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Medical Biomathematics and Biometrics Commons™
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Full-Text Articles in Medical Biomathematics and Biometrics
Optimal Survey Design For Community Intervention Evaluations: Cohort Or Cross-Sectional?, Paula Diehr
Optimal Survey Design For Community Intervention Evaluations: Cohort Or Cross-Sectional?, Paula Diehr
Paula Diehr
Community intervention evaluations that measure changes over time may conduct repeated cross-sectional surveys, follow a cohort of residents over time, or (often) use both designs. Each survey design has implications for precision and cost. To explore these issues, we assume that two waves of surveys are conducted, and that the goal is to estimate change in behavior for people who reside in the community at both times. Cohort designs are shown to provide more accurate estimates (in the sense of lower mean squared error) than cross-sectional estimates if (1) there is strong correlation over time in an individual's behavior at …
Breaking The Matches In A Paired T-Test For Community Interventions When The Number Of Pairs Is Small, Paula Diehr
Breaking The Matches In A Paired T-Test For Community Interventions When The Number Of Pairs Is Small, Paula Diehr
Paula Diehr
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 small. Because of the small number of communities involved, investigators often match treatment and control communities on demographic variables before randomization to minimize the possibility of a bad split. Unfortunately, matching has been shown to decrease the power of the design when the number of pairs is small, unless the matching variable is very highly correlated with the outcome variable (in this case, with change in the health behaviour). We …
Including Deaths When Measuring Health Status Over Time, Paula Diehr
Including Deaths When Measuring Health Status Over Time, Paula Diehr
Paula Diehr
Measuring health status over time is problematic when some subjects die, because death does not have a defined value on most health status measures. This situation is different from the usual missing data problem because the health status of the dead is, in a sense, known. We examined eight strategies for incorporating deaths into such analyses using three health status measures taken from two data sets, after which we used computer simulation to explore more fully the effect of deaths. The strategies differed in the amount of influence given to the deaths, varying from none (deaths were discarded) to complete …