TY - JOUR
T1 - Standardization for subgroup analysis in randomized controlled trials
AU - Varadhan, Ravi
AU - Wang, Sue Jane
N1 - Funding Information:
The analyses upon which this publication is based were performed under contract HHSF2232010000072C, entitled “Partnership in Applied Comparative Effectiveness Science,” sponsored by the Food and Drug Administration, Department of Health and Human Services. This research was also supported by the RSR grant 08-48 awarded by the Center for Drug Evaluation and Research, US Food and Drug Administration. Ravi Varadhan is a Brookdale Leadership in Aging Fellow at the Johns Hopkins University.
PY - 2014/1/2
Y1 - 2014/1/2
N2 - Randomized controlled trials (RCTs) emphasize the average or overall effect of a treatment (ATE) on the primary endpoint. Even though the ATE provides the best summary of treatment efficacy, it is of critical importance to know whether the treatment is similarly efficacious in important, predefined subgroups. This is why the RCTs, in addition to the ATE, also present the results of subgroup analysis for preestablished subgroups. Typically, these are marginal subgroup analysis in the sense that treatment effects are estimated in mutually exclusive subgroups defined by only one baseline characteristic at a time (e.g., men versus women, young versus old). Forest plot is a popular graphical approach for displaying the results of subgroup analysis. These plots were originally used in meta-analysis for displaying the treatment effects from independent studies. Treatment effect estimates of different marginal subgroups are, however, not independent. Correlation between the subgrouping variables should be addressed for proper interpretation of forest plots, especially in large effectiveness trials where one of the goals is to address concerns about the generalizability of findings to various populations. Failure to account for the correlation between the subgrouping variables can result in misleading (confounded) interpretations of subgroup effects. Here we present an approach called standardization, a commonly used technique in epidemiology, that allows for valid comparison of subgroup effects depicted in a forest plot. We present simulations results and a subgroup analysis from parallel-group, placebo-controlled randomized trials of antibiotics for acute otitis media.
AB - Randomized controlled trials (RCTs) emphasize the average or overall effect of a treatment (ATE) on the primary endpoint. Even though the ATE provides the best summary of treatment efficacy, it is of critical importance to know whether the treatment is similarly efficacious in important, predefined subgroups. This is why the RCTs, in addition to the ATE, also present the results of subgroup analysis for preestablished subgroups. Typically, these are marginal subgroup analysis in the sense that treatment effects are estimated in mutually exclusive subgroups defined by only one baseline characteristic at a time (e.g., men versus women, young versus old). Forest plot is a popular graphical approach for displaying the results of subgroup analysis. These plots were originally used in meta-analysis for displaying the treatment effects from independent studies. Treatment effect estimates of different marginal subgroups are, however, not independent. Correlation between the subgrouping variables should be addressed for proper interpretation of forest plots, especially in large effectiveness trials where one of the goals is to address concerns about the generalizability of findings to various populations. Failure to account for the correlation between the subgrouping variables can result in misleading (confounded) interpretations of subgroup effects. Here we present an approach called standardization, a commonly used technique in epidemiology, that allows for valid comparison of subgroup effects depicted in a forest plot. We present simulations results and a subgroup analysis from parallel-group, placebo-controlled randomized trials of antibiotics for acute otitis media.
KW - Confounding
KW - Consistency of treatment effect
KW - Forest plot
KW - Heterogeneity of treatment effects
KW - Interaction
KW - Marginal structural model
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U2 - 10.1080/10543406.2013.856023
DO - 10.1080/10543406.2013.856023
M3 - Article
C2 - 24392983
AN - SCOPUS:84891765135
SN - 1054-3406
VL - 24
SP - 154
EP - 167
JO - Journal of biopharmaceutical statistics
JF - Journal of biopharmaceutical statistics
IS - 1
ER -