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Aim: To identify the risk factors for optic nerve hypoplasia (ONH) and septo‐optic dysplasia (SOD). Method: A retrospective, population‐based study with case–control design was undertaken using the Population Research Data Repository at the Manitoba Center for Health Policy in Manitoba, Canada. Cases were 111 patients (63 males, 48 females; age range 1–35 years [mean 11 years 6 months, SD 7 years 2 months]) with ONH and SOD diagnosed from 1990 to 2019, matched to 555 unrelated population‐based controls (315 males, 240 females; age range 1–35 years [mean 11 years 6 months, SD 7 years 2 months]) on year of birth, sex, and area of residence. Additionally, 75 cases (46 males, 29 females; age range 2–35 years [mean 12 years 6 months, SD 7 years 2 months]) with ONH and SOD were matched one‐on‐one with sibling controls (40 males, 35 females; age range 0–33 years [mean 11 years 7 months, SD 7 years 10 months], the rest did not have siblings). Several antenatal maternal risk factors associated with ONH and SOD were tested for their association with case and control group membership using adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from a multivariate conditional logistic regression model. The outcome was the risk of developing ONH and SOD. Results: Maternal age at conception (OR = 0.91, 95% CI = 0.86–0.96), primigravida (OR = 3.39, 95% CI = 1.92–6.01), and smoking (OR = 2.86, 95% CI = 1.61–5.05) were independently associated with ONH and SOD in the cohort matched to unrelated controls (p < 0.001). In the sibling cohort, smoking was an important risk factor (OR = 3.65, 95% CI = 1.2–11.1, p = 0.02). Interpretation: Unmodifiable and modifiable antenatal maternal risk factors are associated with ONH and SOD. Our investigation suggests that several risk factors reported in previous studies may have been due to confounding bias and that maternal smoking during pregnancy is the main modifiable risk factor associated with ONH and SOD. What this paper adds: Historically, many antenatal risk factors have been associated with optic nerve hypoplasia (ONH) and septo‐optic dysplasia (SOD).Population‐based data with matched controls for potential confounding bias are lacking.Young maternal age at conception, primigravida, and smoking during pregnancy are the main risk factors for ONH and SOD using a population‐based, case–control design. [ABSTRACT FROM AUTHOR]

Objective COPD is a high-cost disease and results in frequent contacts with the healthcare system. The study objective was to compare the accuracy of classification models with different covariates for classifying COPD patients into cost groups. Methods Linked health administrative databases from Saskatchewan, Canada, were used to identify a cohort of newly diagnosed COPD patients (April 1, 2007 to March 31, 2011) and their episodes of healthcare encounters for disease exacerbations. Total costs of the first and follow-up episodes were computed and patients were categorized as persistently high cost, occasionally high cost, and persistently low cost based on cumulative cost distribution ranking using the 75th percentile cutoff for high-cost status. Classification accuracy was compared for seven multinomial logistic regression models containing socio-demographic characteristics (i.e., base model), and socio-demographic and prior healthcare use characteristics (i.e., comparator models). Results Of the 1182 patients identified, 8.5% were classified as persistently high cost, 26.1% as occasionally high cost, and the remainder as persistently low cost. The persistently high-cost and occasionally high-cost patients incurred 10 times (\$12 449 vs \$1263) and seven times (\$9334 vs \$1263) more costs in their first exacerbation episode than persistently low-cost patients, respectively. Classification accuracy was 0.67 for the base model, whereas the comparator model containing socio-demographic and number of prior hospital admissions had the highest accuracy (0.72). Conclusions Costs associated with COPD exacerbation episodes are substantial. Adding prior hospitalization to socio-demographic characteristics produced the highest improvements in classification accuracy. Accurate classification models are important for identifying potential healthcare cost management strategies.

This study investigates procedures for controlling the familywise error rate (FWR) when testing hypotheses about multiple, correlated outcome variables in repeated measures (RM) designs. A content analysis of RM research articles published in 4 psychology journals revealed that 3 quarters of studies tested hypotheses about 2 or more outcome variables. Several procedures originally proposed for testing multiple outcomes in 2-group designs are extended to 2-group RM designs. The investigated procedures include 2 modified Bonferroni procedures that adjust the level of significance, alpha, for the effective number of outcomes and a permutation step-down (PSD) procedure. The FWR, any-variable power, and all-variable power are investigated in a Monte Carlo study. One modified Bonferroni procedure frequently resulted in inflated FWRs, whereas the PSD procedure controlled the FWR. The PSD procedure could be substantially more powerful than the conventional Bonferroni procedure, which does not account for dependencies among the outcome variables. However, the difference in power between the PSD procedure, which does account for these dependencies, and Hochberg's step-up procedure, which does not, were negligible. A numeric example illustrates implementation of these multiple-testing procedures. (Contains 6 tables.)

This study considers the problem of testing the difference between treatment and control groups on m [greater than or equal to] 2 measures when it is assumed a priori that the treatment group will perform better than the control group on all measures. Two procedures are investigated that do not rest on the assumptions of covariance homogeneity or multivariate normality: a likelihood ratio test based on a bootstrap critical value and a composite step-down procedure based on trimmed means. Type I error rates of both procedures are insensitive to assumption violations. Procedures that test a directional alternative hypothesis can be substantially more powerful than a procedure that tests a nondirectional hypothesis for certain configurations of the population mean vectors. The differences in average power of the investigated procedures are a function of the configuration of the population means, the magnitude of correlation among the outcome measures, and the shape of the population distribution. (Contains 4 tables.)

Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of freedom heteroscedastic statistic for independent and correlated groups designs in order to achieve robustness to the biasing effects of nonnormality and variance heterogeneity. The authors describe a nonparametric bootstrap methodology that can provide improved Type I error control. In addition, the authors indicate how researchers can set robust confidence intervals around a robust effect size parameter estimate. In an online supplement, the authors use several examples to illustrate the application of an SAS program to implement these statistical methods. (Contains 4 tables, 1 figure, and 24 footnotes.)