PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 8896669

Trough-to-peak ratios: some statistical considerations.

W H Stewart
View original source ↗Export metadata ↓

Abstract

The trough-to-peak ratio has recently come into prominence as a statistic to evaluate the clinical duration of effect for an antihypertensive medication. The statistic is unusual in that it is defined as the ratio of mean drug effect (compared to placebo) at trough compared to that at peak. There is no corresponding measure available on individual patients. This paper will examine the criteria needed to make the trough-to-peak ratio a useful, interpretable statistic. In addition, other possible trough-to-peak measures and statistics are considered for their potential usefulness in describing diurnal blood pressure (BP) data.

Explore related subjects

Antihypertensive AgentsDose-Response Relationship, DrugHumansHypertensionModels, StatisticalJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W H Stewart. 1996. Trough-to-peak ratios: some statistical considerations.. https://doi.org/10.1016/0895-7061(96)00268-3

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

KEEP EXPLORING

Related citations

PubMed2007

Tree-structured gatekeeping tests in clinical trials with hierarchically ordered multiple objectives.

This paper discusses a new class of multiple testing procedures, tree-structured gatekeeping procedures, with clinical trial applications. These procedures arise in clinical trials with hierarchically ordered multiple objectives, for example, in the context of multiple dose-control tests with logical restrictions or analysis of multiple endpoints. The proposed approach is based on the principle of closed testing and generalizes the serial and parallel gatekeeping approaches developed by Westfall and Krishen (J. Statist. Planning Infer. 2001; 99:25-41) and Dmitrienko et al. (Statist. Med. 2003; 22:2387-2400). The proposed testing methodology is illustrated using a clinical trial with multiple endpoints (primary, secondary and tertiary) and multiple objectives (superiority and non-inferiority testing) as well as a dose-finding trial with multiple endpoints.

Antihypertensive Agents↗
PubMed2007

Bayesian analysis of latent variable models with non-ignorable missing outcomes from exponential family.

To provide a comprehensive framework for analysing complex non-normal medical and biological data, we propose a Bayesian approach for a non-linear latent variable model with covariates, and non-ignorable missing data, under the exponential family of distributions. The non-ignorable missing mechanism is defined via a logistic regression model. Based on conjugate prior distributions, full conditional distributions for the implementation of Markov chain Monte Carlo methods in simulating observations from the joint posterior distribution are derived. These observations are used in computing the Bayesian estimates, as well as in implementing a path sampling procedure to evaluate the Bayes factor for model comparison. The proposed methods are illustrated using real data from a study on the non-adherence of hypertension patients.

Antihypertensive Agents↗
PubMed2007

Revisiting the relation between change and initial value: a review and evaluation.

The relation between initial disease status and subsequent change following treatment has attracted great interest in clinical research. However, statisticians have repeatedly warned against correlating/regressing change with baseline due to two methodological concerns known as mathematical coupling and regression to the mean. Oldham's method and Blomqvist's formula are the two most often adopted methods to rectify these problems. The aims of this article are to review briefly the proposed solutions in the statistical and psychological literature, and to clarify the popular misconception that Blomqvist's formula is superior to Oldham's method. We argue that this misconception is due to a failure to recognize that the heterogeneity of individual responses to treatment is a source of regression to the mean in the analysis of the relation between change and initial value. Furthermore, we demonstrate how each method actually answers different research questions, and how confusion arises when this is not always understood.

Antihypertensive Agents↗