Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
| Autor: | Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, Charles Fisher |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Computer Science - Machine Learning Machine Learning (stat.ML) General Medicine Prognosis 01 natural sciences Machine Learning (cs.LG) Methodology (stat.ME) 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Bias Statistics - Machine Learning Sample Size Linear Models Humans Computer Simulation 030212 general & internal medicine 0101 mathematics Statistics Probability and Uncertainty Statistics - Methodology |
| Popis: | Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care that may be exploitable to this end. However, most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control. Here, we propose a use of historical data that exploits linear covariate adjustment to improve the efficiency of trial analyses without incurring bias. Specifically, we train a prognostic model on the historical data, then estimate the treatment effect using a linear regression while adjusting for the trial subjects’ predicted outcomes (their prognostic scores). We prove that, under certain conditions, this prognostic covariate adjustment procedure attains the minimum variance possible among a large class of estimators. When those conditions are not met, prognostic covariate adjustment is still more efficient than raw covariate adjustment and the gain in efficiency is proportional to a measure of the predictive accuracy of the prognostic model above and beyond the linear relationship with the raw covariates. We demonstrate the approach using simulations and a reanalysis of an Alzheimer’s disease clinical trial and observe meaningful reductions in mean-squared error and the estimated variance. Lastly, we provide a simplified formula for asymptotic variance that enables power calculations that account for these gains. Sample size reductions between 10% and 30% are attainable when using prognostic models that explain a clinically realistic percentage of the outcome variance. |
| Databáze: | OpenAIRE |
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