Statistical shortcomings in licensing applications.

J A Lewis; K M Facey

doi:10.1002/(sici)1097-0258(19980815/30)17:15/16<1663::aid-sim969>3.0.co;2-u

Home/Archive/PubMed

PubMed · 9749438

Statistical shortcomings in licensing applications.

J A Lewis·K M Facey

View original source ↗Export metadata ↓

Abstract

This paper concerns the statistical work carried out with respect to clinical trials conducted for regulatory purposes. Although the general quality of such work has improved markedly over recent years and is now generally high, a number of shortcomings remain. A few of these arise from failure to follow well established statistical practice. Rather more arise from a poor understanding of areas of known statistical disagreement and from the unsatisfactory use of newer and more advanced techniques. Inadequacies in reporting statistical work are commonplace. Examples of all these shortcomings are provided and emphasis is placed on the value of a statistical contribution to overall summaries such as the clinical expert report.

Explore related subjects

Clinical Trials, Phase II as Topic Clinical Trials, Phase III as Topic Data Interpretation, Statistical Dose-Response Relationship, Drug Drug Approval Europe Guidelines as Topic Humans Licensure Meta-Analysis as Topic Regression Analysis Reproducibility of Results Research Design Journal Article Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

J A Lewis, K M Facey. Statistical shortcomings in licensing applications.. https://doi.org/10.1002/(sici)1097-0258(19980815%2F30)17%3A15%2F16%3C1663%3A%3Aaid-sim969%3E3.0.co%3B2-u

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Stochastically curtailed phase II clinical trials.

Phase II trials often test the null hypothesis H(0): p or=p(1), where p is the true unknown proportion responding to the new treatment, p(0) is the greatest response proportion which is deemed clinically ineffective, and p(1) is the smallest response proportion which is deemed clinically effective. In order to expose the fewest number of patients to an ineffective therapy, phase II clinical trials should terminate early when the trial fails to produce sufficient evidence of therapeutic activity (i.e. if p or=p(1)), the trial should declare the drug effective in the fewest patients possible to allow for advancement to a phase III comparative trial. Several statistical designs, including Simon's minimax and optimal designs, have been developed that meet these requirements. In this paper, we propose three alternative designs that rely upon stochastic curtailment based on conditional power. We compare and contrast the properties of the three approaches: (1) stochastically curtailed (SC) binomial tests, (2) stochastically curtailed (SC) Simon's optimal design, and (3) SC Simon's minimax design to those of Simon's minimax and Simon's optimal designs. For each of these designs we compare and contrast the number of opportunities for study termination, the expected sample size of the trial under the null hypothesis (p <or=p(0)), and the effective type I and type II errors. We also present graphical tools for monitoring phase II clinical trials with stochastic curtailment using conditional power.

Clinical Trials, Phase II as Topic↗

PubMed2006

Local excision of rectal cancer revisited: ACOSOG protocol Z6041.

Explore the source record for details and available documents.

Clinical Trials, Phase II as Topic↗

PubMed2006

Adaptive two-stage designs in phase II clinical trials.

Two-stage designs have been widely used in phase II clinical trials. Such designs are desirable because they allow a decision to be made on whether a treatment is effective or not after the accumulation of the data at the end of each stage. Optimal fixed two-stage designs, where the sample size at each stage is fixed in advance, were proposed by Simon when the primary outcome is a binary response. This paper proposes an adaptive two-stage design which allows the sample size at the second stage to depend on the results at the first stage. Using a Bayesian decision-theoretic construct, we derive optimal adaptive two-stage designs; the optimality criterion being minimum expected sample size under the null hypothesis. Comparisons are made between Simon's two-stage fixed design and the new design with respect to this optimality criterion.

Clinical Trials, Phase II as Topic↗