Methodological Challenges in Biomedical HIV Prevention Trials (2008) / Chapter Skim
Currently Skimming:
Appendix C: Supporting Materials for Chapter 2
Appendix C: Supporting Materials for Chapter 2
Pages 236-244
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 236... ...
Relationship of Effectiveness to Biological Efficacy and Adherence The overall effectiveness of an intervention depends on its biological efficacy and the degree to which individuals adhere to the product's intended use. Nonadherence can be due to several factors.
|
From page 237... ...
Impact of Errors in Assumptions on REQUIRED Sample Size This section discusses the impact on sample size requirements of errors in estimating two critical factors: the incidence rate of HIV infection in the control group, and the relative risk of the intervention.
|
From page 238... ...
We are interested in testing the null hypothesis of no effect of the intervention: that is, Ho:R = 1. Investigators typically calculate sample size proceed by assuming a plausible a priori value for R, and then size the trial to have adequate power to detect that relative risk.
|
From page 239... ...
Impact of the Relative Risk on Required Sample Size In this section we develop some simple approximations for the impact on the sample size of changes in the assumed relative risk. The required total number of events (D)
|
From page 240... ...
Impact of Imperfect Adherence on Required Sample Size The results in the preceding section can be used to illustrate the impact of imperfect adherence on the size of trials. As shown by equation 1, nonadherence dilutes the effectiveness of an intervention.
|
From page 241... ...
If there are small differences in both the actual incidence rate and the actual relative risk compared with the assumed rates, then these differences compound, and can result in large differences in the required sample sizes. For example, suppose a trial is designed to detect an effectiveness of 0.30 based on an annual incidence rate of 0.05.
|
From page 242... ...
for the two trial designs is approximately 2 n2 I1 1 − R1 d1 = . n1 I 2 1 − R2 d2 Thus, the relative sample sizes approximately depend multiplicatively on the ratio of incidence rates, the ratio of the square of the effectiveness, and the relative duration of follow-up.
|
From page 243... ...
Efficacy trial size Effectiveness trial Sample Pt FU Incidence RR Pt FU Incidence RR Ratio 0.5 2% 0.4 4 4% 0.6 7.11 0.5 3 5.33 0.5 2 3.56 0.5 0.3 4 0.6 5.22 0.5 3 3.92 0.5 2 3.56 0.5 3% 0.4 4 4% 0.6 4.74 0.5 3 3.56 0.5 2 2.37 0.5 0.3 4 0.6 3.48 0.5 3 2.61 0.5 2 1.74 0.5 4% 0.3 4 4% 0.6 2.61 0.5 3 1.96 0.5 2 1.30 0.5 0.2 4 0.6 2.00 0.5 3 1.50 0.5 2 1.00 0.5 4% 0.4 4 4% 0.6 3.56 0.5 3 2.67 0.5 2 1.78 0.5 0.3 4 0.6 2.61 0.5 3 1.96 0.5 2 1.31 0.5 4% 0.4 4 3% 0.6 2.67 0.5 3 2.00 0.5 2 1.33 0.5 0.3 4 0.6 1.96 0.5 3 1.47 0.5 2 0.98 0.5 4% 0.3 4 2% 0.6 1.31 0.5 3 0.98 0.5 2 0.65 0.5 0.2 4 0.6 1.00 0.5 3 0.75 0.5 2 0.50 Note: Pt FU = participant follow-up.
|
From page 244... ...
International Agency for Research on Cancer workshop, May 25–27, 1983. IARC Scientific Publica tions (82)
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.