Airway reactivity in children before and after stem cell transplantation

New Confidence Intervals for Relative Risk of Two Correlated Proportions

Biomedical studies, such as clinical trials, often require the comparison of measurements from two correlated tests in which each unit of observation is associated with a binary outcome of interest via relative risk. The associated confidence interval is crucial because it provides an appreciation of the spectrum of possible values, allowing for a more robust interpretation of relative risk. Of the available confidence interval methods for relative risk, the asymptotic score interval is the most widely recommended for practical use. We propose a modified score interval for relative risk and we also extend an existing nonparametric U-statistic-based confidence interval to relative risk. In addition, we theoretically prove that the original asymptotic score interval is equivalent to the constrained maximum likelihood-based interval proposed by Nam and Blackwelder. Two clinically relevant oncology trials are used to demonstrate the real-world performance of our methods. The finite sample properties of the new approaches, the current standard of practice, and other alternatives are studied via extensive simulation studies. We show that, as the strength of correlation increases, when the sample size is not too large the new score-based intervals outperform the existing intervals in terms of coverage probability. Moreover, our results indicate that the new nonparametric interval provides the coverage that most consistently meets or exceeds the nominal coverage probability.

Exact confidence intervals for the relative risk and the odds ratio

For comparison of proportions, there are three commonly used measurements: the difference, the relative risk, and the odds ratio. Significant effort has been spent on exact confidence intervals for the difference. In this article, we focus on the relative risk and the odds ratio when data are collected from a matched-pairs design or a two-arm independent binomial experiment. Exact one-sided and two-sided confidence intervals are proposed for each configuration of two measurements and two types of data. The one-sided intervals are constructed using an inductive order, they are the smallest under the order, and are admissible under the set inclusion criterion. The two-sided intervals are the intersection of two one-sided intervals. R codes are developed to implement the intervals. Supplementary materials for this article are available online.

Recommended tests and confidence intervals for paired binomial proportions

We describe, evaluate, and recommend statistical methods for the analysis of paired binomial proportions. A total of 24 methods are considered. The best tests for association include the asymptotic McNemar test and the McNemar mid- p test. For the difference between proportions, we recommend two simple confidence intervals with closed-form expressions and the asymptotic score interval. The asymptotic score interval is also recommended for the ratio of proportions, as is an interval with closed-form expression based on combining two Wilson score intervals for the single proportion. For the odds ratio, we recommend a transformation of the Wilson score interval and a transformation of the Clopper-Pearson mid- p interval. We illustrate the practical application of the methods using data from a recently published study of airway reactivity in children before and after stem cell transplantation and a matched case-control study of the association between floppy eyelid syndrome and obstructive sleep apnea-hypopnea syndrome.

The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional

BACKGROUND

Statistical methods that use the mid-p approach are useful tools to analyze categorical data, particularly for small and moderate sample sizes. Mid-p tests strike a balance between overly conservative exact methods and asymptotic methods that frequently violate the nominal level. Here, we examine a mid-p version of the McNemar exact conditional test for the analysis of paired binomial proportions.

METHODS

We compare the type I error rates and power of the mid-p test with those of the asymptotic McNemar test (with and without continuity correction), the McNemar exact conditional test, and an exact unconditional test using complete enumeration. We show how the mid-p test can be calculated using eight standard software packages, including Excel.

RESULTS

The mid-p test performs well compared with the asymptotic, asymptotic with continuity correction, and exact conditional tests, and almost as good as the vastly more complex exact unconditional test. Even though the mid-p test does not guarantee preservation of the significance level, it did not violate the nominal level in any of the 9595 scenarios considered in this article. It was almost as powerful as the asymptotic test. The exact conditional test and the asymptotic test with continuity correction did not perform well for any of the considered scenarios.

CONCLUSIONS

The easy-to-calculate mid-p test is an excellent alternative to the complex exact unconditional test. Both can be recommended for use in any situation. We also recommend the asymptotic test if small but frequent violations of the nominal level is acceptable.

Lung function and late pulmonary complications among survivors of hematopoietic stem cell transplantation during childhood

Hematopoietic stem cell transplantation (HSCT) is used to treat an expanding array of malignant and non-malignant disorders. Pulmonary complications represent a significant source of morbidity and mortality in HSCT recipients. Young children, whose lungs are still developing and growing, may be especially susceptible to the insults of irradiation, drug toxicities, and recurrent infections associated with immunosuppression. Late pulmonary complications, those occurring more than three months after transplantation, are often noninfectious and present with nonspecific symptomatology. Pulmonary function testing (PFT) is a mainstay of monitoring pulmonary health in HSCT recipients. The pulmonologist should be familiar with common patterns seen on PFT in recipients of HSCT during childhood. In this review, we describe the findings in studies which have examined lung function over time in patients who underwent HSCT during childhood. We discuss patterns of PFT abnormalities, associated noninfectious syndromes and their clinical implications, as well as directions for future research.