Showing 4 results for Crossover
Z Rajabpoor, S.r Majdzadeh, A Feizzadeh Khorasani, A Motevalian, M Hoseini,
Volume 1, Issue 1 (12-2005)
Abstract
Background and Objective: Road traffic injuries are among the most important causes of death and disability in Iran, and the country has one of the highest prevalence of opioid drug use, especially among drivers. The effect of different situations related to opioid use needs great attention. The purpose of this study was to estimate the effect of driving in the withdrawal phase on the occurrence of traffic accidents leading to injury.
Materials and Methods: This is a Case-Crossover study on injured drivers of crashed motor vehicles in Kerman. Drivers having skipped one habitual drug dose within one hour of the driving session were considered as being in withdrawal. We compared the drivers' situation at the time of accident with their regular driving habits.
Results: Among 75 drivers who had history of regular use of opium, 15 were in withdrawal phase at the time of accident. The relative rate of occurrence of traffic injuries while driving in these circumstances was 2.67 (95% confidence interval: 1.52 - 4.68).
Conclusion: According to these findings we can conclude that habitual opioid users are at greater risk of traffic accidents while driving in withdrawal status this risk is more than two-fold relative to not being in withdrawal status.
M Qorbani, M Yunesian, A Fotouhi, H Zeraati, S Sadeghian, Y Rashidi,
Volume 3, Issue 1 (9-2007)
Abstract
Background & Objectives: Recent evidence suggests that long-term exposure to air pollution contributes to progression of atherosclerosis and the risk of cardiac morbidity and mortality short-term exposure may also lead to thrombosis and acute ischemic events. To evaluate the relation between the levels of major air pollutants (CO and PM10) and hospital admission for acute coronary syndrome (ACS) in Tehran, we performed a case-crossover design and checked whether individual characteristics act as effect modifiers.
Methods: We selected 250 Tehran residents who had been hospitalized with an acute coronary syndrome from 4th of April to 10th of June, 2007. The following individual data were gathered: sex, age, date of hospitalization, and coexisting illnesses (hypertension, diabetes). Daily air pollution data were taken from the Air Quality Control Center. Temperature, humidity, stress, physical activity and weekend days were treated as confounding variables, and a conditional logistic regression model was used for statistical analysis.
Results: We found a positive association between ACS and average 24-hour CO levels. The OR for each unit increase of the average 24-hour CO was 1.18 (95%CI: 1.03-1.34). The relation between ACS and 24-hour average PM10 did not reach statistical significance (OR for average 24-hour PM10 was 1.005, 95%CI: 0.99-1.01). The association between ACS and 24-hour average CO tended to be stronger in women (OR=1.68 for each unit increase, 95%CI: 1.25-2.26). The relation between 24-hour average PM10 and ACS did not change across the layers of the effect modifiers.
Conclusions: The results suggest that an increase in average 24-hour CO levels will augment the risk of ACS, and the effect is stronger in females. On the other hand, we were unable to document an association between ACS and average 24-hour PM10 levels.
M Qorbani, M Yunesian,
Volume 4, Issue 1 (4-2008)
Abstract
The case-crossover design was developed in the early 1990s to study the effects of transient, short-term exposures on the risk of acute events such as myocardial infarction. To estimate relative risk, the exposure frequency during a period just before outcome onset (hazard period) is compared with exposure frequency during control time(s) in that person rather than in a control. One or more "control times" are supplied by each of the cases themselves to control for confounding by constant characteristics and self-confounding between the trigger's acute and chronic effects.
In the analysis of case-crossover studies, exposure frequency in the hazard period is compared with the control period or the individual's usual frequency of exposure. The design has been used frequently for heart diseases, injuries and air pollution epidemiology. This review article looks at published case-crossover studies and is intended to help the reader gain a better understanding of the strengths and limitations of the case-crossover design in studying the epidemiology of injuries and air pollution.
Ar Soltanian, S Faghihzadeh, A Gerami, D Mehdibarzi, J Jing Cheng,
Volume 6, Issue 1 (6-2010)
Abstract
Background & Objectives: In clinical trials some of participants do not take assignment treatment. Intention-to-treat (ITT) is one of the strategies to analyze of clinical trials with control. ITT estimation will be invalid and incorrect to show of treatment effects in case of existing non-compliance in participants. In this study we adjusted noncompliance effect to compare of active treatment and placebo.
Methods: To demonstrate efficiency of proposed model, a dataset of crossover clinical trial with 42 patients with knee osteoarthritis was used. To estimate the non-compliance’s effect adjusting at comparison of treatment effects, we use mean of compliance proportion at periods in sequences. The parameters were estimated by maximum likelihood method. ( could you ask authors to have a look at what they wrote and compare with Farsi version)
Results: The results show that baseline variables distributions like duration of disease, severity of disease, age and sex, were not significant (p>0.05). The standard error estimation of treatment effects ( ) based on adjusted model were less than standard model (0.09 and 0.12, respectively). In addition, likelihood ratio statistics based on adjusted model were less than standard model (1177.7 versus 1205.1).
Conclusion: Based on estimation of standard errors and likelihood ratio statistics at adjusted and standard models, we observe that adjusted model is more efficient than standard model.
