PURPOSE: The ATEMPT trial was designed to determine if treatment with trastuzumab emtansine (T-DM1) caused less toxicity than paclitaxel plus trastuzumab (TH) and yielded clinically acceptable invasive disease-free survival (iDFS) among patients with stage I human epidermal growth factor receptor 2–positive (HER2+) breast cancer (BC). METHODS: Patients with stage I centrally confirmed HER2+ BC were randomly assigned 3:1 to T-DM1 or TH and received T-DM1 3.6 mg/kg IV every 3 weeks for 17 cycles or T 80 mg/m² IV with H once every week × 12 weeks (4 mg/kg load →2 mg/kg), followed by H × 39 weeks (6 mg/kg once every 3 weeks). The co-primary objectives were to compare the incidence of clinically relevant toxicities (CRTs) in patients treated with T-DM1 versus TH and to evaluate iDFS in patients receiving T-DM1. RESULTS: The analysis population includes all 497 patients who initiated protocol therapy (383 T-DM1 and 114 TH). CRTs were experienced by 46% of patients on T-DM1 and 47% of patients on TH ( P = .83). The 3-year iDFS for T-DM1 was 97.8% (95% CI, 96.3 to 99.3), which rejected the null hypothesis ( P < .0001). Serially collected patient-reported outcomes indicated that patients treated with T-DM1 had less neuropathy and alopecia and better work productivity compared with patients on TH. CONCLUSION: Among patients with stage I HER2+ BC, one year of adjuvant T-DM1 was associated with excellent 3-year iDFS, but was not associated with fewer CRT compared with TH.

Purpose: Deviations from proportional hazards (DPHs), which may be more prevalent in the era of precision medicine and immunotherapy, can lead to underpowered trials or misleading conclusions. We used a meta-analytic approach to estimate DPHs across cancer trials, investigate associated factors, and evaluate data-analysis approaches for future trials. Experimental Design: We searched PubMed for phase III trials in breast, lung, prostate, and colorectal cancer published in a preselected list of journals between 2014 and 2016 and extracted individual patient-level data (IPLD) from Kaplan–Meier curves. We re-analyzed IPLD to identify DPHs. Potential efficiency gains, when DPHs were present, of alternative statistical methods relative to standard log-rank based analysis were expressed as sample-size requirements for a fixed power level. Results: From 152 trials, we obtained IPLD on 129,401 patients. Among 304 Kaplan–Meier figures, 75 (24.7%) exhibited evidence of DPHs, including eight of 14 (57%) KM pairs from immunotherapy trials. Trial type [immunotherapy, odds ratio (OR), 4.29; 95% confidence interval (CI), 1.11–16.6], metastatic patient population (OR, 3.18; 95% CI, 1.26–8.05), and non-OS endpoints (OR, 3.23; 95% CI, 1.79–5.88) were associated with DPHs. In immunotherapy trials, alternative statistical approaches allowed for more efficient clinical trials with fewer patients (up to 74% reduction) relative to log-rank testing. Conclusions: DPHs were found in a notable proportion of time-to-event outcomes in published clinical trials in oncology and was more common for immunotherapy trials and non-OS endpoints. Alternative statistical methods, without proportional hazards assumptions, should be considered in the design and analysis of clinical trials when the likelihood of DPHs is high.

We review the class of species sampling models (SSM). In particular, we investigate the relation between the exchangeable partition probability function (EPPF) and the predictive probability function (PPF). It is straightforward to define a PPF from an EPPF, but the converse is not necessarily true. In this paper we introduce the notion of putative PPFs and show novel conditions for a putative PPF to define an EPPF. We show that all possible PPFs in a certain class have to define (unnormalized) probabilities for cluster membership that are linear in cluster size. We give a new necessary and sufficient condition for arbitrary putative PPFs to define an EPPF. Finally, we show posterior inference for a large class of SSMs with a PPF that is not linear in cluster size and discuss a numerical method to derive its PPF.

Human microbiome studies use sequencing technologies to measure the abundance of bacterial species or Operational Taxonomic Units (OTUs) in samples of biological material. Typically the data are organized in contingency tables with OTU counts across heterogeneous biological samples. In the microbial ecology community, ordination methods are frequently used to investigate latent factors or clusters that capture and describe variations of OTU counts across biological samples. It remains important to evaluate how uncertainty in estimates of each biological sample’s microbial distribution propagates to ordination analyses, including visualization of clusters and projections of biological samples on low-dimensional spaces. We propose a Bayesian analysis for dependent distributions to endow frequently used ordinations with estimates of uncertainty. A Bayesian nonparametric prior for dependent normalized random measures is constructed, which is marginally equivalent to the normalized generalized Gamma process, a well-known prior for nonparametric analyses. In our prior, the dependence and similarity between microbial distributions is represented by latent factors that concentrate in a low-dimensional space. We use a shrinkage prior to tune the dimensionality of the latent factors. The resulting posterior samples of model parameters can be used to evaluate uncertainty in analyses routinely applied in microbiome studies. Specifically, by combining them with multivariate data analysis techniques we can visualize credible regions in ecological ordination plots. The characteristics of the proposed model are illustrated through a simulation study and applications in two microbiome datasets. Supplementary materials for this article are available online.

Background: External control (EC) data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of EC data for early stopping decisions in randomized clinical trials (RCTs). Methods: We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a prespecified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) data sets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs. Results: Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM data sets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that do not leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power. Conclusion: Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

We introduce a novel stochastic process that we term the multivariate beta process. The process is defined for modelling-dependent random probabilities and has beta marginal distributions. We use this process to define a probability model for a family of unknown distributions indexed by covariates. The marginal model for each distribution is a Polya tree prior. An important feature of the proposed prior is the easy centring of the nonparametric model around any parametric regression model. We use the model to implement nonparametric inference for survival distributions. The nonparametric model that we introduce can be adopted to extend the support of prior distributions for parametric regression models.