic Hematopoietic Stem Cell Transplantation Studies: The Cost of Freedom

Haesook T Kim 1, Philippe Armand 2
PMCID: PMC3633734  NIHMSID: NIHMS433635  PMID: 23305679

Abstract

When designing a study for allogeneic hematopoietic stem cell transplantation (HSCT), many choices must be made, including conditioning regimen, stem cell source, and GVHD prevention method. For each of these there are a growing number of options, which can be combined into a bewildering number of possible HSCT protocols. In order to properly interpret the results of a given strategy and compare them to others, it is essential that there be agreement on the definitions and estimation methods of HSCT endpoints. We report a survey of the recent HSCT literature, which confirms the heterogeneity of endpoint definitions and estimation methods used. Unfortunately, this heterogeneity may lead to significant biases in the estimates of key endpoints including non-relapse mortality (NRM), relapse, GVHD, or engraftment. This can preclude adequate comparisons among studies, even though such comparisons are the major tool with which to improve HSCT outcome. In the context of our survey, we discuss some of the statistical issues that arise when dealing with HSCT endpoints and the ramifications of the choice of endpoint definition, when the endpoint occurs in the context of competing risks. Our hope is to generate discussion and motivate a search for consensus among transplanters and statisticians.

INTRODUCTION

Allogeneic hematopoietic stem cell transplantation (HSCT) can deliver a cure for a variety of malignant and non-malignant hematologic disorders; but the simplicity of the desired outcome belies the great complexity of possible HSCT outcomes and their relationships. Cure through HSCT may be achieved through the cytotoxicity of the conditioning regimen or through the graft-versus-tumor (GVT) effect brought about through adoptive immunotherapy [1]. However, both of those effects are also intimately tied to the toxicity of HSCT; an increase in conditioning intensity may be associated with a decreased risk of relapse and graft failure, but also with an increased risk of mortality; and the strength of the GVT effect is closely tied to the risk and severity of graft-versus-host disease (GVHD) and its considerable attendant morbidity and mortality [2–5]. At present, there are many ways to perform HSCT, using various options of myeloablative conditioning, non-myeloablative or reduced intensity conditioning (RIC); and alongside the traditional bone marrow and peripheral blood (PB) sources of stem cells, umbilical cord blood transplantation (UCBT) and haploidentical transplantation. Moreover, there are different methods of GVHD prophylaxis within each of these HSCT types, leading to a very large number of possible HSCT strategies. All of those carry their own distinct pattern of risks and benefits, and their own trade-offs between the related outcomes of relapse, mortality, GVHD and engraftment. In order to optimize HSCT outcome, and to learn how to select the right procedure for the right patients, we must report the results of well-designed retrospective or prospective studies, and compare the outcomes across subgroups within a given study, across arms within a randomized study, or across studies themselves. While randomized trials provide some way to directly compare transplantation strategies over a set of pre-defined endpoints, those studies are challenging to conduct because of the cost and time involved as well as the difficulty of generating adequate sample sizes within single-center or oligo-center studies. Randomized studies in HSCT require extensive planning and large cooperative infrastructures, which cannot easily keep up with the rapid development of new HSCT strategies. Many of the changes in HSCT practice are therefore likely to come from the interpretation of non-randomized studies. Yet, remarkably, there is at present no consensus on how to estimate and report such basic outcomes as engraftment, GVHD or non-relapse mortality (NRM). How can we hope to compare, for example, a study of myeloablative conditioning peripheral blood stem cell transplantation using a new GVHD prevention regimen and a study of UCBT using a new stem cell expansion protocol, which likely differ significantly in risks of graft failure, GVHD, relapse and NRM, if the two studies do not report those outcomes in the same way?

This is the problem that we consider below. We begin with a survey of the recent transplantation literature that contains competing risks data analysis to describe the variability in endpoint definition and reporting. We then use some examples to highlight the challenges and consequences of the choices that must be made when defining an endpoint in the presence of competing risks. Some of those choices have no clearly correct answer, and yet consensus is essential to move forward. We hope that this report can stimulate discussion and motivate a search for such a consensus.

