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| Title |
Confidence intervals construction for difference of two means with incomplete correlated data
|
|---|---|
| Published in |
BMC Medical Research Methodology, March 2016
|
| DOI | 10.1186/s12874-016-0125-3 |
| Pubmed ID | |
| Authors |
Hui-Qiong Li, Nian-Sheng Tang, Jie-Yi Yi |
| Abstract |
Incomplete data often arise in various clinical trials such as crossover trials, equivalence trials, and pre and post-test comparative studies. Various methods have been developed to construct confidence interval (CI) of risk difference or risk ratio for incomplete paired binary data. But, there is little works done on incomplete continuous correlated data. To this end, this manuscript aims to develop several approaches to construct CI of the difference of two means for incomplete continuous correlated data. Large sample method, hybrid method, simple Bootstrap-resampling method based on the maximum likelihood estimates (B 1) and Ekbohm's unbiased estimator (B 2), and percentile Bootstrap-resampling method based on the maximum likelihood estimates (B 3) and Ekbohm's unbiased estimator (B 4) are presented to construct CI of the difference of two means for incomplete continuous correlated data. Simulation studies are conducted to evaluate the performance of the proposed CIs in terms of empirical coverage probability, expected interval width, and mesial and distal non-coverage probabilities. Empirical results show that the Bootstrap-resampling-based CIs B 1, B 2, B 4 behave satisfactorily for small to moderate sample sizes in the sense that their coverage probabilities could be well controlled around the pre-specified nominal confidence level and the ratio of their mesial non-coverage probabilities to the non-coverage probabilities could be well controlled in the interval [0.4, 0.6]. If one would like a CI with the shortest interval width, the Bootstrap-resampling-based CIs B 1 is the optimal choice. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Librarian | 2 | 17% |
| Student > Master | 2 | 17% |
| Student > Ph. D. Student | 2 | 17% |
| Researcher | 2 | 17% |
| Student > Bachelor | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 2 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 33% |
| Mathematics | 1 | 8% |
| Nursing and Health Professions | 1 | 8% |
| Psychology | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Other | 2 | 17% |
| Unknown | 2 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 March 2016.
All research outputs
#14,842,329
of 22,856,968 outputs
Outputs from BMC Medical Research Methodology
#1,443
of 2,017 outputs
Outputs of similar age
#168,674
of 299,532 outputs
Outputs of similar age from BMC Medical Research Methodology
#22
of 32 outputs
Altmetric has tracked 22,856,968 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,017 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,532 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.