Assessing treatment effect heterogeneity in clinical trials with blocked binary outcomes

This paper addresses treatment effect heterogeneity (also referred to, more compactly, as 'treatment heterogeneity') in the context of a controlled clinical trial with binary endpoints. Treatment heterogeneity, variation in the true (causal) individual treatment effects, is explored using the concept of the potential outcome. This framework supposes the existance of latent responses for each subject corresponding to each possible treatment. In the context of a binary endpoint, treatment heterogeniety may be represented by the parameter, pi2, the probability that an individual would have a failure on the experimental treatment, if received, and would have a success on control, if received. Previous research derived bounds for pi2 based on matched pairs data. The present research extends this method to the blocked data context. Estimates (and their variances) and confidence intervals for the bounds are derived. We apply the new method to data from a renal disease clinical trial. In this example, bounds based on the blocked data are narrower than the corresponding bounds based only on the marginal success proportions. Some remaining challenges (including the possibility of further reducing bound widths) are discussed.     

Assessing exposure-time treatment effect heterogeneity in stepped-wedge cluster randomized trials

A stepped-wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure-time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure-time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure-time periods and may result in more precise average and exposure-time-specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite-sample operating characteristics, and to generate practical guidance on model choices for assessing exposure-time treatment effect heterogeneity in stepped-wedge CRTs.     

Patterns of treatment effects in subsets of patients in clinical trials

We discuss the practice of examining patterns of treatment effects across overlapping patient subpopulations. In particular, we focus on the case in which patient subgroups are defined to contain patients having increasingly larger (or smaller) values of one particular covariate of interest, with the intent of exploring the possible interaction between treatment effect and that covariate. We formalize these subgroup approaches (STEPP: subpopulation treatment effect pattern plots) and implement them when treatment effect is defined as the difference in survival at a fixed time point between two treatment arms. The joint asymptotic distribution of the treatment effect estimates is derived, and used to construct simultaneous confidence bands around the estimates and to test the null hypothesis of no interaction. These methods are illustrated using data from a clinical trial conducted by the International Breast Cancer Study Group, which demonstrates the critical role of estrogen receptor content of the primary breast cancer for selecting appropriate adjuvant therapy. The considerations are also relevant for general subset analysis, since information from the same patients is typically used in the estimation of treatment effects within two or more subgroups of patients defined with respect to different covariates.     

Correcting for heterogeneity and non-comparability bias in multicenter clinical trials with a rescaled random-effect excess hazard model

In the presence of competing causes of event occurrence (e.g., death), the interest might not only be in the overall survival but also in the so-called net survival, that is, the hypothetical survival that would be observed if the disease under study were the only possible cause of death. Net survival estimation is commonly based on the excess hazard approach in which the hazard rate of individuals is assumed to be the sum of a disease-specific and expected hazard rate, supposed to be correctly approximated by the mortality rates obtained from general population life tables. However, this assumption might not be realistic if the study participants are not comparable with the general population. Also, the hierarchical structure of the data can induces a correlation between the outcomes of individuals coming from the same clusters (e.g., hospital, registry). We proposed an excess hazard model that corrects simultaneously for these two sources of bias, instead of dealing with them independently as before. We assessed the performance of this new model and compared it with three similar models, using extensive simulation study, as well as an application to breast cancer data from a multicenter clinical trial. The new model performed better than the others in terms of bias, root mean square error, and empirical coverage rate. The proposed approach might be useful to account simultaneously for the hierarchical structure of the data and the non-comparability bias in studies such as long-term multicenter clinical trials, when there is interest in the estimation of net survival.     

Persistent deficiencies in the measurement and reporting of tobacco use in contemporary genitourinary oncology clinical trials

Smoking is a causal or contributory factor for nearly all genitourinary cancers and exerts significant influence on treatment, quality of life, and survival outcomes. In order to understand the influence smoking has on the outcomes of contemporary therapies, pertinent smoking-related data must be systematically collected and report. We sought to determine how often and how rigorously smoking status is collected and reported in publications of clinical trials in genitourinary cancers by conducting a systematic review. Our initial search yielded 622 articles, 354 of which met criteria. The vast majority of included studies (91.8%) did not report any details about trial participants’ smoking status. When included, 96.3% of studies reported baseline status qualitatively. No studies used a validated measurement instrument or reported change in participants’ smoking status over the study period. Absence of the collection and reporting of smoking-related data precludes further study of how smoking impacts outcomes and highlights an important deficiency in GU oncology clinical trial design.     