METHODS

We reviewed all allogeneic transplantation papers published in Biology of Blood and Marrow Transplantation, Blood, the Journal of Clinical Oncology, or New England Journal of Medicine between July 2010 and June 2011 that dealt with any of the following HSCT clinical outcomes: engraftment, GVHD, NRM, or relapse. One hundred and sixteen articles met this criterion. Among them, 86 were retrospective analyses and 30 were prospective studies; 65 were single center studies, 19 multi-center but not registry studies, and 32 were multi-center registry studies.

SURVEY RESULTS (Table 1)

Table 1.

Frequencies of clinical endpoints reported and statistical methods used.

Endpoint Multivariable analyses performed
Neutrophil/platelet engraftment: 51
 Cumulative Incidence Reported: 25 Cox modelused: 2
  Competing Risk: death without the engraftment: 14 Competing Risks Regression model used: 4
  Competing Risk: death without the engraftment or relapse/2nd transplant: 2 Performed but method not stated: 1
  Not stated: 9
 Median time to engraftment among engrafted: 16
 Mean time to engraftment among engrafted: 2
 Crude proportion: 19
Acute/chronicGVHD: 93
 Cumulative Incidence Reported: 62 Cox modelused: 18
  Competing Risk: death without GVHD: 26 Competing Risks Regression model used: 6
  Competing Risk: death without GVHD or relapse/2nd transplant: 4 Logistic model: 8
  Competing Risk: death without GVHD, relapse or graft failure: 1 Performed but method not stated: 2
  Competing Risk: death without GVHD or graft rejection: 2
  Not stated: 24
  1-KM used: 5
 Crude proportion: 46
Relapse and NRM: 96
 Cumulative Incidence Reported: 83 Cox modelused: 34
  Competing Risk: relapse for NRM, NRM for relapse: 52 Competing Risks Regression model used: 16
  Competing Risk: relapse/2nd transplant for NRM, NRM for relapse: 1 Performed but method not stated: 3
  Not stated: 23
  1-KM: 8
 Crude proportion: 19
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KM, Kaplan-Meier; GVHD, graft-versus-host disease; NRM, non-relapse mortality. Note: some papers reported both cumulative incidence and crude proportion; therefore, the sum of the 2 exceeds the number of articles within each category.

Relapse and Non-Relapse Mortality

Among the 116 articles in our survey, 96 presented results for relapse and/or NRM. Of these, 83 presented cumulative incidences of these events: 52 considered relapse and NRM as competing risks, 1 considered either relapse or second transplant as the competing risk for NRM, and 23 did not specifically state what the competing event was or what method was used; 8 used 1-KM (the complement of the Kaplan-Meier estimate) to estimate relapse or NRM. In addition, 18 reported crude proportion (Table 1). Of note, some articles reported both crude proportion and cumulative incidence of an event. With respect to multivariable regression analysis, 16 used competing risks regression models [6–7], 34 used cause-specific Cox model [8] for relapse and/or NRM, and 3 did not state which multivariable regression analysis method was used (Table 1). In most papers, the definition of relapse did not explicitly state whether it included initiation of donor lymphocyte infusion (DLI), repeat HSCT, or graft failure.

Graft-versus-Host Disease

The complexity of this topic is reflected in the heterogeneity of the published literature. Among 116 papers reviewed, 93 presented results of acute and/or chronic GVHD. Of these, 62 presented cumulative incidence of GVHD; 26 used the competing risks data analysis with death without GVHD as a competing event, 4 considered death or relapse or second transplant as competing events, 1 considered death or relapse or graft failure as competing events, 2 considered death or graft rejection as competing events, 24 did not state what the competing event was or what method was used, and 5 used 1-KM without consideration of competing risks. Forty-six reported crude proportions (Table 1). Again, some papers reported both cumulative incidences and crude proportions. In addition, 10 papers presented day 100 cumulative incidence rate of acute GVHD after RIC HSCT (even though a substantial number of acute GVHD events occur after 100 days in this setting). For multivariable regression analysis, 6 papers used competing risks regression models [6–7], 18 used cause-specific Cox model, 8 used logistic regression model, and 2 did not state the method (Table 1).

