The challenge of subgroup analyses--reporting without distorting.

By S. Lagakos. 2006

524 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Statistics and Probability

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Citations in the corpus

Estimating treatment effect heterogeneity in randomized program evaluation (2013, methodology): “However, the most commonly used method, subgroup analysis, is often inappropriate and remains one of the most debated practices in the medical research community [e.g., Rothwell (2005), Lagakos (2006)].”
Consolidated Health Economic Evaluation Reporting Standards (CHEERS)--explanation and elaboration: a report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force. (2013, background): “[48] Lagakos SW.”
Calcium supplements with or without vitamin D and risk of cardiovascular events: reanalysis of the Women’s Health Initiative limited access dataset and meta-analysis (2011, methodology): “The current analysis has some limitations. We used the publicly accessible, limited access dataset of the WHI clinical trials for these analyses, so the analysis is limited to the information available in this dataset. Several vascular end points are potentially evaluable; we pre-specified those whi”
Subgroup Identification and Variable Selection from Randomized Clinical Trial Data. (2013, background): “A well-known risk in subgroup analysis is that of false positives [Yusuf et al., 1991, Peto et al., 1995, Assmann et al., 2000, Brookes et al., 2001, Cui et al., 2002, Pocock et al., 2002, Brookes et al., 2004, Rothwell, 2005, Lagakos, 2006, Wang et al., 2007].”
Subgroup Analysis via Recursive Partitioning (2009, methodology): “As recommended by Lagakos (2006), it is best not to present p-values for within-subgroup comparisons, but rather to give an estimate of the magnitude of the treatment difference.”
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration (2012, background): “However, a significant effect in one group and a nonsignificant effect in the other is not sound evidence that the effect of the marker differs by subgroup [93,94].”
Variable Selection for Qualitative Interactions (2011, background): “Much of the statistical literature suggests that the search for qualitative interactions should be severely limited and qualitative interactions that are found should be initiallymistrusted [9,14,11,16].”
Sample size planning for statistical power and accuracy in parameter estimation. (2008, background): “…on interim analyses (e.g., Jennison & Turnbull 2006, Mehta & Patel 2006), complications in planning subgroup analyses (e.g., Brookes et al. 2004, Lagakos 2006), the impact of noncompliance on power in randomized studies (e.g., Jo 2002), and Bayesian approaches to sample size planning (e.g.,…”
2007 Focused Update of the ACC/AHA 2004 Guidelines for the Management of Patients With ST-Elevation Myocardial Infarction: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (2008, background): “to provide the point estimate and 95% CI of the treatment effect in various subgroups to allow readers to see the range of possible treatment effects.(82) The writing group encourages randomization of additional patients in future trials to clarify a number of questions, such as 1) the benefits of r”