# NEW STATISTICAL METHODS FOR CANCER TREATMENT

> **NIH NIH R01** · DANA-FARBER CANCER INST · 1991 · $117,795

## Abstract

DESCRIPTION (Adapted from Investigator's Abstract):  Non-parametric
statistical methods are commonly used to plan studies and analyze
biomedical data.  A key feature of these methods is the use of asymptotic
theory to derive the approximate permutational distribution of the relevant
test statistic.  Inference based on such asymptotic approximations in not
always satisfactory.  Many data sets do not satisfy the large sample
criteria that are needed for the asymptotic results to hold.  It is thus
desirable to have the option to base inference on the exact permutational
distribution of the test statistic.  In the past, this has not been a
practical option because of the formidable computational difficulties
inherent in such an approach.  Recent spectacular advances in computer
technology, combined with the availability of efficient numerical
algorithms for exact permutational inference, can change the situation.
The main goal of this research project is to develop such numerical
algorithms for five commonly encountered biomedical problems; loglinear
models for categorical data, regression, multivariate models, power and
sample size computations, and empirical assessments of exact conditional
inference.  An interdisciplinary approach will be taken.  Powerful
techniques of network optimization, recursion, Monte-Carlo sampling with
variance reduction, and hashing, from the fields of operations research and
computer science, are to play a prominent role in all the algorithms.  The
investigator states that many new areas for methodological research will be
stimulated by the development of these algorithm and the results will be
useful to practicing statisticians for planning and analyzing cancer
clinical trials, to applied epidemiologists conducting case-control
studies, and to theoretical statisticians needing exact bench-marks against
which to compare their asymptotic results.

## Key facts

- **NIH application ID:** 3170928
- **Project number:** 2R01CA033019-09
- **Recipient organization:** DANA-FARBER CANCER INST
- **SAM.gov UEI:** DPMGH9MG1X67
- **Principal Investigator:** CYRUS R MEHTA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 1991
- **Award amount:** $117,795
- **Award type:** 2
- **Project period:** 1982-07-15 → 1994-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/3170928

## Citation

> US National Institutes of Health, RePORTER application 3170928, NEW STATISTICAL METHODS FOR CANCER TREATMENT (2R01CA033019-09). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/3170928. Licensed CC0.

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