Demographic variables

Although numerous demographic variables were explored, individually and in combination, the only three variables with consistent statistical significance were race/ethnicity, income, and education. Age and gender were relevant to a lesser degree. Virtually none of the other variables, including health status, was of statistical significance. 

One of the objectives of the Broad Scan program was to make comparisons of residue level distributions across geographic regions and, if possible, certain demographic variables. This required the selection of an appropriate statistical model and approach to the analysis. ( )... 

Electroconvulsive therapy may be administered using unilateral or bilateral electrode placement. Either mode requires consideration of seizure threshold. Several studies involving adults have shown that neither age nor other demographic variables are good predictors of seizure threshold (Coffey et ah, 1995 Enns and Kar-velas, 1995). Knowledge of seizure threshold may be... 

Rosen and colleagues (48) administered a structured interview, based on the Schedule for Affective Disorders and Schizophrenia (SADS) to 89 bipolar I patients, to compare psychotic and nonpsychotic manic patients on a number of clinical outcome and demographic variables (i.e., age, age at first treatment, and duration of illness). Overall, the psychotic manic group had a significantly poorer outcome in terms of social functioning. 

Karlsen KH, Larsen JP, Tandberg E, Maeland JG. Influence of clinical and demographic variables on quality of life in patients with Parkinson s disease. J Neurol Neurosurg Psychiatry 1999 66 431 -35. 

Twenty subjects (nine men and 11 women) with a mean age of 38 years took part in this study. Of those subjects, 17 completed all treatments (placebo, loratadine, and Herbal Blend) three subjects who were unable to attend all test sessions were excluded from pair-wise analyses on a per-comparison basis. The demographic variables for subjects in this study are listed in Table 10.5. 

It is important to minimize or control for the impact of confounding variables in any study. In order to do so, it is imperative that all possible variables that may have an effect on the primary outcome be identified. These usually include demographic variables such as age, sex, income level, education level, and ethnicity. Health-related variables such as comorbidities and severity of illness should also be recorded. Variables also may be identified that cannot be measured. These may include outside education, changes in family structure or support system, and drastic changes in health status not related to the pertinent disease state. 

Cynthia has also discovered that heart failure is the most common hospital discharge diagnosis in individuals over age 65. Median survival rate from the time of diagnosis is 1.7 years in men and 3.2 years in women (Ho et al., 1993). Cynthia knows that she will have to collect some demographic variables as well as the variables that directly address her objectives. Pertinent demographic variables may include age, gender, marital status, ethnicity, income level, and mental status. 

The use of travel style variables, like the demographic variables already discussed, is usually not considered in isolation but forms a part of the explanatory framework for studies of tourists experiencing particular kinds of products. A product-based approach to classifying tourists will now be considered. 

Three basic variables - domestic travel experience, international travel experience and age - were taken into account in an initial attempt to characterise travel experience levels and to consider dimension of life-cycle and experience. A set of cluster analyses established that two distinct groups, an experienced and an inexperienced traveller group, were present in the sample. These groups also differed on a range of other demographic variables. These differences are reported in Table 3.4. 

MODIFICATION OF THE METHOD Data from individuals drawn from a target population are not completely independent. Concentration time curves (longitudinal data) of a subject are considered to be driven by a functionality depending on individual parameter values. But what is the connection between the same parameters in different persons Parts of it may be described by a functionality depending on demographic variables. In any case, unexplained intra and inter individual random effects remain. Mixed effect modeling clearly distinguishes between these two sources of randomness. 

Ereshefsky L, Saklad SR, Watanabe MD, Davis CM, Jann MW. Thiothixene pharmacokinetic interactions a study of hepatic enzyme inducers, clearance inhibitors, and demographic variables. J Clin Psychopharmacol 1991 11(5) 296-301. 

The optimal plasma concentration of clozapine is 200-350 ng/ml, which usually corresponds to a daily dose of 200-400 mg (233,234). A nomogram to predict clozapine steady-state plasma concentrations has been generated using data from 71 patients (235). Clozapine steady-state plasma concentrations and demographic variables were obtained. The model explained 47% of the variance in clozapine concentrations. Two equations were obtained to predict steady-state plasma concentrations, one for men and one for women ... 

Model 101 allows the creation of smaller subsets of activity pattern data from larger sets. These subsets are based on selected demographic variables and estimate summaries of duration by location and by activity. Model 102 generates simulated location patterns (e.g. location distribution and duration) for any number of people, based on actual location patterns obtained from field studies, since the sample sizes for field studies are usually restricted by economic and logistic constraints. Outputs from Model 101 can be used as input to Model 102, and the simulated location patterns can be used as input by the two inhalation-exposure-related Models 107 aud 108. 

Placebos have been found to have effects similar to the active medication being compared.Many investigators have attempted to quantify the placebo response. Scales such as the placebo effect scale (PES) and the side effect scale (SES) have been used to identify patients likely to exhibit placebo effects. These scales determine possible predictors of placebo response such as demographic variables, illness characteristics, and diagnostic and symptom variables to identify patients at increased benefit or risk of demonstrating a placebo response.The need for routine use of such scales prior to inclusion in clinical trials and implications of identifying those most susceptible to placebo response require further study. 

Demographic variables, such as age structure, population density and urban-rural ratios, may also affect prevalence, and socioeconomic factors, like unemployment, education or income, correlate with prevalence levels in some, but not all, studies. Factors relating to geographic diffusion, such as trafficking routes, may also have an influence (see box above). 

Several demographic variables were collected however, weight, ethnic origin, and a-l-acid glycoprotein (AAG) were considered the more likely covariates to influence pharmacokinetics, based on previous data (15). A summary of demographics in actual data obtained for each study is given in Table 16.2. 

When modeling real data, an important first step is to carefully consider and specify the intended use of the model to be developed. With this in hand, an appropriate strategy and analysis plan can be crafted to ensure the appropriateness of the model (15). For the purposes of this chapter, a simulated data set is used to mimic a typical Phase 2 trial of a novel compound where an endpoint measurement is collected at each study visit. In this case, the endpoint of interest is the presence or absence of a particular adverse event. Various demographic variables are available for possible correlation with the endpoint in addition to a calculated measure of individual exposure to the drug. 

FIGURE 32.2 Comparison of distributions of demographic variables between the real and simulated data sets (adapted from Ref. 2). Ml to MIO represent the number of replications used for data supplementation. See Appendix 32.2 for a sample S-Plus code. 

FIGURE 39 1 Collinearity of various demographic variables in pediatric patients. Of special importance are the collinearities of body surface area (BSA), weight (WTKG), height (HTCM), and age. 

Equations to estimate creatinine clearance or GFR are commonly used in ambulatory and inpatient settings, and incorporate patient demographic variables such as serum creatinine, age, gender, weight, and ethnicity. 

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