Background: For finite samples with binary outcomes penalized logistic regression such as ridge logistic regression has the potential of achieving smaller mean squared errors (MSE) of coefficients and predictions than maximum likelihood estimation. There is evidence, however, that ridge logistic regression can result in highly variable calibration slopes in small or sparse data situations.Methods: In this paper, we elaborate this issue further by performing a comprehensive simulation study, investigating the performance of ridge logistic regression in terms of coefficients and predictions and comparing it to Firth’s correction that has been shown to perform well in low-dimensional settings. In addition to tuned ridge regression where the penalty strength is estimated from the data by minimizing some measure of the out-of-sample prediction error or information criterion, we also considered ridge regression with pre-specified degree of shrinkage. We included ‘oracle’ models in the simulation study in which the complexity parameter was chosen based on the true event probabilities (prediction oracle) or regression coefficients (explanation oracle) to demonstrate the capability of ridge regression if truth was known.Results: Performance of ridge regression strongly depends on the choice of complexity parameter. As shown in our simulation and illustrated by a data example, values optimized in small or sparse datasets are negatively correlated with optimal values and suffer from substantial variability which translates into large MSE of coefficients and large variability of calibration slopes. In contrast, in our simulations pre-specifying the degree of shrinkage prior to fitting led to accurate coefficients and predictions even in non-ideal settings such as encountered in the context of rare outcomes or sparse predictors.Conclusions: Applying tuned ridge regression in small or sparse datasets is problematic as it results in unstable coefficients and predictions. In contrast, determining the degree of shrinkage according to some meaningful prior assumptions about true effects has the potential to reduce bias and stabilize the estimates.

The parameters of logistic regression models are usually obtained by the method of maximum likelihood (ML). However, in analyses of small data sets or data sets with unbalanced outcomes or exposures, ML parameter estimates may not exist. This situation has been termed ‘separation’ as the two outcome groups are separated by the values of a covariate or a linear combination of covariates. To overcome the problem of non-existing ML parameter estimates, applying Firth’s correction (FC) was proposed. In practice, however, a principal investigator might be advised to ‘bring more data’ in order to solve a separation issue. We illustrate the problem by means of examples from colorectal cancer screening and ornithology. It is unclear if such an increasing sample size (ISS) strategy that keeps sampling new observations until separation is removed improves estimation compared to applying FC to the original data set. We performed an extensive simulation study where the main focus was to estimate the cost-adjusted relative efficiency of ML combined with ISS compared to FC. FC yielded reasonably small root mean squared errors and proved to be the more efficient estimator. Given our findings, we propose not to adapt the sample size when separation is encountered but to use FC as the default method of analysis whenever the number of observations or outcome events is critically low.

Background: The use of potentially inappropriate medication (PIM) in population of older adults may result in adverse drug events (ADE) already after short term exposure, especially when it is prescribed to patients with chronic kidney disease (CKD). In order to limit ADE in the treatment of older adults PIM lists have been constructed as a source of information for healthcare professionals. The aim of this study was to estimate the utilization of PIM and incidence of ADE in older adults (≥70 years) with CKD.Methods: We conducted a retrospective population-wide cohort study including patients from Lower Austria who were 70 years or older and diagnosed with CKD in the period from 2008 to 2011. Utilization of PIM was estimated from prescriptions filled by target population. We estimated risks of hospitalization due to ADE within 30 days after incident PIM prescription and compared them to a PIM-free control group by using marginal structural models (MSM).Results: We identified 11,547 patients (women: 50.6%, median age in 2008: 78 years) who fulfilled the inclusion criteria. In total 24.7 and 8.1% of all prescriptions from that period contained a medication with a substance listed in the EU (7)-PIM and AT-PIM list, respectively. Proton pump inhibitors andGinkgo bilobawere the most often prescribed PIMs in this population. 94.6 and 79.3% patients filled at least one EU(7)-PIM and AT-PIM prescription, respectively. Despite the relatively high utilization of PIM there was only a low incidence of clinically relevant ADE. No event type exceeded the threshold level of 1% in the analysis of risks of ADE after filling a prescription for PIM. Nevertheless, MSM analysis showed an increased risk for 11 drugs and reduced risk for 4 drugs.Conclusions: PIM prescription was common among older adults with CKD, however, only a small number of these drugs eventually led to hospitalization due to ADE within 30 days after incident PIM was filled. In the absence of a clinically important PIM-related increase in risk, an assessment of potential ADE severity to a PIM list by using a warning score system seems prudent.

Logistična regresija se uporablja za preučevanje povezanosti med binarno odzivno spremenljivko (nek dogodek se zgodi ali pa ne) in množico neodvisnih spremenljivk. Z modelom lahko natančno napovemo verjetnosti dogodka za nove enote, pogojno na vrednosti neodvisnih spremenljivk. Poleg tega ocene parametrov modela, ki jih dobimo z metodo največjega verjetja, lahko interpretiramo kot logaritem razmerja obetov. Kadar so vzorci majhni ali dogodki v kateri od skupin, ki določajo neodvisno spremenljivko, redki, se lahko zgodi, da algoritem po metodi največjega verjetja ne konvergira, ocene parametrov modela pa so posledično nesmiselne. Pojav se v statistiki imenuje »ločenost«. Ker programska oprema problema pogosto ne identificira, raziskovalci ločenost v praksi lahko prezrejo. Dobljeni rezultati raziskovalce lahko begajo ali pa jih napačno interpretirajo. S člankom zato želimo: motivirati uporabo logistične regresije za preučevanje povezanosti binarne odzivne spremenljivke z množico neodvisnih spremenljivk; bralcem predstaviti problem ločenosti na konkretnem primeru; pokazati, kako problem ločenosti premostiti.

Background/objectives: Grapevine (Vitis vinifera L.) is one of the most economically and culturally important fruit crops worldwide and hosts more than 100 viruses. Viral infections can cause severe yield losses, but plants can adapt to infection through changes in miRNA-mediated regulatory pathways. MicroRNAs are key regulators of plant development and stress responses. Several prediction tools are available for miRNA detection from small RNA sequencing data, each relying on different algorithms. The aim of this study was to compare miRNA predictions generated by three widely used tools (miRador, ShortStack, and miRDeep2) and to evaluate how viral coinfections influence miRNA expression in grapevine. Methods: Two grapevine cultivars, Refošk (“Terrano”) and Zeleni Sauvignon (“Sauvignon Vert”), were analyzed. Small RNA sequencing was performed on virus-free plants and plants coinfected with grapevine Pinot gris virus (GPGV), grapevine rupestris stem pitting-associated virus (GRSPaV), and grapevine rupestris vein feathering virus (GRVFV). Three miRNA prediction tools were used to identify miRNAs annotated in public databases. Differential expression analysis was performed separately for each tool and by using an integrated approach that combined all three datasets. The expression of selected miRNAs was further evaluated using stem-loop RT-qPCR. Results: The three prediction tools detected markedly different numbers of miRNAs, resulting in largely distinct sets of differentially expressed miRNAs and limited overlap between individual analyses. The integrated approach yielded a separate set of differentially expressed miRNAs, most of which overlapped with at least one individual dataset. Stem-loop RT-qPCR analysis supported the differential expression of several selected miRNAs. Conclusions: This study provides new insight into miRNA expression in grapevine under mixed-virus infection and demonstrates that miRNA profiling outcomes are strongly influenced by the choice of bioinformatic prediction tool. Our results highlight the importance of integrated analytical strategies combined with experimental validation to obtain robust and biologically meaningful interpretations of miRNA expression in plants.