ordinal outcome
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2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Saeed Akhtar ◽  
Eisa Aldhafeeri ◽  
Farah Alshammari ◽  
Hana Jafar ◽  
Haya Malhas ◽  
...  

Abstract Background The aims of this cross-sectional study were to i) assess one-year period prevalence of one, two, three or more road traffic crashes (RTCs) as an ordinal outcome and ii) identify the drivers’ characteristics associated with this ordinal outcome among young adult drivers with propensity to recurrent RTCs in Kuwait. Methods During December 2016, 1465 students, 17 years old or older from 15 colleges of Kuwait University participated in this cross-sectional study. A self-administered questionnaire was used for data collection. One-year period prevalence (95% confidence interval (CI)) of one, two, three or more RTCs was computed. Multivariable proportional odds model was used to identify the drivers’ attributes associated with the ordinal outcome. Results One-year period prevalence (%) of one, two and three or more RTCs respectively was 23.1 (95% CI: 21.2, 25.6), 10.9 (95% CI: 9.4, 12.6), and 4.6 (95% CI: 3.6, 5.9). Participants were significantly (p < 0.05) more likely to be in higher RTCs count category than their current or lower RCTs count, if they habitually violated speed limit (adjusted proportional odds ratio (pORadjusted) = 1.40; 95% Cl: 1.13, 1.75), ran through red lights (pORadjusted = 1.64; 95%CI: 1.30, 2.06), frequently (≥ 3) received multiple (> 3) speeding tickets (pORadjusted = 1.63; 95% CI: 1.12, 2.38), frequently (> 10 times) violated no-parking zone during the past year (pORadjusted = 1.64; 95% CI: 1.06, 2.54) or being a patient with epilepsy (pORadjusted = 4.37; 95% CI: 1.63, 11.70). Conclusion High one-year period prevalence of one, two and three or more RTCs was recorded. Targeted education based on identified drivers’ attributes and stern enforcement of traffic laws may reduce the recurrent RTCs incidence in this and other similar populations in the region.


2021 ◽  
Author(s):  
Benjamin Domingue ◽  
Klint Kanopka ◽  
Sam Trejo ◽  
Jeremy Freese

Years of education is a commonly used outcome variable in many lifecourse studies. We argue that such studies may derive additional insights from a treatment of years of education as an ordinal outcome rather than the standard treatment using the linear model. Via simulation, we show that the ordinal approach performs well if the linear model is actually the true model while, in the reverse scenario, estimates from the linear model may be somewhat suboptimal when the ordinal model is the true model. We use data from the Health and Retirement Study to illustrate additional insights that are readily derived from application of the ordinal model and offer a suggested workflow for future analysis.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Payam Amini ◽  
Abbas Moghimbeigi ◽  
Farid Zayeri ◽  
Leili Tapak ◽  
Saman Maroufizadeh ◽  
...  

Associated longitudinal response variables are faced with variations caused by repeated measurements over time along with the association between the responses. To model a longitudinal ordinal outcome using generalized linear mixed models, integrating over a normally distributed random intercept in the proportional odds ordinal logistic regression does not yield a closed form. In this paper, we combined a longitudinal count and an ordinal response variable with Bridge distribution for the random intercept in the ordinal logistic regression submodel. We compared the results to that of a normal distribution. The two associated response variables are combined using correlated random intercepts. The random intercept in the count outcome submodel follows a normal distribution. The random intercept in the ordinal outcome submodel follows Bridge distribution. The estimations were carried out using a likelihood-based approach in direct and conditional joint modelling approaches. To illustrate the performance of the model, a simulation study was conducted. Based on the simulation results, assuming a Bridge distribution for the random intercept of ordinal logistic regression results in accurate estimation even if the random intercept is normally distributed. Moreover, considering the association between longitudinal count and ordinal responses resulted in estimation with lower standard error in comparison to univariate analysis. In addition to the same interpretation for the parameter in marginal and conditional estimates thanks to the assumption of a Bridge distribution for the random intercept of ordinal logistic regression, more efficient estimates were found compared to that of normal distribution.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
I. E. Ceyisakar ◽  
N. van Leeuwen ◽  
Diederik W. J. Dippel ◽  
Ewout W. Steyerberg ◽  
H. F. Lingsma

Abstract Background There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. Methods We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. Results In the IMPACT study (9578 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37 to 63% less patients. Conclusions Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements. Trial registration We do not report the results of a health care intervention.


Author(s):  
Nelson Lee ◽  
Stephanie W Smith ◽  
David S C Hui ◽  
Ming Ye ◽  
Nathan Zelyas ◽  
...  

Abstract Background An obstacle in influenza therapeutics development is the lack of clinical endpoints, especially in hospitalized patients. A single time-point binary outcome measure is limited by patients’ diverse clinical trajectories and low event rates. Methods A 6-point ordinal scale with ascending clinical status severity (scoring: discharged; subacute care; acute care without/with respiratory failure; intensive care unit [ICU]; death) was proposed to study outcomes of adults hospitalized with influenza. Individual patient data from 2 active surveillance cohorts’ datasets (2015/2016−2017/2018; Edmonton, Hong Kong) was used for evaluation. The impact of neuraminidase inhibitor (NAI) treatment on longitudinal ordinal outcome changes over 30 days was analyzed using mixed-effects ordinal logistic regression and group-based trajectory models. Results Patient (n = 1226) baseline characteristics included age (mean 68.0 years), virus-type (A 78.1%, B 21.9%), respiratory failure (57.2%), ICU admittance (14.4%), and NAI treatment within 5 days of illness (69.2%). Outcomes at 30 days included discharged (75.2%), subacute care (13.7%), acute care (4.5%), and death (6.6%). Two main clinical trajectories were identified, predictive by baseline scoring (mean ± SD, 4.3 ± 0.6 vs 3.5 ± 0.6, P &lt; .001). Improved outcomes with NAI treatment within 5 days were indicated by significantly lower clinical status scores over time (unadjusted odds ratio [OR], 0.53; 95% confidence interval [CI], .41−.69; P &lt; .001; adjusted OR, 0.62; 95% CI, .50−.77; P &lt; .001, for baseline score, age, and within-patient correlations). In subanalysis, influenza vaccination was also associated with lower scores (adjusted OR, 0.67; 95% CI, .50−.90; P = .007). Analyses of binary endpoints showed insignificant results. Conclusions The ordinal outcome scale is a potentially useful clinical endpoint for influenza therapeutic trials, which could account for the diverse clinical trajectories of hospitalized patients, warranting further development.


2020 ◽  
Author(s):  
Iris E. Ceyisakar ◽  
Nikki van Leeuwen ◽  
Diederik W.J. Dippel ◽  
Ewout W. Steyerberg ◽  
Hester F. Lingsma

Abstract Background There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. Methods We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. Results In the IMPACT study (8,799 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1,657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37% to 63% less patients.Conclusions Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements.


2020 ◽  
Author(s):  
Iris E. Ceyisakar ◽  
Nikki van Leeuwen ◽  
Diederik W.J. Dippel ◽  
Ewout W. Steyerberg ◽  
Hester F. Lingsma

Abstract Background There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. Methods We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples. We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers. In the IMPACT study (8,799 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale (‘unfavorable outcome’), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1,657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37% to 63% less patients. Conclusion Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements.


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