A matching algorithm to address imbalances in study populations: application to the National Institute of Neurological Diseases and Stroke Recombinant Tissue Plasminogen Activator acute stroke trial.
OBJECTIVE
Outcome from stroke is highly dependent on baseline conditions. Patients with stroke have a wide range of severities, ages, and etiologies and it has proven difficult to achieve randomization of key variables in clinical trials. We present a new post hoc approach to achieve balance among selected variables. To illustrate the approach, we rebalanced the National Institute of Neurological Diseases and Stroke Recombinant Tissue Plasminogen Activator trial, in which the contribution of baseline imbalances continues to be debated.


METHODS
We selected baseline stroke severity (National Institutes of Health Stroke Scale), age, and glucose as matching criteria. The closest matched placebo and treated subjects were identified based on nearness to each other in 3-dimensional Euclidean space. Matching was performed within the quintiles of National Institutes of Health Stroke Scale that have been previously used to assess balance. Subjects who could not be matched were eliminated. Outcomes were assessed using the original specified National Institute of Neurological Diseases and Stroke trial measures.


RESULTS
We successfully matched the 2 arms resulting in nearly identical baseline characteristics and distribution among quintiles. Despite fewer subjects after outlier elimination, the primary outcome measures remained significantly improved. After rebalancing, the magnitude of benefit was reduced by 13% to 23%. Benefit was apparent mostly in the large vessel occlusion subtype.


CONCLUSIONS
This study demonstrated the feasibility of rebalancing individual subjects within a randomized trial. After rebalancing and outlier elimination, recombinant tissue plasminogen activator continued to demonstrate improved outcome. That the apparent treatment effect was reduced suggests that imbalances contributed to the magnitude of the original National Institute of Neurological Diseases and Stroke outcomes. This method could in theory be applied to any data set to find matched subjects for outcome or other analyses.
Mandava
Pitchaiah
P
Michael E. DeBakey VA Medical Center Stroke Program and the Department of Neurology, Baylor College of Medicine, Houston, Texas, USA. pmandava@bcm.tmc.edu
Kalkonde
Yogeshwar V
YV
Rochat
Ryan H
RH
Kent
Thomas A
TA
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005343:Fibrinolytic Agents / Q000627:therapeutic use*; D006801:Humans; D008875:Middle Aged; D055825:National Institute of Neurological Disorders and Stroke (U.S.); D010919:Placebos; D011994:Recombinant Proteins / Q000627:therapeutic use*; D012107:Research Design; D020521:Stroke / Q000188:drug therapy*; D010959:Tissue Plasminogen Activator / Q000627:therapeutic use*; D016896:Treatment Outcome; D014481:United States
D005343:Fibrinolytic Agents; D010919:Placebos; D011994:Recombinant Proteins; D010959:Tissue Plasminogen Activator