Biomedical subjects

V W Berger

Publications and source records attributed to V W Berger.

5 recordsLinked to original sources

PubMed2001

Drawbacks to integer scoring for ordered categorical data.

Linear rank tests are widely used when testing for independence against stochastic order in a 2 x J contingency table with two treatments and J ordered outcome levels. For this purpose, numerical scores are assigned, possibly by default, to the J outcome levels. When the choice of scores is not apparent, integer (equally spaced) scores are often assigned. We show that this practice generally leads to unnecessarily conservative tests. The use of slightly perturbed scores will result in a less conservative and uniformly more powerful test.

Biometry↗

PubMed2000

Pros and cons of permutation tests in clinical trials.

Hypothesis testing, in which the null hypothesis specifies no difference between treatment groups, is an important tool in the assessment of new medical interventions. For randomized clinical trials, permutation tests that reflect the actual randomization are design-based analyses for such hypotheses. This means that only such design-based permutation tests can ensure internal validity, without which external validity is irrelevant. However, because of the conservatism of permutation tests, the virtues of permutation tests continue to be debated in the literature, and conclusions are generally of the type that permutation tests should always be used or permutation tests should never be used. A better conclusion might be that there are situations in which permutation tests should be used, and other situations in which permutation tests should not be used. This approach opens the door to broader agreement, but begs the obvious question of when to use permutation tests. We consider this issue from a variety of perspectives, and conclude that permutation tests are ideal to study efficacy in a randomized clinical trial which compares, in a heterogeneous patient population, two or more treatments, each of which may be most effective in some patients, when the primary analysis does not adjust for covariates. We propose the p-value interval as a novel measure of the conservatism of a permutation test that can be defined independently of the significance level. This p-value interval can be used to ensure that the permutation test have both good global power and an acceptable degree of conservatism.

Humans↗

PubMed1999

Detecting selection bias in randomized clinical trials.

Lack of concealment of allocation in randomized clinical trials can invite selection bias, which is the preferential enrollment of specific patients into one treatment group over another. For example, patients more likely to respond may be enrolled only when the next treatment to be assigned is known to be the active treatment, and patients less likely to respond may be enrolled only when the next treatment to be assigned is known to be the control. Despite the fact that selection bias can compromise both the internal and external validity of trials, little methodology has been developed for its detection. An investigator may test the success of the randomization by comparing baseline characteristics across treatment groups, but such test is limited by the potential inability of the measured baseline variables to predict response. A new method for detecting selections bias, based on response data only, is developed for the case in which a small block size, and either unmasking of treatment codes or an open-label design, have compromised the concealment of allocation. This new method complements baseline comparisons, and is sensitive to detect selection bias even in situations in which baseline comparisons are not.

Computer Simulation↗

PubMed1999

Exact inference for growth curves with intraclass correlation structure.

We consider repeated observations taken over time for each of several subjects. For example, one might consider the growth curve of a cohort of babies over time. We assume a simple linear growth curve model. Exact results based on sufficient statistics (exact tests of the null hypothesis that a coefficient is zero, or exact confidence intervals for coefficients) are not available to make inference on regression coefficients when an intraclass correlation structure is assumed. This paper will demonstrate that such exact inference is possible using generalized inference.

Biometry↗

PubMed1998

Convex hull test for ordered categorical data.

When testing for stochastic order in ordered 2 x J contingency tables, it is common to select the cutoff required to declare significance so as to ensure that the size of the test is exactly alpha conditionally on the margins. It is valid, however, to use the margins to select not only the cutoff but also the form of the test. Linear rank tests, which are locally most powerful and frequently used in practice, suffer from the drawback that they may have power as low as zero to detect some alternatives of interest when the margins satisfy certain conditions. The Smirnov and convex hull tests are shown, through exact conditional power calculations and simulations, to avoid this drawback. The convex hull test is also admissible and palindromic invariant and minimizes the required significance level to have limiting power of one as the alternative moves away from the null in any direction.

Antineoplastic Agents↗