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Quantile estimation following non-parametric phase I clinical trials with ordinal response
Ranjan K Paul1, William F Rosenberger, Nancy Flournoy
1Math and Computing Technology, The Boeing Co., P. O. Box 3707, MS 7L-21, Seattle, WA 98124-2207, USA.
Statistics in Medicine
|August 3, 2004
Summary
A new non-parametric isotonic regression estimator improves accuracy and efficiency for estimating toxicity quantiles in small oncology clinical trial datasets. This method outperforms standard estimators, especially with ordinal toxicity data.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Pharmacometrics
Background:
- Phase I oncology clinical trials often generate small datasets with ordinal toxicity responses.
- Existing non-parametric designs for these trials can lead to data limitations.
- Accurate estimation of toxicity quantiles is crucial for dose selection and patient safety.
Purpose of the Study:
- To develop and evaluate a non-parametric multi-dimensional isotonic regression estimator for ordinal toxicity data.
- To compare its performance against standard parametric and other non-parametric estimators in small sample settings.
- To assess its utility within various non-parametric sequential designs for phase I oncology trials.
Main Methods:
- Development of a non-parametric multi-dimensional isotonic regression estimator.
- Comparison with maximum likelihood estimators from proportional odds models.
- Evaluation alongside three non-parametric sequential designs for ordinal response data (two existing, one new random walk rule).
- Comparison with a non-parametric design for binary response trials using dichotomized ordinal data.
Main Results:
- The multi-dimensional isotonic regression estimator demonstrated superior accuracy and efficiency compared to other methods.
- The Simon et al. rule provided efficient estimators but resulted in more dose-limiting toxicities than the random walk rule.
- The proposed estimator proved effective even with extremely small datasets, as shown in a leukemia clinical trial analysis.
Conclusions:
- Non-parametric multi-dimensional isotonic regression offers a robust and accurate approach for estimating toxicity quantiles in early-phase oncology trials.
- This estimator enhances decision-making in sequential trial designs, particularly when dealing with ordinal toxicity data and limited sample sizes.
- The findings support the adoption of advanced non-parametric methods for improved precision and efficiency in clinical trial biostatistics.