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Randomization-based inference in the presence of selection bias
- Source :
- Statistics in medicineReferences. 40(9)
- Publication Year :
- 2020
-
Abstract
- For the analysis of clinical trials, the study participants are usually assumed to be representative sample of a target population. This assumption is rarely fulfilled in clinical trials, and particularly not if the sample size is small. In addition, covariate imbalances may affect the trial. Randomization tests provide a nonparametric analysis method of the treatment effect that does not rely on population-based assumptions. We propose a nonparametric statistical model that yields a formal basis for randomization tests. We adapt the model for the presence of covariate imbalance in the form of selection bias and investigate the effects of bias on the rejection probability of the randomization test using Monte Carlo simulations. Finally, we show that ancillary statistics can be used to control for the influence of bias. We show that covariate imbalance leads to an inflation of the type I error probability. The proposed nonparametric model allows for the use of ancillary statistics that yield an unbiased adjusted randomization test.
- Subjects :
- Statistics and Probability
Epidemiology
Computer science
media_common.quotation_subject
Population
01 natural sciences
010104 statistics & probability
03 medical and health sciences
Random Allocation
0302 clinical medicine
Bias
Statistics
Covariate
Humans
030212 general & internal medicine
0101 mathematics
education
Selection Bias
media_common
Selection bias
education.field_of_study
Models, Statistical
Nonparametric statistics
Statistical model
Sample size determination
Sample Size
Ancillary statistic
Sufficient statistic
Subjects
Details
- ISSN :
- 10970258
- Volume :
- 40
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Statistics in medicineReferences
- Accession number :
- edsair.doi.dedup.....066dcd3ba01a08775720a329d7638b34