Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations.

Despite the increasing relevance of statistics in health sciences, teaching styles in higher education are remarkably similar across disciplines: lectures covering the theory and methods, followed by application and computer exercises in given data sets. This often leads to challenges for students in comprehending fundamental statistical concepts essential for medical research. To address these challenges, we propose an engaging learning approach-DICE (design, interpret, compute, estimate)-aimed at enhancing the learning experience of statistics in public health and epidemiology. In introducing DICE, we guide readers through a practical example. Students will work in small groups to plan, generate, analyze, interpret, and communicate their own scientific investigation with simulations. With a focus on fundamental statistical concepts such as sampling variability, error probabilities, and the construction of statistical models, DICE offers a promising approach to learning how to combine substantive medical knowledge and statistical concepts. The materials in this paper, including the computer code, can be readily used as a hands-on tool for both teachers and students.

Multiple imputation of systematically missing data on gait speed in the Swedish National Study on Aging and Care.

BACKGROUND: There is insufficient investigation of multiple imputation for systematically missing discrete variables in individual participant data meta-analysis (IPDMA) with a small number of included studies. Therefore, this study aims to evaluate the performance of three multiple imputation strategies - fully conditional specification (FCS), multivariate normal (MVN), conditional quantile imputation (CQI) - on systematically missing data on gait speed in the Swedish National Study on Aging and Care (SNAC). METHODS: In total, 1 000 IPDMA were simulated with four prospective cohort studies based on the characteristics of the SNAC. The three multiple imputation strategies were analysed with a two-stage common-effect multivariable logistic model targeting the effect of three levels of gait speed (100% missing in one study) on 5-years mortality with common odds ratios set to OR1 = 0.55 (0.8-1.2 vs ≤0.8 m/s), and OR2 = 0.29 (>1.2 vs ≤0.8 m/s). RESULTS: The average combined estimate for the mortality odds ratio OR1 (relative bias %) were 0.58 (8.2%), 0.58 (7.5%), and 0.55 (0.7%) for the FCS, MVN, and CQI, respectively. The average combined estimate for the mortality odds ratio OR2 (relative bias %) were 0.30 (2.5%), 0.33 (10.0%), and 0.29 (0.9%) for the FCS, MVN, and CQI respectively. CONCLUSIONS: In our simulations of an IPDMA based on the SNAC where gait speed data was systematically missing in one study, all three imputation methods performed relatively well. The smallest bias was found for the CQI approach.

The Association between Birthweight and Use of Cardiovascular Medications: The Role of Health Behaviors.

BACKGROUND: There is limited evidence on the effect of low birthweight on the use of cardiovascular medications and the role of health behaviors. This study aims to determine the independent effect of low birthweight and its combination with adult health behaviors on the number of dispensed cardiovascular medications. METHODS: We included 15618 participants with information on birthweight and self-reported health behaviors. Dispensed cardiovascular medications were identified from the Prescribed Drug Register based on a three-digit level Anatomical Therapeutic Chemical classification code (C01 to C10 and B01) and categorized into 0, 1, and ≥2 different types of medications. We applied multinomial logistic regression models estimating odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Participants with low birthweight had a higher estimated OR of using ≥2 types of cardiovascular medications (OR = 1.46, 95% CI = 1.06, 2.01). Further, an increased risk for using ≥2 types of cardiovascular medications was found in participants with poor health behaviors for normal (OR = 2.17, 95% CI = 1.80, 2.62) and high (OR = 1.84, 95% CI = 1.29, 2.62) birthweight. The strongest effect on using ≥2 types of cardiovascular medications was found for low birthweight and poor health behaviors (OR = 3.14, 95% CI = 1.80, 5.50). CONCLUSION: This cohort study provides evidence that low birthweight increases the risk of using more types of cardiovascular medications in adulthood. This study also suggests that ideal health behaviors reduce this risk.

A probabilistic bias analysis on the magnitude of unmeasured confounding: The impact of driving mileage on road traffic crashes.

INTRODUCTION: Unmeasured confounding can lead to biased interpretations of empirical findings. This paper aimed to assess the magnitude of suspected unmeasured confounding due to driving mileage and simulate the statistical power required to detect a discrepancy in the effect of polypharmacy on road traffic crashes (RTCs) among older adults. METHODS: Based on Monte Carlo Simulation (MCS) approach, we estimated 1) the magnitude of confounding of driving mileage on the association of polypharmacy and RTCs and 2) the statistical power of to detect a discrepancy from no adjusted effect. A total of 1000 studies, each of 500000 observations, were simulated. RESULTS: Under the assumption of a modest adjusted exposure-outcome odds ratio of 1.35, the magnitude of confounding bias by driving mileage was estimated to be 16% higher with a statistical power of 50%. Only an adjusted odds ratio of at least 1.60 would be associated with a statistical power of about 80% CONCLUSION: This applied probabilistic bias analysis showed that not adjusting for driving mileage as a confounder can lead to an overestimation of the effect of polypharmacy on RTCs in older adults. Even considering a large sample, small to moderate adjusted exposure effects were difficult to be detected.