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Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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At least 1,171 records · Page 65Linked to original sources
PubMed2002

Statistical methodology of phase III cancer clinical trials: advances and future perspectives.

The methodology for conducting cancer clinical trials has undergone enormous changes over the past 25-30 years since the EORTC Data Center was created. The purpose of this paper is to highlight and to provide a historical perspective for the main methodological concepts, both practical and theoretical, which form the basis for the design and analysis of phase III cancer clinical trials within the EORTC Data Center. Some statistical aspects of other associated topics such as quality of life, health economics, meta-analysis and treatment outcome will also be briefly discussed. Finally, some future perspectives and topics for further statistical methodological research will be presented in order to spur statisticians to meet the challenge of efficiently designing and analysing the clinical trials of tomorrow.

Clinical Trials, Phase III as Topic↗
PubMedDate not supplied

Systematic reviews of interpretive research: interpretive data synthesis of processed data.

This paper discusses the synthesis of qualitative data during the systematic review of interpretive research. Current approaches to data synthesis can be broadly classified as either descriptive or interpretive. Descriptive data synthesis is achieved through narrative and tabulation. While a number of different terms have been used, interpretive synthesis is achieved through processes similar to those used by primary interpretive researchers. However, there has been only limited professional discussion and debate of this issue and existing methods are in need of further investigation and the validity and usefulness of the product of this synthesis examined.

Data Interpretation, Statistical↗
PubMed2005

Nonparametric adjustment techniques for binary covariates.

Though a variety of reasons are often articulated for adjusting analyses for covariates, these reasons often fall into one of two general objectives, specifically to increase precision or to decrease bias. In practice, one does not generally choose between these objectives, because the methods that address one tend to address the other, as well. Because of this, no distinction is made in the methods used to correct for a baseline imbalance with respect to a prognostic covariate versus to ensure a fair comparison across treatment groups by making the comparisons within the levels of a prognostic covariate. Yet the literal translation of these two uses of covariate adjustment will lead to two distinct adjustment methods. We illustrate this divergence in the simplest case of a single binary covariate, a binary outcome, and two treatments, and we note that it is possible to combine the two approaches to derive yet a third approach. Each of these approaches is nonparametric and exact, and so it is the precise reason for adjusting that should dictate which would be used in any given situation.

Analysis of Variance↗
PubMed2005

Maximum likelihood estimation in survival studies under progressive interval censoring with random removals.

Censoring occurs commonly in clinical trials. This article investigates a new censoring scheme, namely, Type II progressive interval censoring with random removals to cope with the setting that patients are examined at fixed regular intervals and dropouts may occur during the study period. We discuss the maximum likelihood estimation of the model parameters and derive the corresponding asymptotic variances when survival times are assumed to be Weibull distributed. An example is discussed to illustrate the application of the results under this censoring scheme.

Data Interpretation, Statistical↗
PubMed2003

Nonparametric comparison of two survival-time distributions in the presence of dependent censoring.

When testing the null hypothesis that treatment arm-specific survival-time distributions are equal, the log-rank test is asymptotically valid when the distribution of time to censoring is conditionally independent of randomized treatment group given survival time. We introduce a test of the null hypothesis for use when the distribution of time to censoring depends on treatment group and survival time. This test does not make any assumptions regarding independence of censoring time and survival time. Asymptotic validity of this test only requires a consistent estimate of the conditional probability that the survival event is observed given both treatment group and that the survival event occurred before the time of analysis. However, by not making unverifiable assumptions about the data-generating mechanism, there exists a set of possible values of corresponding sample-mean estimates of these probabilities that are consistent with the observed data. Over this subset of the unit square, the proposed test can be calculated and a rejection region identified. A decision on the null that considers uncertainty because of censoring that may depend on treatment group and survival time can then be directly made. We also present a generalized log-rank test that enables us to provide conditions under which the ordinary log-rank test is asymptotically valid. This generalized test can also be used for testing the null hypothesis when the distribution of censoring depends on treatment group and survival time. However, use of this test requires semiparametric modeling assumptions. A simulation study and an example using a recent AIDS clinical trial are provided.

Acquired Immunodeficiency Syndrome↗
PubMed1994

Review of nonparametric methods for the analysis of crossover studies.

This paper reviews nonparametric methods for the analysis of crossover studies. Primary attention is given to crossover studies to compare two treatments for a response variable that has a metric measurement level. For this situation, one can often test hypotheses or obtain confidence intervals for parameters of interest by applying well known univariate nonparametric rank methods (e.g., the Wilcoxon rank sum test, or the Wilcoxon signed rank test) to appropriately specified functions of the data. Related extensions are also available, to some degree, for crossover studies to compare more than two treatments or those for which the measurement level of the response variable is ordinal or has a censored time-to-event nature. Methods for several specific situations along these lines are discussed in terms of principles with potentially broader applicability. Several examples are provided to illustrate the performance of some of the methods.

Confidence Intervals↗