Cluster randomized controlled trials are increasingly used to assess the
effectiveness of life-style interventions in improvement of health services or prevention
of disease. However, statistical methods in the analysis of cluster randomized
controlled trials are not well established especially for analyzing binary outcomes.
This project is motivated by the Community Hypertension Assessment Trial
(CHAT) to assess the effectiveness of a 12-month community-based blood pressure
management program in improving the management and monitoring of high blood
pressure (BP) among older people. The study is a paired cluster randomized controlled
trial, where the family physicians' practices are the clusters randomly allocated to
CHAT intervention or usual practice, and a random sample of 55 patients 65 years and
older were selected from the 14 practices in each study arm for health record review.
The primary outcome was controlled BP over 12 months defined as systolic BP c:; 140
and diastolic BP c:; 90 for patients without diabetes or target organ damage or systolic
BP c:; 130 and diastolic BP c:; 80 for patients with diabetes or target organ damage.
Secondary outcomes include frequency of BP monitoring and average BP over a 12
month period.
The clinical objective of this project is to evaluate the effectiveness of the
CHAT intervention. The statistical objective is to compare Bayesian and classical
methods of analyzing cluster-randomized trials using CHAT study as an example. We
compared the results of different cluster-level analysis methods: i) un-weighted regression, ii) weighted regression, iii) random-effects meta-analytic approach, and
different individual-level analyses: i) standard logistic regression, ii) robust standard
errors approach, iii) generalized estimating equations, iv) random-effect logistic
regression, v) Bayesian random-effect regression.
We find that there is no sufficient evidence in support of the effectiveness of the
CHAT intervention on all outcomes. For BP control, odds ratio (95% confidence
interval) is 1.14 (0.72, 1.80) from generalized estimating equations. This result remains
robust under different methods. We also find that the results from different statistical
methods are different. The results from cluster-level analysis methods are quite
different, while the results from the individual-level analysis methods are similar.
We conclude that using various methods to analyze the trial provide good
sensitivity analyses to help in interpreting the results of cluster randomized trials.
Extensive simulation studies comparing the statistical powers of the different methods
in different situations are required. / Thesis / Master of Science (MS)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29254 |
| Date | 12 1900 |
| Creators | Ma, Jinhui |
| Contributors | Thabane, Lehana, Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0017 seconds