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Subgroup Analyses in Randomized Controlled Trials: The Need for Risk Stratification in Kidney Transplantation

Wagner, M.; Balk, E. M.; Kent, D. M.; Kasiske, B. L. and Ekberg, Henrik LU (2009) In American Journal of Transplantation 9(10). p.2217-2222
Abstract
Although randomized controlled trials (RCT) are the gold standard for establishing causation in clinical research, their aggregated results can be misleading when applied to individual patients. A treatment may be beneficial in some patients, but its harms may outweigh benefits in others. While conventional one-variable-at-a-time subgroup analyses have well-known limitations, multivariable risk-based analyses can help uncover clinically significant heterogeneity in treatment effects that may be otherwise obscured. Trials in kidney transplantation have yielded the finding that a reduction in acute rejection does not translate into a similar benefit in prolonging graft survival and improving graft function. This paradox might be explained by... (More)
Although randomized controlled trials (RCT) are the gold standard for establishing causation in clinical research, their aggregated results can be misleading when applied to individual patients. A treatment may be beneficial in some patients, but its harms may outweigh benefits in others. While conventional one-variable-at-a-time subgroup analyses have well-known limitations, multivariable risk-based analyses can help uncover clinically significant heterogeneity in treatment effects that may be otherwise obscured. Trials in kidney transplantation have yielded the finding that a reduction in acute rejection does not translate into a similar benefit in prolonging graft survival and improving graft function. This paradox might be explained by the variation in risk for acute rejection among included kidney transplant recipients varying the likelihood of benefit or harm from intense immunosuppressive regimens. Analyses that stratify patients by their immunological risk may resolve these otherwise puzzling results. Reliable risk models should be developed to investigate benefits and harms in rationally designed risk-based subgroups of patients in existing RCT data sets. These risk strata would need to be validated in future prospective clinical trials examining long-term effects on patient and graft survival. This approach may allow better individualized treatment choices for kidney transplant recipients. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Kidney transplantation, prediction models, risk factors, outcomes
in
American Journal of Transplantation
volume
9
issue
10
pages
2217 - 2222
publisher
Wiley-Blackwell
external identifiers
  • wos:000269801000006
  • scopus:70349224483
ISSN
1600-6135
DOI
10.1111/j.1600-6143.2009.02802.x
language
English
LU publication?
yes
id
73ad7e40-a9ff-4485-8f6b-c72391e86bc9 (old id 1492456)
date added to LUP
2009-10-16 16:17:04
date last changed
2017-07-09 03:37:26
@misc{73ad7e40-a9ff-4485-8f6b-c72391e86bc9,
  abstract     = {Although randomized controlled trials (RCT) are the gold standard for establishing causation in clinical research, their aggregated results can be misleading when applied to individual patients. A treatment may be beneficial in some patients, but its harms may outweigh benefits in others. While conventional one-variable-at-a-time subgroup analyses have well-known limitations, multivariable risk-based analyses can help uncover clinically significant heterogeneity in treatment effects that may be otherwise obscured. Trials in kidney transplantation have yielded the finding that a reduction in acute rejection does not translate into a similar benefit in prolonging graft survival and improving graft function. This paradox might be explained by the variation in risk for acute rejection among included kidney transplant recipients varying the likelihood of benefit or harm from intense immunosuppressive regimens. Analyses that stratify patients by their immunological risk may resolve these otherwise puzzling results. Reliable risk models should be developed to investigate benefits and harms in rationally designed risk-based subgroups of patients in existing RCT data sets. These risk strata would need to be validated in future prospective clinical trials examining long-term effects on patient and graft survival. This approach may allow better individualized treatment choices for kidney transplant recipients.},
  author       = {Wagner, M. and Balk, E. M. and Kent, D. M. and Kasiske, B. L. and Ekberg, Henrik},
  issn         = {1600-6135},
  keyword      = {Kidney transplantation,prediction models,risk factors,outcomes},
  language     = {eng},
  number       = {10},
  pages        = {2217--2222},
  publisher    = {Wiley-Blackwell},
  series       = {American Journal of Transplantation},
  title        = {Subgroup Analyses in Randomized Controlled Trials: The Need for Risk Stratification in Kidney Transplantation},
  url          = {http://dx.doi.org/10.1111/j.1600-6143.2009.02802.x},
  volume       = {9},
  year         = {2009},
}