Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

Repeated Measures of Modified Rankin Scale Scores to Assess Functional Recovery From Stroke: AFFINITY Study Findings

Background

Function after acute stroke using the modified Rankin Scale (mRS) is usually assessed at a point in time. The analytical implications of serial mRS measurements to evaluate functional recovery over time is not completely understood. We compare repeated‐measures and single‐measure analyses of the mRS from a randomized clinical trial.

Methods and Results

Serial mRS data from AFFINITY (Assessment of Fluoxetine in Stroke Recovery), a double‐blind placebo randomized clinical trial of fluoxetine following stroke (n=1280) were analyzed to identify demographic and clinical associations with functional recovery (reduction in mRS) over 12 months. Associations were identified using single‐measure (day 365) and repeated‐measures (days 28, 90, 180, and 365) partial proportional odds logistic regression. Ninety‐five percent of participants experienced a reduction in mRS after 12 months. Functional recovery was associated with age at stroke <70 years; no prestroke history of diabetes, coronary heart disease, or ischemic stroke; prestroke history of depression, a relationship partner, living with others, independence, or paid employment; no fluoxetine intervention; ischemic stroke (compared with hemorrhagic); stroke treatment in Vietnam (compared with Australia or New Zealand); longer time since current stroke; and lower baseline National Institutes of Health Stroke Scale & Patient Health Questionnaire‐9 scores. Direction of associations was largely concordant between single‐measure and repeated‐measures models. Association strength and variance was generally smaller in the repeated‐measures model compared with the single‐measure model.

Conclusions

Repeated‐measures may improve trial precision in identifying trial associations and effects. Further repeated‐measures stroke analyses are required to prove methodological value.

Registration

URL: http://www.anzctr.org.au; Unique identifier: ACTRN12611000774921.

Model-assisted analyses of longitudinal, ordinal outcomes with absorbing states

Studies of critically ill, hospitalized patients often follow participants and characterize daily health status using an ordinal outcome variable. Statistically, longitudinal proportional odds models are a natural choice in these settings since such models can parsimoniously summarize differences across patient groups and over time. However, when one or more of the outcome states is absorbing, the proportional odds assumption for the follow-up time parameter will likely be violated, and more flexible longitudinal models are needed. Motivated by the VIOLET Study (Ginde et al), a parallel-arm, randomized clinical trial of Vitamin D 3 in critically ill patients, we discuss and contrast several treatment effect estimands based on time-dependent odds ratio parameters, and we detail contemporary modeling approaches. In VIOLET, the outcome is a four-level ordinal variable where the lowest "not alive" state is absorbing and the highest "at-home" state is nearly absorbing. We discuss flexible extensions of the proportional odds model for longitudinal data that can be used for either model-based inference, where the odds ratio estimator is taken directly from the model fit, or for model-assisted inferences, where heterogeneity across cumulative log odds dichotomizations is modeled and results are summarized to obtain an overall odds ratio estimator. We focus on direct estimation of cumulative probability model (CPM) parameters using likelihood-based analysis procedures that naturally handle absorbing states. We illustrate the modeling procedures, the relative precision of model-based and model-assisted estimators, and the possible differences in the values for which the estimators are consistent through simulations and analysis of the VIOLET Study data.

Predicting preference-based utility values using partial proportional odds models

BACKGROUND

The majority of analyses on utility data have used ordinary least square (OLS) regressions to explore potential relationships. The aim of this paper is to explore the benefits of response mapping onto health dimension profiles to generate preference-based utility scores using partial proportional odds models (PPOM).

METHODS

Models are estimated using EQ-5D data collected in the Health Survey for England and the predicted utility scores are compared with those obtained using OLS regressions. Explanatory variables include age, acute illness, educational level, general health, deprivation and survey year. The expected EQ-5D scores for the PPOMs are obtained by weighting the predicted probabilities of scoring one, two or three for the five health dimensions by the corresponding preference-weights.

RESULTS

The EQ-5D scores obtained using the probabilities from the PPOMs characterise the actual distribution of EQ-5D preference-based utility scores more accurately than those obtained from the linear model. The mean absolute and mean squared errors in the individual predicted values are also reduced for the PPOM models.

CONCLUSIONS

The PPOM models characterise the underlying distributions of the EQ-5D data better than models obtained using OLS regressions. Additional research exploring the effect of modelling conditional responses and two part models could potentially improve the results further.

Regression analysis of ordinal stroke clinical trial outcomes: an application to the NINDS t-PA trial

BACKGROUND

The modified Rankin scale (mRS) is the most common functional outcome assessed in stroke trials. The proportional odds model is commonly used to analyze this ordinal outcome but it requires a restrictive assumption that a single odds ratio applies across the entire outcome scale.

AIMS

The study aims to model the effect of tissue-type plasminogen activator on ordinal mRS, test model assumptions, and compare fits and predictive ability of the statistical models.

METHODS

Several ordinal regression methods are presented and applied to a re-analysis of the 1995 NINDS tissue-type plasminogen activator study. Violations of the proportional odds assumption are demonstrated using graphs and statistical tests, and the partial proportional odds model is introduced and recommended as an alternative for the analysis of mRS.

RESULTS

The partial proportional odds model relaxes the assumptions about treatment effect on the ordinal outcome scale and provides a better fit to the data than the commonly used proportional odds model (likelihood ratio test chi-square = 8·05, P = 0·005). It provides easily interpretable odds ratios and it is able to detect efficacy at the lower end and a lack of efficacy at the upper end of the mRS scale. Further, it provides lower prediction error than the proportional odds model (0·002 versus 0·005).

CONCLUSIONS

Assuming proportional odds when it does not hold can mask differential treatment effects at the upper end of the ordinal mRS scale and has implications for reduced power when studies are designed under this assumption.