Using short-term endpoints to improve interim decision making and trial duration in two-stage phase II trials with nested binary endpoints.

In oncology, phase II clinical trials are often planned as single-arm two-stage designs with a binary endpoint, for example, progression-free survival after 12 months, and the option to stop for futility after the first stage. Simon's two-stage design is a very popular approach but depending on the follow-up time required to measure the patients' outcomes the trial may have to be paused undesirably long. To shorten this forced interruption, it was proposed to use a short-term endpoint for the interim decision, such as progression-free survival after 3 months. We show that if the assumptions for the short-term endpoint are misspecified, the decision-making in the interim can be misleading, resulting in a great loss of statistical power. For the setting of a binary endpoint with nested measurements, such as progression-free survival, we propose two approaches that utilize all available short-term and long-term assessments of the endpoint to guide the interim decision. One approach is based on conditional power and the other is based on Bayesian posterior predictive probability of success. In extensive simulations, we show that both methods perform similarly, when appropriately calibrated, and can greatly improve power compared to the existing approach in settings with slow patient recruitment. Software code to implement the methods is made publicly available.

Two-stage phase II oncology designs using short-term endpoints for early stopping.

Phase II oncology trials are conducted to evaluate whether the tumour activity of a new treatment is promising enough to warrant further investigation. The most commonly used approach in this context is a two-stage single-arm design with binary endpoint. As for all designs with interim analysis, its efficiency strongly depends on the relation between recruitment rate and follow-up time required to measure the patients' outcomes. Usually, recruitment is postponed after the sample size of the first stage is achieved up until the outcomes of all patients are available. This may lead to a considerable increase of the trial length and with it to a delay in the drug development process. We propose a design where an intermediate endpoint is used in the interim analysis to decide whether or not the study is continued with a second stage. Optimal and minimax versions of this design are derived. The characteristics of the proposed design in terms of type I error rate, power, maximum and expected sample size as well as trial duration are investigated. Guidance is given on how to select the most appropriate design. Application is illustrated by a phase II oncology trial in patients with advanced angiosarcoma, which motivated this research.

Curtailment in single-arm two-stage phase II oncology trials.

Two-stage designs that allow for early stopping if the treatment is ineffective are commonly used in phase II oncology trials. A limitation of current designs is that early stopping is only allowed at the end of the first stage, even if it becomes evident during the trial that a significant result is unlikely. One way to overcome this limitation is to implement stochastic curtailment procedures that enable stopping the trial whenever the conditional power is below a pre-specified threshold θ. In this paper, we present the results for implementing curtailment rules in either only the second stage or both stages of the designs. In total, 102 scenarios with different parameter settings were investigated using conditional power thresholds θ between 0 and 1 in steps of 0.01. An increase in θ results not only in a decrease of the actual Type I error rate and power but also of the expected sample size. Therefore, a reasonable balance has to be found when selecting a specific threshold value in the planning phase of a curtailed two-stage design. Given that the effect of curtailment highly depends on the underlying design parameters, no general recommendation for θ can be made. However, up to θ=0.2, the loss in power was less than 5% for all investigated scenarios while savings of up to 50% in expected sample size occurred. In general, curtailment is most appropriate when the outcome can be observed fast or when accrual is slow so that adequate information for making early and frequent decisions is available.

Patient- and provider-related determinants of generic and specific health-related quality of life of patients with chronic systolic heart failure in primary care: a cross-sectional study.

BACKGROUND: Identifying the determinants of health-related quality of life (HRQOL) in patients with systolic heart failure (CHF) is rare in primary care; studies often lack a defined sample, a comprehensive set of variables and clear HRQOL outcomes. Our aim was to explore the impactof such a set of variables on generic and disease-specific HRQOL. METHODS: In a cross-sectional study, we evaluated data from 318 eligible patients. HRQOL measures used were the SF-36 (Physical/Mental Component Summary, PCS/MCS) and four domains of the KCCQ (Functional status, Quality of life, Self efficacy, Social limitation). Potential determinants (instruments) included socio-demographical variables (age, sex, socio-economic status: SES), clinical (e.g. NYHA class, LVEF, NT-proBNP levels, multimorbidity (CIRS-G)), depression (PHQ-9), behavioural (EHFScBs and prescribing) and provider (e.g. list size of and number. of GPs in practice) variables. We performed linear (mixed) regression modelling accounting for clustering. RESULTS: Patients were predominantly male (71.4%), had a mean age of 69.0 (SD: 10.4) years, 12.9% had major depression, according to PHQ-9. Across the final regression models, eleven determinants explained 27% to 55% of variance (frequency across models, lowest/highest ß): Depression (6×, -0.3/-0.7); age (4×, -0.1/-0.2); multimorbidity (4×, 0.1); list size (2×, -0.2); SES (2×, 0.1/0.2); and each of the following once: no. of GPs per practice, NYHA class, COPD, history of CABG surgery, aldosterone antagonist medication and Self-care (0.1/-0.2/-0.2/0.1/-0.1/-0.2). CONCLUSIONS: HRQOL was determined by a variety of established individual variables. Additionally the presence of multimorbidity burden, behavioural (self-care) and provider determinants may influence clinicians in tailoring care to individual patients and highlight future research priorities.