Subpopulation Treatment Effect Pattern Plot (STEPP) analysis for continuous, binary and count outcomes
| Autor: | William Barcella, Xin Victoria Wang, Bernard F. Cole, Wai ki Yip, Ann A. Lazar, Marco Bonetti, Richard D. Gelber |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
MEDICINE (ALL)
Data Interpretation 01 natural sciences law.invention 010104 statistics & probability 0302 clinical medicine Randomized controlled trial law Models Statistics Medicine GENERALIZED LINEAR MODEL RANDOMIZED CLINICAL TRIAL SUBGROUP ANALYSIS SUBPOPULATION TREATMENT EFFECT PATTERN PLOT (STEPP) MEDICINE (ALL) PHARMACOLOGY subgroup analysis Randomized Controlled Trials as Topic SUBGROUP ANALYSIS Tumor General Medicine Statistical Treatment Outcome 030220 oncology & carcinogenesis Data Interpretation Statistical Colorectal Neoplasms Adenoma Risk Statistics & Probability Clinical Sciences Generalized linear model Subgroup analysis Plot (graphics) Article 03 medical and health sciences Folic Acid Clinical Research GENERALIZED LINEAR MODEL Biomarkers Tumor Humans Treatment effect 0101 mathematics SUBPOPULATION TREATMENT EFFECT PATTERN PLOT (STEPP) Pharmacology Models Statistical Aspirin business.industry Prevention RANDOMIZED CLINICAL TRIAL randomized clinical trial Subpopulation Treatment Effect Pattern Plot Survival Analysis Good Health and Well Being business Digestive Diseases Biomarkers |
| Zdroj: | Clinical trials (London, England), vol 13, iss 4 |
| Popis: | Background: For the past few decades, randomized clinical trials have provided evidence for effective treatments by comparing several competing therapies. Their successes have led to numerous new therapies to combat many diseases. However, since their conclusions are based on the entire cohort in the trial, the treatment recommendation is for everyone, and may not be the best option for an individual. Medical research is now focusing more on providing personalized care for patients, which requires investigating how patient characteristics, including novel biomarkers, modify the effect of current treatment modalities. This is known as heterogeneity of treatment effects. A better understanding of the interaction between treatment and patient-specific prognostic factors will enable practitioners to expand the availability of tailored therapies, with the ultimate goal of improving patient outcomes. The Subpopulation Treatment Effect Pattern Plot (STEPP) approach was developed to allow researchers to investigate the heterogeneity of treatment effects on survival outcomes across values of a (continuously measured) covariate, such as a biomarker measurement. Methods: Here, we extend the Subpopulation Treatment Effect Pattern Plot approach to continuous, binary, and count outcomes, which can be easily modeled using generalized linear models. With this extension of Subpopulation Treatment Effect Pattern Plot, these additional types of treatment effects within subpopulations defined with respect to a covariate of interest can be estimated, and the statistical significance of any observed heterogeneity of treatment effect can be assessed using permutation tests. The desirable feature that commonly used models are applied to well-defined patient subgroups to estimate treatment effects is retained in this extension. Results: We describe a simulation study to confirm that the proper Type I error rate is maintained when there is no treatment heterogeneity, and a power study to show that the statistics have power to detect treatment heterogeneity under alternative scenarios. As an illustration, we apply the methods to data from the Aspirin/Folate Polyp Prevention Study, a clinical trial evaluating the effect of oral aspirin, folic acid, or both as a chemoprevention agent against colorectal adenomas. The pre-existing R software package stepp has been extended to handle continuous, binary, and count data using Gaussian, Bernoulli, and Poisson models, and it is available on the Comprehensive R Archive Network. Conclusion: The extension of the method and the availability of new software now permit STEPP to be applied to the full range of clinical trial end points. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|