Engraftment

Fifty-one of the reviewed papers presented results of neutrophil and/or platelet engraftment (defined as absolute neutrophil count >0.5×109/L in the first 3 consecutive days and platelet count >20×109/L in the first 7 of consecutive days without transfusion support, respectively). Of these, 25 reported cumulative incidence of engraftment: 14 considered death without engraftment as a competing event, 2 considered death or second transplant or relapse as competing events, and 9 did not state what the competing event was or what method was used. (Table 1). Sixteen presented median time to engraftment, and 2 presented mean time to engraftment among engrafted patients; 19 reported crude proportions. Only a few papers presented multivariable analysis (Table 1). Perhaps motivated by a number of reports on the impact of delayed or non-engraftment on survival or GVHD [9–14], many studies in our survey reported proportion of engraftment by a certain time point. However, there was broad variability on how to define this time point. Three studies reported day 28, 5 reported day 30, 1 reported day 31, 3 reported day 42, 1 reported day 45, 1 reported day 50, 7 reported day 60 and 3 reported day 100 neutrophil engraftment; one study reported day 50, 3 reported day 60, 8 reported day 100, and 1 reported day 180 platelet engraftment. Furthermore, 2 studies of RIC HSCT reported a range of time for neutrophil and platelet engraftment that included 0. Those calculations therefore included patients who did not nadir, and whose time to engraftment was considered to be 0. Because of this, one study reported that the median time to platelet engraftment was much shorter than the median time to neutrophil engraftment.

CONSIDERATIONS WHEN DEFINING AN ENDPOINT

To illustrate the impact of the choice of statistical methods for endpoints with competing risks, we present below a few examples using actual data, and highlight the challenges that arise in statistical analysis of HSCT outcome.

Cumulative Incidence and Competing Events

As shown in our survey, cumulative incidence of an event in the presence of competing risks can be estimated using the Kaplan-Meier (KM) method treating competing events as censored observations or using competing risks method. The difference between these two methods is well documented in the literature [15–17], and there is broad agreement that the KM estimator is not an appropriate choice in the presence of competing risks. However, even under this agreement, several issues are important: to appropriately recognize the presence of competing risks, to appropriately report the results of competing risks analysis, and to properly select the competing risks.

In the case of relapse and NRM, our survey suggests that the majority of studies recognize these two events as competing events and there is broad agreement that a competing risks method should be used to calculate the cumulative incidence. However, for engraftment, this is much less clear and many studies did not use a competing risks framework when reporting this endpoint. Since engraftment is particularly relevant and important in the context of UCBT, where delayed or non-engraftment may be more frequent and relevant to survival [10–14], we consider the example from a UCBT study that compared neutrophil engraftment between 12 patients who received ex-vivo, 16,16-Dimethyl-Prostaglandin E2 (PGE2)-treated double UCBT, and 53 who received PGE2-untreated double UCBT [18]. In the PGE2 cohort, all patients engrafted, whereas in the control cohort, there were two early deaths without neutrophil engraftment at days 20 and 24 post transplant. If the two deaths are included as competing events in the control cohort, the cumulative incidence of neutrophil engraftment at day 42 (an arbitrary time point) is 100% in the PGE2 and 89% in the control cohort (p=0.04) (Figure 1). If the two early deaths in the control cohort are censored and the 1-KM is used to compare two cohorts, the cumulative incidence of neutrophil engraftment at day 42 is 100% in the PGE2 cohort and 94% in the control cohort (p=0.1). Thus, the choice of a statistical method that is driven by the recognition of competing risks yields two very different interpretations of the same data, and judicious choices for analyzing and reporting engraftment will be necessary, especially in UCBT studies.

