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Articles 1 - 3 of 3
Full-Text Articles in Biostatistics
Multiple Outcomes In Health Services Research: Hypothesis Tests And Power, Donald C. Martin, Paula Diehr, Thomas D. Koepsell, Stephan D. Fihn
Multiple Outcomes In Health Services Research: Hypothesis Tests And Power, Donald C. Martin, Paula Diehr, Thomas D. Koepsell, Stephan D. Fihn
UW Biostatistics Working Paper Series
Health services research often is directed towards making small improvements in a number of outcomes that reflect many aspects of the patient’s life rather than a large improvement in a single well defined outcome. A researcher might choose five scales to measure different aspects of treatment outcomes and not expect any large treatment differences on any single outcome measure. O’Brien (1984) has proposed a nonparametric statistical procedure which is particularly well suited to this type of problem and that can result in considerable increases in statistical power. This paper will briefly review O’Brien’s pooled rank method and develop power calculations. …
Effect Size And Power For Clinical Trials Using Years Of Healthy Life As The Primary Endpoint, Paula Diehr
Effect Size And Power For Clinical Trials Using Years Of Healthy Life As The Primary Endpoint, Paula Diehr
Paula Diehr
Some clinical trials perform repeated measurements on patients over time, plot those measures against time, and summarize the results in terms of the area under the curve. If the measured variable is health status, the summary outcome is sometimes referred to as years of healthy life (YHL), or quality-adjusted life years (QALY). This paper investigates some theoretical and practical aspects of randomized trials designed to assess measures such as YHL. We first derived algebraic expressions for the effect size of YHL measures under several theoretical models of the treatment's effect on health. We used these expressions to examine how the …
Pooling Community Data For Community Interventions When The Number Of Pairs Is Small, Paula Diehr, Ted Lystig, Holly Andrilla, Ziding Feng
Pooling Community Data For Community Interventions When The Number Of Pairs Is Small, Paula Diehr, Ted Lystig, Holly Andrilla, Ziding Feng
UW Biostatistics Working Paper Series
There is considerable interest in community interventions for health promotion, where the community is the experimental unit. Because such interventions are expensive, the number of experimental units (communities) is usually very small, yielding a study with low power. We examined the ability of a process known as “pooling” or “preliminary significance testing” to improve the power of community variations. In this process, one first tests whether there is significant community variation, using type 1 error of perhaps 0.25. If there is significant variation, the usual community-level test is performed. If not, a person-level test is performed. We found through Monte …