Melanoma-associated retinopathy (MAR) is a paraneoplastic syndrome associated with cutaneous metastatic melanoma in which patients develop vision deficits that include reduced night vision, poor contrast sensitivity, and photopsia. MAR is caused by autoantibodies targeting TRPM1, an ion channel found in melanocytes and retinal ON-bipolar cells (ON-BCs). The visual symptoms arise when TRPM1 autoantibodies enter ON-BCs and block the function of TRPM1, thus detection of TRPM1 autoantibodies in patient serum is a key criterion in diagnosing MAR. Electroretinograms are used to measure the impact of TRPM1 autoantibodies on ON-BC function and represent another important diagnostic tool for MAR. To date, MAR case reports have included one or both diagnostic components, but only for a single time point in the course of a patient\'s disease. Here, we report a case of MAR supported by longitudinal analysis of serum autoantibody detection, visual function, ocular inflammation, vascular integrity, and response to slow-release intraocular corticosteroids. Integrating these data with the patient\'s oncological and ophthalmological records reveals novel insights regarding MAR pathogenesis, progression, and treatment, which may inform new research and expand our collective understanding of the disease. In brief, we find TRPM1 autoantibodies can disrupt vision even when serum levels are barely detectable by western blot and immunohistochemistry; intraocular dexamethasone treatment alleviates MAR visual symptoms despite high levels of circulating TRPM1 autoantibodies, implicating antibody access to the retina as a key factor in MAR pathogenesis. Elevated inflammatory cytokine levels in the patient\'s eyes may be responsible for the observed damage to the blood-retinal barrier and subsequent entry of autoantibodies into the retina.

The problem of missing data occurs frequently in veterinary epidemiological studies. Most studies use a complete case (CC) analysis which excludes all observations for which any relevant variable have missing values. Alternative approaches (most notably multiple imputation (MI)) which avoid the exclusion of observations with missing values are now widely available but have been used very little in veterinary epidemiology. This paper uses a case study based on research into dairy producers\' attitudes toward mastitis control procedures, combined with two simulation studies to evaluate the use of MI and compare results with a CC analysis. MI analysis of the original data produced results which had relatively minor differences from the CC analysis. However, most of the missing data in the original data set were in the dependent variable and a subsequent simulation study based on the observed missing data pattern and 1000 simulations showed that an MI analysis would not be expected to offer any advantages over a CC analysis in this situation. This was true regardless of the missing data mechanism (MCAR - missing completely at random, MAR - missing at random, or NMAR - not missing at random) underlying the missing values. Surprisingly, recent textbooks dealing with MI make little reference to this limitation of MI for dealing with missing values in the dependent variable. An additional simulation study (1000 runs for each of the three missing data mechanisms) compared MI and CC analyses for data in which varying levels (n=7) of missing data were created in predictor variables. This study showed that MI analyses generally produced results that were less biased on average, were more precise (smaller SEs), were more consistent (less variability between simulation runs) and consequently were more likely to produce estimates that were close to the \"truth\" (results obtained from a data set with no missing values). While the benefit of MI varied with the mechanism used to generate the missing data, MI always performed as well as, or better than, CC analysis.