Needle size in intravitreal injections - pain evaluation of a randomized clinical trial.

PURPOSE: To evaluate the influence of the needle size used for intravitreal (IVT) injections on patients' pain experience in a randomized, double-armed, single-blinded, clinical trial. METHODS: Patients included were randomized to have an IVT injection performed with a 27-gauge needle (group 1) or with a 30-gauge needle (group 2). The topical anaesthesia before the injection was standardized. Immediately after the injection, patients were asked to grade their pain using the visual analogue scale (VAS) and the Wong-Baker FACES scale. The main outcome measure was the pain score assessment. Cofactors analysed were patients' demographics (age and gender) and clinical characteristics (such as the number of previous IVT injections). In addition, scaled surgeon's questionnaires to assess the IVT injection procedure were evaluated. For statistical analysis, a regression model was used. RESULTS: The data of 208 patients (group 1: 104 patients; group 2: 104 patients) were analysed. There was no significant difference in the VAS pain scores (p > 0.18) and in the Wong-Baker pain scores (p > 0.59) between both treatment groups. Gender (p = 0.0288) and the number of previous IVT injections (p = 0.0028) significantly influenced the VAS pain scores (p < 0.05). Female patients and patients with a history of previous IVT injections had higher pain scores. The surgeon's questionnaire showed an overall preference towards the use of a 30-gauge needle for IVT injections. CONCLUSION: The use of a 30-gauge needle for IVT injections showed no significant effect in pain relief compared to the use of a 27-gauge needle. However, a 30-gauge needle was preferred by all surgeons.

Detection of epistatic effects with logic regression and a classical linear regression model.

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.