PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10643761

Commentary: model building, quantitative testing, and model comparison.

E B Klerman·M E Jewett
View original source ↗Export metadata ↓

Abstract

The final goal is to create mathematical models that are based on our current knowledge of the underlying physiology and that explain all of the experimental data available. To do this, we suggest a consideration of several potential mathematical structures in the formulation of models and the formal comparison of these various structures with other models in the literature. However, when making these comparisons, one must pay careful attention to the systems being modeled and the data sets chosen to represent those systems.

Explore related subjects

Data Interpretation, StatisticalModels, BiologicalModels, TheoreticalNASA Discipline Regulatory PhysiologyNon-NASA CenterCommentJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

E B Klerman, M E Jewett. 1999. Commentary: model building, quantitative testing, and model comparison.. https://doi.org/10.1177/074873099129000993

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

KEEP EXPLORING

Related citations

PubMed2002

Clinical trials in the new millennium.

Since the introduction of the randomized clinical trial (RCT) over 50 years ago, this method has become the corner stone for evaluating new pharmacologic or biologic agents with many disease areas benefiting. There are numerous examples demonstrating beneficial interventions as well as others either not beneficial or harmful. Statistical methodology for clinical trials has grown rapidly. Advances in information technology for data collection have allowed trials to be conducted around the world. Academia, industry and government have worked in partnership to conduct RCTs. Despite these many RCT achievements, the most interesting and challenging era of clinical trials lies ahead of us. With the human genome now sequenced, we face a new set of challenges to transform vast amounts of data into useful information. The post-genomics era will better identify the disease mechanism and thus help design better treatments, and be more selective in screening patients, yielding more efficient clinical trials. For some areas of medicine, such as medical devices, standards of acceptance and regulatory approval are changing. Other areas, such as medical procedures and alternative medicines, are generally not well evaluated and could benefit greatly from a wider use of RCT methodology. As the ICH guidelines facilitate and encourage international clinical trials, the scientific and ethical dimension of conducting trials in Third World countries are raised. For example, Western society's best standard of care may not be available or affordable to these countries as the control Western investigators should not exploit patients in the Third World. Those and many other challenges face us in the decade ahead. It is truly an exciting time with new opportunities for the RCT to contribute to medicine and health care or prevention.

Data Interpretation, Statistical↗
PubMed2002

The European regulatory experience.

This paper discusses four methodological topics that have been a regular source of difficulty and debate in European regulatory work. (i) The increasing use of non-inferiority trials in the development of medicinal products has highlighted several problems. These relate first to the choice of the non-inferiority margin and secondly to the circumstances under which a non-inferiority design is or is not appropriate. (ii) The use of meta-analysis in regulatory applications is still controversial and acceptable uses need to be defined. (iii) Analysis of responders provides a useful insight into the size of treatment benefits but can be misleading, especially when it is impossible to be certain whether or not an individual patient has truly responded to treatment. (iv) The extent of the monitoring of clinical trial procedures and data still distinguishes industry-sponsored trials from other trials: it is not clear that it should. These questions are all equally important for those involved in clinical trial work outside the arena of pharmaceutical development.

Data Interpretation, Statistical↗
PubMed2002

Intention-to-treat meets missing data: implications of alternate strategies for analyzing clinical trials data.

True intention-to-treat analyses are rare in reports of randomized clinical trials. To highlight the complex issues that arise in conducting and interpreting data from intention-to-treat analyses in studies with substantial levels of protocol violation (e.g. attrition, noncompliance, or withdrawal of participants), data from a clinical trial of treatment for cocaine dependence were analyzed using three strategies to manage missing data: Strategy 1 addressed the effectiveness of treatments based on data collected from participants up to the point of dropout. Strategy 2 addressed the effectiveness of treatments based on data from the full intended duration of the protocol including data collected after participant dropout. The third strategy used a more novel approach, which used an intention-to-treat strategy for the full duration of the trial and the full sample, but also evaluated the effect of treatment retention outcomes by including an independent variable to reflect active treatment retention as a time-varying covariate. Conclusions about the relative efficacy of the study treatments varied to some extent depending on the analytic strategy used. These findings suggest that investigators should make every effort to conduct intent-to-treat analyses, but also to make use of multiple analytic strategies to fully understand the effects of the treatments studied. Moreover, regardless of the strategy used, investigators should clearly describe their handling of data from participants who violate the protocol.

Data Interpretation, Statistical↗