Statistics in medicine--reporting of subgroup analyses in clinical trials.

By Rui Wang, S. Lagakos, J. Ware, David J. Hunter, J. Drazen. 2007

1,405 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability, Medical & Health AI

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Citations in the corpus

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (2017, background): “Individual-level data is critical for certain secondary data analyses (e.g. propensity score matching techniques) and subgroup analyses [1].”
From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer (2012, background): “Several studies have demonstrated how spurious results from subgroup analyses can be easily, if inadvertently, generated [7,12,13].”
Reporting and interpretation of randomized controlled trials with statistically nonsignificant results for primary outcomes. (2010, background): “Sample size, median (IQR) [range] 84 (46-206) [4-6848]”
Overall survival analysis of a phase II randomized controlled trial of a Poxviral-based PSA-targeted immunotherapy in metastatic castration-resistant prostate cancer. (2010, neutral): “Effect modifier analyses were used to assess for lack of homogeneity of the arm effect across the levels of putatively influential factors.(13) Each factor was analyzed separately in dichotomous form, with continuous factors dichotomized at the median, thereby defining the patient subgroups.”
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].”