ArticleStatistical methods in medical research2010
Bayesian credible intervals for binomial proportions in a single patient trial.
Article in Statistical methods in medical research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 19 citations in OpenAlex.
- Continuity corrected Wilson interval for the difference of two independent proportions.Journal of statistical theory and applications : JSTA · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Practitioners are often asking if the treatment successfully improved performance. Many times this question is directed towards the outcome of a single individual. In this article, we develop a method to assess the improvement of a single individual who is administered a test of percent correct at pre-treatment and post-treatment. A Bayesian approach is taken where the number correct is modelled as a binomial random variable and the percent correct is set to a beta prior distribution. The first model assumes percent correct at pre-test is equal to the percent correct at post-test and the posterior predictive distribution is used to evaluate the change in the number correct. We subsequently model the proportions correct at pre-test and post-test as unequal. The second model then assumes independent proportions and the third assumes correlated beta distributions for the two proportions. 95% credible intervals are calculated for the various methods for number of correct at post-test given a particular level at pre-test. An example using data from a cochlear implant clinical trial is presented where clinicians recorded percent correct in a consonant-nucleus-consonant test.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.