A consumer's guide to subgroup analyses.

A D Oxman; G H Guyatt

doi:10.7326/0003-4819-116-1-78

Home/Archive/PubMed

PubMed · 1530753

A consumer's guide to subgroup analyses.

A D Oxman·G H Guyatt

View original source ↗Export metadata ↓

Abstract

The extent to which a clinician should believe and act on the results of subgroup analyses of data from randomized trials or meta-analyses is controversial. Guidelines are provided in this paper for making these decisions. The strength of inference regarding a proposed difference in treatment effect among subgroups is dependent on the magnitude of the difference, the statistical significance of the difference, whether the hypothesis preceded or followed the analysis, whether the subgroup analysis was one of a small number of hypotheses tested, whether the difference was suggested by comparisons within or between studies, the consistency of the difference, and the existence of indirect evidence that supports the difference. Application of these guidelines will assist clinicians in making decisions regarding whether to base a treatment decision on overall results or on the results of a subgroup analysis.

Explore related subjects

Causality Decision Support Techniques Meta-Analysis as Topic Randomized Controlled Trials as Topic Research Design Journal Article Meta-Analysis Research Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

A D Oxman, G H Guyatt. 1992-01-01. A consumer's guide to subgroup analyses.. https://doi.org/10.7326/0003-4819-116-1-78

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

KEEP EXPLORING

Related citations

PubMed2026

The causal relationship between multiple cardiovascular diseases and glioblastoma: A Mendelian randomization study.

Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P < .05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio < 1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P < 5 × 10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.

Causality↗

PubMed2007

Analysing and interpreting competing risk data.

When competing risks are present, two types of analysis can be performed: modelling the cause specific hazard and modelling the hazard of the subdistribution. This paper contrasts these two methods and presents the benefits of each. The interpretation is specific to the analysis performed. When modelling the cause specific hazard, one performs the analysis under the assumption that the competing risks do not exist. This could be beneficial when, for example, the main interest is whether the treatment works in general. In modelling the hazard of the subdistribution, one incorporates the competing risks in the analysis. This analysis compares the observed incidence of the event of interest between groups. The latter analysis is specific to the structure of the observed data and it can be generalized only to another population with similar competing risks.

Causality↗

PubMed2007

Evaluating candidate agents of selective pressure for cystic fibrosis.

Cystic fibrosis is the most common lethal single-gene mutation in people of European descent, with a carrier frequency upwards of 2%. Based upon molecular research, resistances in the heterozygote to cholera and typhoid fever have been proposed to explain the persistence of the mutation. Using a population genetic model parameterized with historical demographic and epidemiological data, we show that neither cholera nor typhoid fever provided enough historical selective pressure to produce the modern incidence of cystic fibrosis. However, we demonstrate that the European tuberculosis pandemic beginning in the seventeenth century would have provided sufficient historical, geographically appropriate selective pressure under conservative assumptions. Tuberculosis has been underappreciated as a possible selective agent in producing cystic fibrosis but has clinical, molecular and now historical, geographical and epidemiological support. Implications for the future trajectory of cystic fibrosis are discussed. Our result supports the importance of novel investigations into the role of arylsulphatase B deficiency in cystic fibrosis and tuberculosis.

Causality↗