Study Variables Sample Clauses
The "Study Variables" clause defines the specific data points, measurements, or parameters that will be collected and analyzed during a research study. It typically outlines which variables are primary and secondary, how they will be measured, and the methods or instruments used for data collection. By clearly specifying these variables, the clause ensures that all parties have a shared understanding of the study's focus and methodology, thereby promoting consistency and reliability in the research process.
Study Variables. We included patient’s demographics, disease conditions, Medicaid enrollment status and resident county characteristics from our databases to examine their association with the receipt of cervical cancer treatment. Patient’s covariates included: 1) age at Medicaid enrollment, 2) race/ethnicity, 3) stage at diagnosis, 4) ▇▇▇▇▇▇▇▇ comorbidity index, 5) pre- enrolled in Medicaid, 6) Medicaid eligibility category and 7) length of Medicaid enrollment. The stage data were from either GCCR or based on Medicaid claims for services received in or after their month of diagnosis and enrollment. The stage system we used here is the Surveillance, Epidemiology, and End Results (SEER) Program summary stage which can group cases into five main categories: 1) in situ, 2) local, 3) regional, 4) distant and 5) unstaged. It can also be derived from the ICD-9-CM codes in medical claims which would help us to identify stage for those not from GCCR. The full list of codes for identifying cervical cancer stage from the claims is available upon request. To adjust the severity of a non-cancer medical illness which might affect the treatment options, we adopted ▇▇▇▇▇▇'▇(▇▇▇▇▇▇, Roost et al. 1993) modification of the comorbidity index originally developed by ▇▇▇▇▇▇▇▇(▇▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇ et al. 2000; ▇▇▇▇▇▇▇▇, ▇▇▇▇▇▇ et al. 2002). All available medical claims up to one year after the first month of Medicaid enrollment were used to compute the ▇▇▇▇▇▇▇▇ comorbidity index and patients were assigned the maximal score observed. Because reasons for enrollment in Medicaid vary, we hypothesized that persons who enrolled in Medicaid before cancer diagnosis would be a distinct group compared with persons who enrolled in Medicaid after their cancer diagnosis. Thus, we created a dichotomous variable “pre-enrolled” to indicate whether subjects were previously enrolled in Medicaid four or more months before the month of their cancer diagnosis as recorded in the GCCR. Medicaid eligibility category was defined based on the most frequent eligibility category during the initial six months of enrollment and classified into three categories: 1) BCCPTA,
Study Variables. The primary exposure of interest was residence in a state that expanded Medicaid (expansion state) vs. a state that did not (non-expansion state) The main outcome under study was overall survival (OS) with survival time, defined as months from diagnosis to death from any cause or last contact. Cause-specific mortality is not available in the NCDB. Other participant characteristics included year of diagnosis, age at diagnosis, race, sex, median household income (based on patient’s zip code and American Community Survey data), educational attainment (based on percentage of adults who did not graduate from high school residing in the patient’s zip code), and rural/urban/metro residency (based on patients’ zip code). Clinical stage of esophageal cancer was defined based on the American Joint Committee on Cancer (AJCC) guidelines. The AJCC guidelines on staging consist of four different types of staging: clinical staging, pathological staging, post-therapy or post-neoadjuvant therapy staging, and restaging. The T, N, M staging system, maintained by the AJCC, is the classification system tool for doctors to stage different cancer types based on standardized criteria based on the extent of the primary tumor (T), the extent of spread to the lymph nodes (N), and presence of metastasis (M). The T, N, and M categories analyze characteristics from tumor/lymph node/metastasis cannot be evaluated to the size of the tumor and extent of spread. Once T, N, and M are determined, they are combined, and an overall stage of 0, I, II, III, IV is assigned. 72 Descriptive statistics, expressed as frequencies and percentages, were generated to compare study participants residing in expansion and non-expansion states with respect to distributions of demographic, socioeconomic characteristics. Multivariable survival analyses of the association between residence in a Medicaid expansion state and all-cause mortality following esophageal cancer diagnosis were performed using ▇▇▇ proportional hazards models. To assess influence of various confounding factors three versions of the Cox model were used:
Study Variables. The response variable was physicians agreement classified as 1=Agree and 0=Disagree. This physician agreement is affected by the explanatory variables are classified as deceased related, respondent related and geographically related variables [10-12]. The conceptual framework (see figure) was used to notice the effect of these factors on physician agreement.