Registered clinical trials investigating treatment with cell-derived extracellular vesicles: a scoping review

Background aimsInterest in cell-based therapy using extracellular vesicles (EVs) is intensifying, building upon promising preclinical research and a handful of published clinical studies. Registered clinical trials remain small, heterogeneous in design and underpowered to determine safety and efficacy on their own. A scoping review of registered studies can identify opportunities to pool data and perform meta-analysis.MethodsRegistered trials were identified by searching clinical trial databases (Clinicaltrials.gov, the World Health Organization International Clinical Trials Registry Platform and the Chinese Clinical Trial Registry) on June 10, 2022.ResultsSeventy-three trials were identified and included for analysis. Mesenchymal stromal cells (MSCs) were the most common cell type from which EVs were derived (49 studies, 67%). Among the 49 identified MSC-EV studies, 25 were controlled trials (51%) with a combined total of 3094 participants anticipated to receive MSC-derived EVs (2225 in controlled studies). Although EVs are being administered to treat a broad range of conditions, trials treating patients with coronavirus disease-2019 and/or acute respiratory distress syndrome were observed most commonly. Despite heterogeneity between studies, we anticipate that at least some of the studies could be combined in meaningful meta-analysis and that a combined sample size of 1000 patients would provide the ability to detect a ≥5% difference in mortality with MSC-EVs compared to controls and could be achieved by December 2023.ConclusionsThis scoping review identifies potential barriers that may stall clinical translation of EV-based treatment, and our analysis calls for more standardized product characterization, use of quantifiable product quality attributes and consistent outcome reporting in future clinical trials.     

Vascular effects of aromatase inhibitors: data from clinical trials

Aromatase inhibitors (AIs) are becoming the endocrine treatment of first choice for postmenopausal women with hormone receptor-positive breast cancer and are under investigation for use in breast cancer prevention. AIs reduce circulating estrogen to barely detectable concentrations. It is possible that such a low concentration will be deleterious to the vascular system since estrogen receptors are known to be in the cell walls of blood vessels and estrogen is thought to be important in maintaining blood vessel integrity. Because most women who present with primary breast cancer are cured by surgery and systemic therapy and the major cause of female death is vascular disease, it is particularly important to investigate the vascular side effects of AIs in current breast cancer adjuvant and prevention trials. In order to set the vascular toxicities of AIs reported in the current adjuvant trials into context, here we compare them with the toxicities seen during treatment with hormone replacement therapy (HRT) and selective estrogen receptor modulators (SERMs). Clinical trial evidence indicates that HRT increases risk of coronary heart disease (CHD) whereas SERMs and AIs (to date) appear to be neutral. Cerebrovascular disease and venous thromboembotic events are increased by HRT and SERMs but appear to be unaffected by treatment with AIs. Cognitive function is also considered here since it may also have a vascular component and is potentially a serious potential side effect/benefit of AIs. Recent studies indicate that HRT has a small detrimental effect on cognitive function and is associated with a doubling of the incidence of dementia. A comprehensive study of the SERM, raloxifene, on cognitive function showed no significant effect. There are no definitive reported studies investigating tamoxifen and none for AIs on cognitive function, although there is one in progress in the context of the IBIS II prevention trial which compares anastrozole to placebo in women at high risk. At present concerns about deleterious vascular side effects are confined to HRT and SERMs. However, we have few long-term data using AIs for the treatment and prevention of breast cancer.     

Adaptive treatment assignment methods and clinical trials.

This paper provides a general review of adaptive experimental designs which utilize accumulating information for assigning the best treatment to the most patients in clinical trials. The historical development of such methods is traced. Though the statistical literture on adaptive designs has developed rapidly and continues to grow, the methods are almost totally unused in practice. An extensive evaluation of why adaptive designs are rarely used in clinical trials is presented. It is asserted that most published methods have important deficiencies that render them unsuitable for application. Suggestions are offered for reorienting this area of research into directions that are potentially more useful for clinical trials.