ABSTRACT
Background: Nausea and vomiting are common complaints in the first half of pregnancy. These symptoms can significantly affect a person's personal and professional life. Aromatherapy is one of the types of complementary medicine that is used in the treatment of nausea and vomiting
The objective of this study was to determine the effect of aromatherapy with peppermint oil on the severity of nausea and vomiting of pregnancy [NVP]
Methods: This was a single-blind clinical trial that was conducted on 56 pregnant women with mild to moderate severity of NVP and 6 to 20 weeks of gestational age. After the determination of gestational age and base severity of NVP in each woman, they were randomly assigned to one of the two groups: peppermint oil [n=28] or placebo [n=28]. Inhalation aromatherapy was done for four days and at the end of each day,they responded to the Pregnancy Unique Quantification of Emesis/Nausea questionnaire [PUQE]. The data obtained were analyzed with Mann-Whitney test and ANOVA with repeated measures using SPSS software version 22. Also, the level of significance was p<0.05
Results: Although the severity of NVP in each intervention group significantly decreased [p<0.001], the comparison of the severity of NVP during the study period and at the end of it was not statistically significant between the placebo and intervention groups
Conclusion: According to the possibility of neurological mechanisms causing NVP, the effect of aromatherapy with peppermint oil and placebo were the same in this study. This similarity can be due to psychological impacts of intervention on pregnant women
ABSTRACT
The aim of this study is to evaluate the association between different treatments and survival time of breast cancer patients using either standard Cox model or stratified Cox model. The study was conducted on 15830 women diagnosed with breast cancer in British Columbia, Canada. They were divided into eight groups according to patients' ages and stage of disease Either Cox's PH model or stratified Cox model was fitted to each group according to the PH assumption and tested using Schoenfeld residuals. The data show that in the group of patients under age 50 years old and over age 50 with stage ? cancer, the highest hazard was related to radiotherapy [HR= 3.15, CI: 1.85-5.35] and chemotherapy [HR= 3, CI: 2.29- 3.93] respectively. For both groups of patients with stage ?? cancer, the highest risk was related to radiotherapy [HR=3.02, CI: 2.26-4.03] [HR=2.16, CI: 1.85-2.52]. For both groups of patients with stage III cancer, the highest risk was for surgery [HR=0.49, CI: 0.33-0.73], [HR=0.45, CI: 0.36-0.57]. For patients of age 50 years or less with stage IV cancer, none of the treatments were statistically significant. In group of patients over age 50 years old with stage ?V cancer, the highest hazard was related to surgery [HR=0.64, CI: 0.53-0.78]. The results of this study show that for patients with stage I and II breast cancer, radiotherapy and chemotherapy had the highest hazard; for patients with stage III and IV breast cancer, the highest hazard was associated with treatment surgery
ABSTRACT
In interventional or observational longitudinal studies, the issue of missing values is one of the main concepts that should be investigated. The researcher's main concerns are the impact of missing data on the final results of the study and the appropriate methods that missing values should be handled. Regarding the role and the scale of the variable that missing values have been occurred and the structure of missing values, different methods for analysis have been presented. In this article, the impact of missing values on a binary response variable, in a longitudinal clinical trial with three follow up sessions has been investigated Propensity Score, Predictive Model Based and Mahalanobis imputation strategies with complete case and available data methods have been used for dealing with missing values in the mentioned study. Three models; Random intercept, Marginal GEE and Marginalized Random effects models were implemented to evaluate the effect of covariates. The percentage of missing responses in each of the treatment groups, throughout the course of the study, differs from 6.8 to 14.1. Although, the estimate of variance component in random intercept and marginalized random effect models were highly significant [p <0.001] the same results were obtained for the effect of independent variables on the response variable with different imputation strategies. In our study according to the low missing percentage, there were no considerable differences between different methods that were used for handling missing data