A simple method for estimating the odds ratio in matched case-control studies with incomplete paired data

Fungal signatures of oral disease reflect environmental degradation in a facultative avian scavenger

Degradation of natural ecosystems increases the risk of infections in wildlife due to microbiota dysbiosis. However, little is known about its influence on the development of fungal communities in predators and facultative avian scavengers. We evaluated the incidence of oral disease in wild nestling black kites (Milvus migrans) under contrasting environmental degradation conditions, and explored their oral fungal patterns using molecular methods and multivariate analysis. Oral lesions were found in 36.8% of the 38 nestlings examined in an anthropogenically altered habitat (southeastern Madrid, Spain), but in none of the 105 nestlings examined in a well-conserved natural area (Doñana National Park, Spain). In a subsample of 48 black kites, the composition of the oral fungal community differed among symptomatic nestlings from Madrid (SM) and asymptomatic nestlings from Madrid (AM) and Doñana (AD). Opportunistic fungal pathogens (e.g., Fusarium incarnatum-equiseti species complex, Mucor spp., Rhizopus oryzae) were more prevalent in SM and AM than in AD. Hierarchical clustering and principal component analyses revealed that fungal patterns were distinct between both study areas, and that anthropogenic and natural environmental factors had a greater impact on them than oral disease. Fungal signatures associated with anthropogenic and natural stresses harbored some taxa that could be used to flag oral infection (F. incarnatum-equiseti species complex and Alternaria), indicate environmental degradation (Alternaria) or provide protective benefits in degraded environments (Trichoderma, Epicoccum nigrum and Sordaria). Co-occurrence associations between potentially beneficial and pathogenic fungi were typical of AM and AD, hinting at a possible role in host health. This study shows that early-life exposure to highly degraded environments induces a shift towards a higher prevalence of pathogenic species in the oral cavity of black kites, favoring oral disease. Furthermore, our findings suggest potential ecological applications of the monitoring of oral mycobiome as a bioindication of oral disease and environmental degradation.

Valid statistical inference methods for a case-control study with missing data

The main objective of this paper is to derive the valid sampling distribution of the observed counts in a case-control study with missing data under the assumption of missing at random by employing the conditional sampling method and the mechanism augmentation method. The proposed sampling distribution, called the case-control sampling distribution, can be used to calculate the standard errors of the maximum likelihood estimates of parameters via the Fisher information matrix and to generate independent samples for constructing small-sample bootstrap confidence intervals. Theoretical comparisons of the new case-control sampling distribution with two existing sampling distributions exhibit a large difference. Simulations are conducted to investigate the influence of the three different sampling distributions on statistical inferences. One finding is that the conclusion by the Wald test for testing independency under the two existing sampling distributions could be completely different (even contradictory) from the Wald test for testing the equality of the success probabilities in control/case groups under the proposed distribution. A real cervical cancer data set is used to illustrate the proposed statistical methods.

A new framework of statistical inferences based on the valid joint sampling distribution of the observed counts in an incomplete contingency table

Some existing confidence interval methods and hypothesis testing methods in the analysis of a contingency table with incomplete observations in both margins entirely depend on an underlying assumption that the sampling distribution of the observed counts is a product of independent multinomial/binomial distributions for complete and incomplete counts. However, it can be shown that this independency assumption is incorrect and can result in unreliable conclusions because of the under-estimation of the uncertainty. Therefore, the first objective of this paper is to derive the valid joint sampling distribution of the observed counts in a contingency table with incomplete observations in both margins. The second objective is to provide a new framework for analyzing incomplete contingency tables based on the derived joint sampling distribution of the observed counts by developing a Fisher scoring algorithm to calculate maximum likelihood estimates of parameters of interest, the bootstrap confidence interval methods, and the bootstrap testing hypothesis methods. We compare the differences between the valid sampling distribution and the sampling distribution under the independency assumption. Simulation studies showed that average/expected confidence-interval widths of parameters based on the sampling distribution under the independency assumption are shorter than those based on the new sampling distribution, yielding unrealistic results. A real data set is analyzed to illustrate the application of the new sampling distribution for incomplete contingency tables and the analysis results again confirm the conclusions obtained from the simulation studies.