Figure 1.

Figure 1

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Neutrophil engraftment for 12 patients who received ex-vivo, 16,16-Dimethyl-Prostaglandin E2 (PGE2)-treated double UCBT (PGE2), and 53 who received PGE2-untreated double UCBT (control). Death is the competing event of neutrophil engraftment.

Another point to note here is that when analyzing an endpoint using competing risks methods, it is essential that the cumulative incidences of all competing risks be presented, as shown in Figure 1. For example, it has been suggested that the rates of GVHD are lower with UCBT than with PBSCT [19]. However, UCBT may be associated with an increased risk of early mortality from delayed engraftment or infection [19]. Because patients who die early from infection are removed from the at-risk set for GVHD as uncensored observations in a competing risks analysis, and the probability of developing GVHD for these patients is zero, the rate of GVHD may appear low if there is a high early death rate. Therefore, the benefits and risks of UCBT will only be properly assessed if the incidences of both the event of interest and competing risks are presented in parallel.

Even when it is agreed upon that competing risks should be considered in an endpoint, it is very challenging to agree on what exactly the competing risks should be. Using GVHD as an example, death without GVHD is an easy choice. But what about relapse without GVHD? Many studies have suggested the interdependent relationship (i.e., GVL vs. GVHD) of these two events [1, 20–25]. If relapse precludes subsequent development of GVHD, it should be considered as a competing risk to GVHD. Another issue that arises in reporting GVHD is that the management of post-HSCT relapse often involves immune manipulation through accelerated immunosuppression (IS) taper, which clearly increases the risk of GVHD. Should GVHD incidence occurring after IS taper be counted toward the original transplantation? To illustrate the impact of IS taper on GVHD, Figure 2 presents the cumulative incidence of chronic GVHD with and without considering as GVHD events those that occurred after the IS taper. One-hundred seventy six patients who underwent matched unrelated RIC HSCT between 2006 and 2010 at Dana-Farber Cancer Institute were included. The 2-year cumulative incidence rate of chronic GVHD is 51% (95% CI: 43%, 59%) if chronic GVHD developing after the IS taper is counted, and 42% (95% CI: 34%, 50%) if not counted. This choice must also consider the practical consideration that in larger studies (especially registry studies), information regarding IS taper may not be easily available. A similar controversy may arise when considering DLI performed for graft failure if graft failure is not included in time-to-progression endpoint. While there may not be a definitive answer to this question, consensus is nevertheless possible and important so that this endpoint, like others, may be homogeneously reported.

Figure 2.

Figure 2

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Cumulative incidence of chronic GVHD with and without excluding chronic GVHD incidences occurred after the taper of immunosuppression among 176 patients who underwent matched unrelated RIC HSCT.

Multivariable Analysis for Competing Risks Data

Multivariable regression analysis is very useful for identifying potential prognostic factors or for assessing a prognostic factor of interest after adjusting for other prognostic factors [17]. If the sample size permits, multivariable regression analysis allows one to examine whether an apparent difference between two cumulative incidences may be due to confounding factors.

