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Article 1: Bala, M. V., & Mauskopf, J. (1999). The estimation and use of confidence intervals in economic analysis. Therapeutic Innovation & Regulatory Science, 33(3), 841–848. doi:10.1177/009286159903300325.

Summary/Abstract: This paper summarizes the different methods used to estimate confidence intervals for cost-effectiveness ratios, along with their main strengths and weaknesses. We then examine the cost-effectiveness panel’s recommendation on how to use these confidence intervals in decision making. We conclude that the guidelines on the use of confidence intervals need to be based in decision theory and that further work is needed to guide the use of confidence intends in health care decision making. We propose Bayesian analysis as a promising theoretical framework for integrating uncertainty into health economic analysis.

Questions to Consider

1. What two approaches are typically used to characterize the decision model? Which one is favored the most and why?

2. The article states that confidence intervals allow decision-makers to determine the confidence they can place on the:

1. cost-effectiveness results.
2. cost ratio.
3. benefit transaction.
4. population variance.

3. The author states that confidence intervals can be used to:

1. rule out alternative interpretations.
2. predict sample variance.
3. Interpret past research.
4. guide decisions regarding future research.
    

Article 2: Ely, M. (1999). The importance of estimates and confidence intervals rather than P Values. Sociology, 33(1), 185–190. doi:10.1017/s0038038599000103.

Summary/Abstract: The importance of reporting estimates and confidence intervals for statistical analyses has been well publicized in the arena of medical studies for some years now. The requirement to give confidence intervals for the main results of a study has been included in the statistical guidelines for contributors to medical journals since the 1980s and methodological points such as this are discussed in the Statistical Notes section of the British Medical Journal. If the use of quantitative methods in British sociology is to be encouraged, as Frank Bechhofer (1996) suggests is needed, it is important to have a forum for the dissemination of basic methodological issues which is accessible to researchers within the discipline. This note aims to achieve such dissemination by using an example from current research to illustrate these fundamental, but often overlooked, aspects of quantitative analysis.

Questions to Consider

1. What points does the author provide as to why we should ignore “p-values”?

2. Statistical analysis in itself ____ provide an answer to questions about the substantive importance of this estimate, which is a matter for the ________ to evaluate.

1. can; social scientist
2. cannot; social scientist
3. can; statistician
4. cannot; statistician

3. Compared to “p-values,” a confidence interval can tell us whether the estimate is __________.

1. theoretically important
2. theoretically unimportant
3. statistically significant
4. statistically insignificant