Study Variables. Several variables included in this study are the administrative region and nationality. Others include the age group and the gender of the participants. The nationality consists of the KSA nationals and foreigners (non-Saudis). The age groups were divided into five: less than one year, 1 – 5 years, 5 – 15 years, 16 – 44 years, and > 45 years of age. This study included data from different administrative regions across KSA (Northern Border, Riyadh, Aseer, Eastern, and Quassim. Others are Najran, Makkah, Al-Jouf, Hail, ▇▇▇▇▇▇▇, Al-Baha, and Madinah in addition to Jazan). Descriptive analyses were performed to determine the burden of HBV in the population and the rate of disease occurrence. We calculated the incidence rates (IRs) per 100,000 persons > five years (2014 – 2018) based on the different variables (i.e., gender, age, nationality, and administrative region). Similarly, the number of cases was calculated over the period 2014 –2018. ▇▇▇▇-▇▇▇▇▇▇▇ test is a nonparametric test used to determine if there is a significant difference between two independent groups. This comparison was based upon the confidence interval for the sample mean calculated. If the p-value is less than 0.05, there is a significant difference between the two values at that 95 % confidence level. This research involved secondary data analyses without personal identifiers. Thus, it did not meet the definition of human subject’s research and was classified as exempt by the Emory University Institutional Review Board.
Study Variables. Based on the expectation that the concentration of LA in tissue will tend to increase over time as a function of the long-term cumulative exposure pattern, it is unlikely that tissue burdens will vary substantially due to short-term fluctuations in media concentrations. Thus, the timing of the animal collection is primarily based on ease of sample collection and permit requirements. Collection of fish occurred in August 2012 and collection of game animals will likely occur in the fall of 2012.
Study Variables. For ease of implementation, duff samples will be collected from the same general area where tree bark samples are collected.
Study Variables. The incidence rate of disenrollment (per 100 person-months) was defined as the total number of disenrollees divided by the sum of the months that individuals were enrolled and, hence, exposed to the risk of disenrolling. The denominator was the length of enrollment measured in months from the first month of enrollment in Medicaid to the first month of disenrollment from Medicaid, death, turned 65, or end of the study period. The numerator was the number of new disenrollees that occurred during a period of Medicaid enrollment experienced by the population at risk. This numerator and denominator was calculated for women in each of the three cancer groups as an unadjusted rate. Then, the adjusted (for covariates) rate was measured from DID estimation. As noted, we used DID analysis to estimate the net effect of BCCPTA. To do this we created three dummy variables for the estimation: 1) treatment versus control cancers, 2) pre- versus post-BCCPTA period, and 3) the interaction of dummy variables 1 and 2. This interaction dummy measures the difference in the change pre- versus post-BCCPTA for the treatment versus the control group and hence, is the DID estimator. The DID methodology has been used for policy analysis by a number of researchers (▇▇▇▇▇, ▇▇▇▇▇▇▇▇, ▇▇▇▇▇▇, ▇▇▇▇▇▇, & ▇▇▇▇▇, 2003; ▇▇▇▇▇ et al., 2009; ▇▇▇▇▇ & ▇▇▇▇▇▇, 2003; ▇▇▇▇▇, ▇▇▇▇▇, ▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇- ▇▇▇▇, & ▇▇▇▇▇, 2007; ▇▇▇-▇▇▇▇▇, ▇▇▇▇▇▇, & LoSasso, 2001). We also included a set of covariates that could affect our outcome but that were considered independent of BCCPTA. Individual covariates were: 1) age at diagnosis, 2) race/ethnicity, 3) marital status, and 4) stage at diagnosis. County covariates were: 1) geographic area of residence, 2) presence of a teaching hospital, 3) percentage small firms (<10 employees), 4) percentage of service employers, and 5) percentage of Medicaid recipients within the county. These factors were hypothesized to be area characteristics that reflected alternative insurance options and community norms or preferences regarding those in need of Medicaid coverage. Finally, dummy variables for year at diagnosis were included to control for secular changes.
Study Variables. Information gathered on our study population included demographic data (age, marital status, education, number of children, occupation, monthly income), quitting attempt status (first attempt, or had previous attempts), number of attempts, duration of smoking (months), body mass index (BMI), reasons for initiation, reasons for willingness or unwillingness to quit, and reasons for relapse (in case of multiple previous attempts), as well as presence of a smoker in the family. Weight and height were collected separately and BMI was calculated from the self- reported weight and height. Participants were asked to identify their reasons for smoking initiation using a 5-point Likert scale in which they indicated the importance of any individual reason, with 1 indicating the lowest agreement and 5 indicating the highest agreement. There were six categories of reasons: (a) friends, (b) social imitation, (c) family members, (d) stress, (e) advertising, and (f) others. Two kinds of measurement for attempted quitting were collected. One measure, a dichotomous measure, was whether a woman had any history of attempts of quitting. The other measure, a continuous measure, traced the actual number of quitting attempts. Participants were asked about the reasons they were willing to quit smoking using a 5- point Likert scale in which they indicated the importance of any individual reason, with 1 indicating the lowest agreement and 5 indicating the highest agreement. There were six categories of reasons: (a) health, (b) money savings, (c) religious beliefs, (d) familial pressure,
Study Variables. The unit of analysis was a comparison be- tween a control and a treatment phase. There were 133 comparisons, yielding a mean of 1.7 compar- isons across 80 studies. The first author reviewed all studies and scored each comparison on the fol- lowing study variables: (a) year of publication, (b) participants’ age in years, (c) gender, (d) diagnosis,
Study Variables. 4.13.1. Independent variable