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Related Experiment Video

Updated: Jun 20, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Net efficacy adjusted for risk: further developments.

J Boada1, C Boada, M M Garcia

  • 1University of La Laguna, Tenerife, Spain. boadajuarez@gmail.com

Expert Opinion on Drug Safety
|August 28, 2009
PubMed
Summary
This summary is machine-generated.

Net efficacy adjusted for risk (NEAR) analysis shows that using intention-to-treat (ITT) or per-protocol (PP) populations impacts results. NEAR meta-analyses offer new clinical trial insights, but improved adverse event reporting is needed.

Related Experiment Videos

Last Updated: Jun 20, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Clinical Trials
  • Pharmacoeconomics
  • Biostatistics

Background:

  • Net efficacy adjusted for risk (NEAR) is a quantitative measure integrating drug risk and benefit.
  • It identifies drugs with favorable risk:benefit profiles by maximizing favorable responses while minimizing adverse drug reactions.
  • NEAR estimates are derived from expected frequencies, as direct reporting of this number is rare.

Purpose of the Study:

  • To investigate how using intention-to-treat (ITT) or per-protocol (PP) populations affects NEAR results.
  • To explore the application of NEAR as an effect size in meta-analyses.

Main Methods:

  • Analysis of examples to illustrate differences in NEAR calculations between ITT and PP populations.
  • Demonstration of a meta-analysis utilizing NEAR as the effect size.

Main Results:

  • NEAR results can be optimized by selecting the appropriate population (ITT or PP).
  • Meta-analyses employing NEAR as an effect size yield novel perspectives on clinical trial outcomes.
  • Deficiencies in adverse drug reaction reporting in clinical trials require correction for accurate risk assessment.

Conclusions:

  • The choice of population (ITT or PP) influences NEAR outcomes, highlighting the importance of this consideration.
  • NEAR meta-analyses provide valuable new insights into clinical trial data.
  • Enhanced reporting of adverse drug reactions is crucial for robust clinical trial risk assessment.