Subgroup analysis and other (mis)uses of baseline data in clinical trials.

By S. Assmann, Stuart J. Pocock, L. Enos, L. Kasten. 2000

1,218 citations recorded by the source corpus.

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Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “Subgroup analysis has long been used in medical studies ([Foster et al., 2011]), but it is often subject to criticism due to concerns of multiple testing ([Assmann et al., 2000]).”
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, background): “…heterogeneous treatment eﬀects is the fear that researchers will iteratively search for subgroups with high treatment levels, and then report only the results for subgroups with extreme eﬀects, thus highlighting heterogeneity that may be purely spurious [Assmann et al., 2000, Cook et al., 2004].”
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].”
The Revised CONSORT Statement for Reporting Randomized Trials: Explanation and Elaboration (2001, methodology): “In the mid-1990s, electronic searching of MED- LINE yielded only about half of all RCTs relevant to a topic (64).”
Essential Medical Statistics (2003, background): “In their review, Assmann et al. (2001) found that the use of subgroup analyses is widespread in clinical trials, and often flawed.”
Subgroup analysis in clinical trials (2004, background): “au EBM: Trials on Trial whether there are subgroups of trial participants who a more (or less) likely to be helped (or harmed) by th intervention under investigation, and a recent survey trials published over 3 months in four leading journa found that 70% included subgroup analyses.(1,2) Furthe more”
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): “[44] Assmann SF, Pocock SJ, Enos LE, Kasten LE.”
Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach (2008, background): “However, covariate adjustment has inspired considerable controversy among numerous authors [1, 7, 8, 9] and regulatory authorities [10, 11] because of the potential for biased estimation due to post hoc selection of covariates and, more ominously, the temptation for analysts to engage in a “fishing ”