Accuracy of time to treatment estimates in the CRASH-3 clinical trial: impact on the trial results

Mansukhani, Raoul; Frimley, Lauren; Shakur-Still, Haleema; Sharples, Linda D.; Roberts, Ian

doi:10.1186/s13063-020-04623-5

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…But an important treatment effect modifier is missing, that is the time between treatment and the trauma. For example Mansukhani et al (2020) details that there is a 10% reduction in treatment effectiveness for every 20-min increase in time to treatment (TTT). In addition TTT is probably shifted between the two populations.…”

Section: General Goal and Problematicmentioning

confidence: 99%

Causal effect on a target population: a sensitivity analysis to handle missing covariates

Colnet¹,

Josse²,

Scornet³

et al. 2021

Preprint

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While a randomized controlled trial (RCT) readily measures the average treatment effect (ATE), this measure may need to be shifted to generalize to a different population. Standard estimators of the target population treatment effect are based on the distributional shift in covariates, using inverse propensity sampling weighting (IPSW) or modeling response with the g-formula. However, these need covariates that are available both in the RCT and in an observational sample, which often qualifies very few of them. Here we analyze how the classic estimators behave when covariates are missing in at least one of the two datasets-RCT or observational. In line with general identifiability conditions, these estimators are consistent when including only treatment effect modifiers that are shifted in the target population. We compute the expected bias induced by a missing covariate, assuming Gaussian covariates and a linear model for the conditional ATE function. This enables sensitivity analysis for each missing covariate pattern. In addition, this method is particularly useful as it gives the sign of the expected bias. We also show that there is no gain imputing a partially-unobserved covariate. Finally we study the replacement of a missing covariate by a proxy, and the impact of imputation. We illustrate all these results on simulations, as well as semi-synthetic benchmarks using data from the Tennessee Student/Teacher Achievement Ratio (STAR), and with a real-world example from the critical care medical domain.

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Section: General Goal and Problematicmentioning

confidence: 99%

Causal effect on a target population: a sensitivity analysis to handle missing covariates

Colnet¹,

Josse²,

Scornet³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Digit preference and biased conclusions in cardiac arrest studies

Lapostolle

Schneider

Agostinucci

et al. 2023

The American Journal of Emergency Medicine

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Effect of early tranexamic acid treatment on fatigue in patients with mild traumatic brain injury: data from the CRASH-3 clinical trial

Mansukhani¹,

Belli²,

Brenner³

et al. 2021

Wellcome Open Res

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Background: Each year world-wide about 65 million people sustain a mild traumatic brain injury (mTBI). Fatigue is a common and distressing symptom after mTBI. We examine the effect of tranexamic acid (TXA) on fatigue in patients with mTBI using data from the CRASH-3 trial. Methods: The CRASH-3 trial randomised 9,202 patients with traumatic brain injury and no significant extracranial bleeding to receive TXA or placebo within 3 hours of injury. The primary outcome was death from head injury within 28 days of injury. The methods and results are presented elsewhere. Fatigue was recorded as “None”, “Moderate” or “Extreme.” This study examines the effect of TXA on extreme fatigue in the 2,632 patients with mTBI (Glasgow Coma Scale [GCS] score≥13). Our analyses were not prespecified. Results: Our study primary outcome, extreme fatigue, was reported for 10 (0.8%) of 1,328 patients receiving TXA and 19 (1.5%) of 1,288 patients receiving placebo (risk ratio [RR]=0.51, 95% confidence interval [CI] 0.24-1.09). Death within 28 days of injury was reported for 34 (2.6%) of 1,328 patients receiving TXA versus 47 (3.6%) of 1,288 patients receiving placebo (RR=0.70, 95% CI 0.45-1.08). Among patients allocated to TXA, 44 (3.3%) patients either died or reported extreme fatigue versus 66 (5.1%) patients among those allocated to placebo (RR=0.65, 95% CI 0.44-0.94). Conclusions: Early tranexamic acid treatment may reduce fatigue in mTBI patients, but these results need to be confirmed in a larger trial. Registration: ISRCTN (ISRCTN15088122, 19/07/2011), ClinicalTrials.gov (NCT01402882, 26/07/2011), EudraCT (2011-003669-14, 25/07/2011), Pan African Clinical Trial Registry (PACTR20121000441277, 30/10/2012).

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Accuracy of time to treatment estimates in the CRASH-3 clinical trial: impact on the trial results

Cited by 4 publications

References 22 publications

Causal effect on a target population: a sensitivity analysis to handle missing covariates

Causal effect on a target population: a sensitivity analysis to handle missing covariates

Digit preference and biased conclusions in cardiac arrest studies

Effect of early tranexamic acid treatment on fatigue in patients with mild traumatic brain injury: data from the CRASH-3 clinical trial

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