Variability demographic

The randomization data are used in both efficacy and safety analyses, as they are typically the key stratification variable for the trial. The randomization data allow you to answer the question of whether patients who are getting the study therapy fare better than the alternative. CDISC places treatment assignment information in the special demographics domain. 

Much like the previous demographics table example, the majority of the tables generated for clinical trial reporting compare a set of continuous and/or categorical variables across treatment groups. The purpose is to see whether therapy groups are comparable or whether they differ in some way. These kinds of tables include the following ... 

A number of methods and models have been used at sites to estimate potential risks from exposure to lead. One method is the use of prevalence data for estimating PbB levels. In this case, PbB measurements can be made at a site and extrapolated to other sites with similar environmental and demographic data. Limitations of this method include site-to-site variability with respect to, among other things, children s behavioral patterns, age, and bioavailability issues. Estimation of past exposures can be problematic because of redistribution of Pb out of the blood compartment since PbB is only an indicator of recent exposure (<90 days). 

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. 

ROCK C L, THORNQUIST M D, KRISTAL A R, PATTERSON R E, COOPER D A, NEUHOUSER M L, neumark-sztainer D and cheskin l j (1999), Demographic, dietary and lifestyle factors differentially explain variability in serum carotenoids and fat-soluble vitamins baseline results from the sentinel site of the Olestra Post-Marketing Surveillance Study , J Nutr, 129(4), 855-64. rodale (1996), The Prevention Index - a report card on the nation s health, 1996 summary report. Rodale Press, Inc, Emmaus, Pennsylvania. SANDLER R S, ZORICH N L, FILLOON T G, WISEMAN H B, LIETZ D J, BROCK M H, ROYER M G and MIDAY R K (1999), Gastrointestinal symptoms in 3181 volunteers ingesting snack foods containing olestra or triglycerides , Annals Internal Med, 130, 253-61. 

Pharmacokinetic concentration-time curves for a drug and ifs mefabolifes are used to identify primary exposure metrics such as AUC, or which are not time-dependent unlike the sequential measurements of concentration over time. A peak plasma concentration of a drug is often associated with a PD response, especially with an adverse event. There can be large inter-individual variability in the time-to-peak concentration, and closely spaced sampling times are often critical to determining the peak plasma concentration accurately in individual patients because of differences in demographics, disease states, and food effects, if any. All these elements are clearly spelled out in the protocols written to conduct these studies. 

The explanation of variability by identifying factors of demographic, pathophysiologic, environmental, or concomitant drug-related origin that may influence the PK behavior of a drug. 

Significant ethnic variability is observed with many pharmacogenetic markers (38). Consequently, the demographics of the study population need to be taken into account when performing power analyses to determine the appropriate sample size. If the frequency of the polymorphism to be studied is unknown in any of the ethnic groups included in the study, the polymorphism should be screened in relevant population controls to determine their allele frequency. This will help to narrow down the number of polymorphisms to be included and prevent using finite patient DNA to assess markers that may have low frequency or be absent from the study population. 

Dr. Pearson has written a compelling volume on the careers of African American doctorate holders in chemistry. He provides documentation that makes possible a critical assessment of the circumstances that governed the experiences of those African Americans who obtained the Ph.D. in chemistry since the beginning of the twentieth century. The information he gathered goes beyond simply statistics to indepth interviews that yield information not previously available in demographic studies of Black scientists. From such data. Dr. Pearson is able to provide new insight on factors that influenced the choice of chemistry as a career. He includes discussion of the variability of these factors as they impacted the lives of the individuals interviewed for this volume. 

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. ( )... 

By contrast, in the population approach, the raw data set that is analysed consists of concentration-time points (and other necessary data such as demographic information) taken from a large number (up to hundreds to thousands) of patients in Phase 11 and/or Phase 111 trials. The number of plasma samples per subject may be sparse but it is possible to estimate the individual pharmacokinetic characteristics of each subject and hence a measure of the mean parameters and their variability can be assessed. Relationships can be sought between patient characteristics (demographics, chnical status) and pharmacokinetic values is found, its consequence may be examined by looking for altered efficacy or safety which may not be possible in a traditional volunteer study. This might lead to demonstration of a therapeutic concentration range. 

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. 

---