In our survey, two types of regression methods were used for multivariable analysis of competing risks data: the Cox model and a competing risks regression model [6–8]. The difference between these two models has been extensively reviewed elsewhere [6–7, 17, 26–27]. Briefly, the Cox model tests the effects of covariates on a cause-specific hazard (e.g., relapse-specific hazard) treating the competing events (e.g., NRM) as censored observations, whereas the competing risks regression models [6–7] test the effects of covariates on the cumulative incidence of an event directly. Cause-specific hazard is the probability of failure due to a specific cause at an instantaneous time, given that no failure has occurred up until that time. Cumulative incidence is the cumulative probability of an event over time in the presence of competing events. Thus testing covariate effects on cause-specific hazard is different from testing their effects on the cumulative incidence of an event directly in the presence of competing events. The difference between the two approaches is well illustrated in the example shown in Klein and Andersen [7]. Using 1715 patients from the International Bone Marrow Transplant Registry, they compared relapse and NRM between patients with different donor types. If a relapse-specific multivariable Cox model is used, the hazard ratio (HR) of HLA-matched unrelated donor to HLA-identical sibling donor is 1.01 (p=0.94). If a direct regression model on the cumulative incidence of relapse in the presence of the competing risk of NRM is used instead, the HR is 0.69 (p=0.02) using the Klein and Andersen model [7] and 0.73 (p=0.004) using the Fine and Gray model [6], indicating that the use of an HLA-matched unrelated donor is in fact associated with a decreased risk of cumulative incidence of relapse. This difference conforms to the difference seen in the cumulative incidence curves of relapse (Figure 3, adapted from Klein and Andersen [7]), with a 5-year cumulative incidence rate of relapse of approximately 18% for matched unrelated and 25% for matched related donors. Other multivariable regression analysis methods such as additive or multi-state model have also been proposed [27–28], but are beyond the scope of this article.

Figure 3.

Figure 3

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Cumulative incidence of relapse with different donor types among 1715 patients from the International Bone Marrow Transplant Registry between 1985 and 1991 (adapted and reprinted with permission from Klein and Andersen, Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function. Biometrics, 2005;16:223–229. DOI: 10.1111/j.0006-341X.2005.031209.x)

Since the two methods are designed to address different questions, the Cox and competing risks regression models may yield different results, as in the example above. Despite this difference in model formulation between two approaches, our survey suggests that there remains some controversy over which model should be chosen for standard use in the analysis of competing risks data. As in other areas discussed above, there may not be a right and a wrong choice, and practically the two methods often give similar results; yet it is important to understand the consequences of the choice of a tool on the interpretation of data. Further discussion is needed as to which model should be adopted for standard use. To this end, consideration should also be given for other existing models or for development of new models as an alternative.

CONCLUSIONS

Statistical analyses of HSCT outcome face unique challenges because many clinical endpoints depend on GVT and GVHD, two events that are immunologically intertwined but of diametrically opposite clinical consequences. For this reason, competing risks methodology is an essential part of endpoint estimation in HSCT research. However, the choice of the competing events for an endpoint of interest are far from clear and yet will have significant implications on the estimate itself. Our survey highlights the great variability in both endpoint definition and estimation methods in the recent HSCT literature. The most commonly recognized competing risks are relapse and NRM, while engraftment is rarely considered in a competing risks framework. Our findings underscore the need for a consensus approach, much as consensus was needed to develop useful clinical definitions for chronic GVHD [30–32]. Unless such a consensus is reached, comparisons of results across HSCT studies or study arms will remain difficult. It is also critical that, even in the absence of consensus, the chosen endpoint definitions and estimation methods be described in enough detail in published studies for their results to be properly interpreted. Our survey suggests that those details are often omitted.

Given the challenges associated with conducting randomized controlled trials in HSCT, and the rapid parallel developments in all aspects of HSCT including conditioning regimen optimization, development of alternative stem cell sources, ex vivo stem cell processing, GVHD prophylaxis, and relapse prevention, we need to be able to compare results across all salient HSCT endpoints, and for this we need a common language. Ultimately, the freedom to define new endpoints may have been an instrument of progress in promoting a better understanding of HSCT and the development of new HSCT techniques; but we may be paying the cost of this freedom if we cannot properly interpret their results.

Acknowledgments

This work was supported by NIAID U19 AI29530, NHLBI PO1 HL070149 and NCI PO1 CA18029. P.A. is a recipient of an American Society of Hematology Scholar Award and an ASCO/Conquer Cancer Foundation Career Development Award. We are deeply indebted to Dr. Mary Horowitz for her critical review, and also gratefully acknowledge the support of Drs. Robert Gray, Robert Soiffer, Joseph Antin, and Jerome Ritz for their valuable comments on the manuscript.

Footnotes

Authorship Contibution

HTK: designed the study, performed the data analysis, wrote the manuscript.

PA: wrote the manuscript.

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