Unbiased estimation in seamless phase II/III trials with unequal treatment effect variances and hypothesis-driven selection rules

Seamless phase II/III clinical trials offer an efficient way to select an experimental treatment and perform confirmatory analysis within a single trial. However, combining the data from both stages in the final analysis can induce bias into the estimates of treatment effects. Methods for bias adjustment developed thus far have made restrictive assumptions about the design and selection rules followed. In order to address these shortcomings, we apply recent methodological advances to derive the uniformly minimum variance conditionally unbiased estimator for two‐stage seamless phase II/III trials. Our framework allows for the precision of the treatment arm estimates to take arbitrary values, can be utilised for all treatments that are taken forward to phase III and is applicable when the decision to select or drop treatment arms is driven by a multiplicity‐adjusted hypothesis testing procedure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Correcting for bias in the selection and validation of informative diagnostic tests

When developing a new diagnostic test for a disease, there are often multiple candidate classifiers to choose from, and it is unclear if any will offer an improvement in performance compared with current technology. A two-stage design can be used to select a promising classifier (if one exists) in stage one for definitive validation in stage two. However, estimating the true properties of the chosen classifier is complicated by the first stage selection rules. In particular, the usual maximum likelihood estimator (MLE) that combines data from both stages will be biased high. Consequently, confidence intervals and p-values flowing from the MLE will also be incorrect. Building on the results of Pepe et al. (SIM 28:762–779), we derive the most efficient conditionally unbiased estimator and exact confidence intervals for a classifier's sensitivity in a two-stage design with arbitrary selection rules; the condition being that the trial proceeds to the validation stage. We apply our estimation strategy to data from a recent family history screening tool validation study by Walter et al. (BJGP 63:393–400) and are able to identify and successfully adjust for bias in the tool's estimated sensitivity to detect those at higher risk of breast cancer. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Between-arm comparisons in randomized Phase II trials

In a Phase II trial, we may randomize patients to multiple arms of experimental therapies and evaluate their efficacy to determine if any of them is worthy of a large scale Phase III trial. Usually the primary objective of such a study is to identify experimental therapies that are efficacious compared to a historical control. Each arm is independently evaluated using a standard design a for single-arm Phase II trial, e.g., Simon's optimal or minimax design. When more than one arm is accepted through such a randomized trial, we may want to select the winner(s) among them. There are methods for between-arm comparisons in the literature, but most of them have drawbacks. They have a large false selection (type I error) probability when the competing arms have a small difference in efficacy, or the statistical tests used in the selection procedure do not properly reflect the small sample sizes and multi-stage design of the trials. In this paper, we propose between-arm comparison methods for selection in randomized Phase II trials addressing these issues.