摘要:

AbstractEvaluating a clinical trial can be problematic if the studied treatments affect patients in many ways. A possible method for evaluating treatments is to have raters rank all the patients' trial experiences and then test whether the distribution of ranks differ between treatments. Before one can advocate such a procedure, however, one would like to be assured that raters agree fairly well with one another. As a first step in examining whether raters tend to agree, we conducted a small study with 20 raters evaluating 43 trial experiences from an imaginary cardiovascular clinical trial. Raters showed a high degree of consensus. Moreover, the average ranks agreed fairly well with two quantitative ranking rules. On the other hand, the average ranks did not agree very well with weightings usually selected in cardiovascular trials. These results suggest ranking may be a feasible approach to analysing certain clinical trials with multiple outcomes.

ISSN:0277-6715    

DOI:10.1002/sim.4780110402

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780110403

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780110404

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780110405

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780110406

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780110407

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

摘要:

AbstractThe use of time dependent covariates has allowed for incorporation into analysis of survival data intervening events that are binary and non‐reversible (for example, heart transplant, initial response to chemotherapy). We can represent this type of intervening event as a three‐state stochastic process with a starting state (S), an intervening state (I), and an absorbing state (D), which usually represents death. In this paper we present three procedures for calculating survivorship functions which attempt to display the prognostic significance of the time dependent covariate. The first method compares survival from baseline for the two possible paths through the stochastic process; the second method compares overall survival to survival with stateIremoved from the process; and, the third method compares survival for those already in stateIat a landmark timexto those in stateSat timexwho will never enter stateI.We develop discrete hazard estimates for the survival curves associated with the three methods. Two examples illustrate how these methods can yield different results and in which situations one might employ each of the three methods. Extensions to applications with reversible binary time dependent covariates and models with both baseline and time dependent covariates are sugges

ISSN:0277-6715    

DOI:10.1002/sim.4780110408

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

摘要:

AbstractMortality prediction models hold substantial promise as tools for patient management, quality assessment, and, perhaps, health care resource allocation planning. Yet relatively little is known about the predictive validity of these models. We report here a comparison of the cross‐validation performance of seven statistical models of patient mortality: (1) ordinary‐least‐squares (OLS) regression predicting 0/1 death status six months after admission; (2) logistic regression; (3) Cox regression; (4–6) three unit‐weight models derived from the logistic regression, and (7) a recursive partitioning classification technique (CART).We calculated the following performance statistics for each model in both a learning and test sample of patients, all of whom were drawn from a nationally representative sample of 2558 Medicare patients with acute myocardial infarction: overall accuracy in predicting six‐month mortality, sensitivity and specificity rates, positive and negative predictive values, and per cent improvement in accuracy rates and error rates over model‐free predictions (i.e., predictions that make no use of available independent variables). We developed ROC curves based on logistic regression, the best unit‐weight model, the single best predictor variable, and a series of CART models generated by varying the misclassification cost specifications.In our sample, the models reduced model‐free error rates at the patient level by 8–22 per cent in the test sample. We found that the performance of the logistic regression models was marginally superior to that of other models. The areas under the ROC curves for the best models ranged from 0·61 to 0·63. Overall predictive accuracy for the best models may be adequateto support activities such as quality assessment that involve aggregating over large groups of patients, but the extent to which these models may be appropriately applied to patient‐level resource allocation

ISSN:0277-6715    

DOI:10.1002/sim.4780110409

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

摘要:

AbstractWe have used Monte Carlo methods to compare the type I error properties of the conditional and unconditional versions of the generalizedtand the generalized rank‐sum tests to those of the independent samplestand Wilcoxon rank‐sum tests. Results showed inflated type I errors for the conditional generalized tests but not for the unconditional tests. We also compared the power of the unconditional generalized tests to that of thetand Wilcoxon tests under a variety of conditions. Results showed the generalized tests to be much more efficient than their traditional counterparts in some circumstances, but substantially less powerful in others. Based on these and other considerations, we conclude that the application of these newer statistics in medical research needs further considerat

ISSN:0277-6715    

DOI:10.1002/sim.4780110410

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780110411

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1992

数据来源: WILEY