Related Experiment Video
Updated: Jun 12, 2026
08:36
The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
Published on: April 19, 2024
Frequency of treatment-effect modification affecting indirect comparisons: a systematic review
1Cancer Epidemiology Centre, Cancer Council, Carlton, Victoria, Australia. Michael.Coory@cancervic.org.au
Pharmacoeconomics
|June 16, 2010
Summary
Treatment-effect modification occurs in 10-46% of meta-analyses, challenging the similarity assumption in indirect comparisons. Always compare patient and study differences to assess this risk.
Area of Science:
- Medical Statistics
- Evidence Synthesis
- Pharmacoeconomics
Background:
- Indirect comparisons are crucial for treatment evaluation when direct head-to-head trials are absent.
- A core assumption is 'similarity,' meaning no treatment-effect modification across studies.
- Lack of direct evidence on the frequency of treatment-effect modification hinders assumption validation.
Purpose of the Study:
- To systematically review and summarize the frequency of treatment-effect modification in trials.
- To assess the impact of patient characteristics and study methods on treatment effect heterogeneity.
- To inform the critical assumption of similarity in indirect treatment comparisons.
Main Methods:
- Systematic review of MEDLINE and EMBASE databases (inception to November 2009).
- Identified six analyses assessing treatment-effect modification in direct head-to-head meta-analyses.
- Analyzed frequency of modification for ratio measures (odds ratio, relative risk) and risk difference.
Main Results:
- Treatment-effect modification occurred in 10-33% of meta-analyses for ratio measures.
- For risk difference, treatment-effect modification ranged from 15-46%.
- No direct statistical assessment for treatment-effect modification in indirect comparisons, unlike head-to-head meta-analyses.
Conclusions:
- The assumption of similarity in indirect comparisons is not always prudent.
- Ratio measures are not sufficiently robust to justify assuming similarity without further investigation.
- Thorough narrative comparison of patient and study differences is essential for assessing similarity plausibility.
Related Concept Videos
01:15Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
01:12Hazard Ratio
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...
For example, in a clinical trial evaluating a...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:29Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...