Skip to main content

Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs.

Publication ,  Journal Article
Naidech, AM; Lawlor, PN; Xu, H; Fonarow, GC; Xian, Y; Smith, EE; Schwamm, L; Matsouaka, R; Prabhakaran, S; Marinescu, I; Kording, KP
Published in: Front Neurol
2020

Randomized Controlled Trials (RCTs) are considered the gold standard for measuring the efficacy of medical interventions. However, RCTs are expensive, and use a limited population. Techniques to estimate the effects of stroke interventions from observational data that minimize confounding would be useful. We used regression discontinuity design (RDD), a technique well-established in economics, on the Get With The Guidelines-Stroke (GWTG-Stroke) data set. RDD, based on regression, measures the occurrence of a discontinuity in an outcome (e.g., odds of home discharge) as a function of an intervention (e.g., alteplase) that becomes significantly more likely when crossing the threshold of a continuous variable that determines that intervention (e.g., time from symptom onset, since alteplase is only given if symptom onset is less than e.g., 3 h). The technique assumes that patients near either side of a threshold (e.g., 2.99 and 3.01 h from symptom onset) are indistinguishable other than the use of the treatment. We compared outcomes of patients whose estimated onset to treatment time fell on either side of the treatment threshold for three cohorts of patients in the GWTG-Stroke data set. This data set spanned three different treatment thresholds for alteplase (3 h, 2003-2007, N = 1,869; 3 h, 2009-2016, N = 13,086, and 4.5 h, 2009-2016, N = 6,550). Patient demographic characteristics were overall similar across the treatment thresholds. We did not find evidence of a discontinuity in clinical outcome at any treatment threshold attributable to alteplase. Potential reasons for failing to find an effect include violation of some RDD assumptions in clinical care, large sample sizes required, or already-well-chosen treatment threshold.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Front Neurol

DOI

ISSN

1664-2295

Publication Date

2020

Volume

11

Start / End Page

961

Location

Switzerland

Related Subject Headings

  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1701 Psychology
  • 1109 Neurosciences
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Naidech, A. M., Lawlor, P. N., Xu, H., Fonarow, G. C., Xian, Y., Smith, E. E., … Kording, K. P. (2020). Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs. Front Neurol, 11, 961. https://doi.org/10.3389/fneur.2020.00961
Naidech, Andrew M., Patrick N. Lawlor, Haolin Xu, Gregg C. Fonarow, Ying Xian, Eric E. Smith, Lee Schwamm, et al. “Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs.Front Neurol 11 (2020): 961. https://doi.org/10.3389/fneur.2020.00961.
Naidech AM, Lawlor PN, Xu H, Fonarow GC, Xian Y, Smith EE, et al. Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs. Front Neurol. 2020;11:961.
Naidech, Andrew M., et al. “Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs.Front Neurol, vol. 11, 2020, p. 961. Pubmed, doi:10.3389/fneur.2020.00961.
Naidech AM, Lawlor PN, Xu H, Fonarow GC, Xian Y, Smith EE, Schwamm L, Matsouaka R, Prabhakaran S, Marinescu I, Kording KP. Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs. Front Neurol. 2020;11:961.

Published In

Front Neurol

DOI

ISSN

1664-2295

Publication Date

2020

Volume

11

Start / End Page

961

Location

Switzerland

Related Subject Headings

  • 5202 Biological psychology
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 1701 Psychology
  • 1109 Neurosciences
  • 1103 Clinical Sciences