摘要:

AbstractEvaluations of the efficacy of over‐the‐ounter drugs using ANOVA techniques often misuse multiple comparison procedures. Studies that involve both a placebo control and established drugs as positive controls are especially prone to these problems. The most common mistake involves using a procedure which does not control the experimentwise type I error rate, usually the Duncan procedure or some version of multiplettests. These procedures control comparisonwise type I error rate, but lack the important experimentwise error control.The purpose of this paper is to clarify the issues involved in performing ANOVA followed by a multiple comparison procedure for over‐the‐ounter drug studies involving both placebo and positive c

ISSN:0277-6715    

DOI:10.1002/sim.4780100102

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

年代:1991

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780100103

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

年代:1991

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780100104

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

年代:1991

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780100105

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

年代:1991

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780100106

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

年代:1991

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780100107

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

年代:1991

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780100108

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

年代:1991

数据来源: WILEY

摘要:

AbstractMethods for estimating required sample size for comparing two population means have been published. Most involve the use of complicated formulae and tables. These methods are limited to comparing two groups. Although techniques exist to determine sample sizes for comparing more than two groups, they are intrinsically far more complicated. A simple linear nomogram is proposed as a solution to these problems, and its use is illustrated with examples of parallel group, ordered parallel group and factorial designs.

ISSN:0277-6715    

DOI:10.1002/sim.4780100109

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

年代:1991

数据来源: WILEY

摘要:

AbstractWe define ‘biologic efficacy’ as the effect of treatment for all persons who receive the therapeutic agent to which they were assigned. It measures the biologic action of treatment among compliant persons. In a randomized trial with one treatment and one placebo control, one can theoretically estimate efficacy by comparing persons who complete the treatment regimen with controls who similarly complete the control regimen. In practice, however, we make this comparison with reservation because a control protocol often presents a different challenge for compliance than does the treatment, so that the compliant subgroups are not comparable. Standard practice employs intent‐to‐treat comparisons in which one compares those randomized to treatment and control without reference to whether they actually received the treatment. Intent‐to‐treat comparisons estimate the ‘programmatic effectiveness’ of a treatment rather than its biologic efficacy.This paper introduces and derives the statistical properties of an alternative estimator of biologic efficacy that avoids the potential selection bias inherent in a comparison of compliant subgroups. The method applies to randomized trials with a dichotomous outcome measure, whether or not a placebo is given to the control group. The idea is to compare the compliers in the treatment group to an inferred control subgroup chosen to eliminate selection bias. The methodology was motivated by and is illustrated in the analysis of a randomized community trial of the impact of vitamin A supplementation on child

ISSN:0277-6715    

DOI:10.1002/sim.4780100110

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

年代:1991

数据来源: WILEY

摘要:

AbstractThis paper considers the estimation of the variance of a mortality rate in community intervention studies with little or no replication of intervention regimens. Our approach in estimation of this cluster sampling variance is to determine the variance for simple random sampling and multiply it by a design effect which we calculate with use of information obtained from other sources. When the county is the unit of randomization and the outcome is mortality, we calculate the design effect as the ratio of the age adjusted mortality rates for single stage cluster sampling relative to simple random sampling; we use information from all counties in a state in the calculations. We apply this approach empirically for breast cancer mortality. We found that these design effects were dependent on length of time for accumulation of deaths (1.1 for one year up to 3.5 for eight years) and were quite consistent for the three states and nine years considered in the investigation. We present a model that accounts for the time dependence of the design effect and we show it provides a good representation of the observed relationship.

ISSN:0277-6715    

DOI:10.1002/sim.4780100111

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

年代:1991

数据来源: WILEY