Gender data

So it is with gender data it is collected, analyzed and tabulated by each study and by each drug, but data on drugs of the same class and between each... 

It has also been claimed (because gender data are rarely mentioned in clinical studies, papers or reports) that gender differences are not sought. This presupposes that data are neither collected nor examined. In fact, the opposite is much more likely 94% of surveyed pharmaceutical firms were found to collect gender data in their studies (Edwards, 1991). The reality is that... 

Verheul et al. (2004) pooled data from seven European acamprosate studies in an effort to identify patient-related predictors of response to the medication. Although they examined a number of potential predictors, including patients level of physiological dependence before treatment, family history of alcoholism, age of onset of alcoholism, baseline anxiety symptom severity, baseline craving, and gender, none was shown to interact with acamprosate treatment. These findings led the authors to conclude that, although the effect size for acamprosate was moderate, the medication can be considered potentially effective for all patients with alcohol dependence. 

O In the PROC REPORT statement, if the MISSING option had been excluded, then the data for subject 712 would have been wrongfully omitted from the output for all summarized variables, including age, gender, and race. 

FORMAT GENDER N(%) AS DESIRED. data gender set gender ... 

TRANSPOSE THE GENDER SUMMARY STATISTICS. proc transpose data = gender... 

CREATE GENDER FIRST ROW FOR THE TABLE. data label ... 

APPEND gender descriptive statistics to gender p value row AND CREATE GENDER DESCRIPTIVE STATISTIC ROW LABELS. data gender ... 

CONCATENATE AGE, GENDER, AND RACE STATISTICS AND CREATE GROUPING GROUP VARIABLE FOR LINE SKIPPING IN PROC REPORT. data forreport ... 

O This is the sample pain data set with a dependent variable called success, which is 1 if the patient achieved clinical success and 0 otherwise. The treatment, gender, race, and baseline pain scores serve as the independent variables. 

Note that with PROC PHREG all covariates need to be numeric, so treatment and gender need to be numeric. The p-values and hazard ratios that are useful for your statistical tables can be found in the ProbChiSq and HazardRatio variables, respectively, in the pvalue data set. 

In order to determine the optimal number of compartments, literature information on small intestinal transit times was utilized. A total of over 400 human small intestinal transit time data were collected and compiled from various publications, since the small intestinal transit time is independent of dosage form, gender, age, body weight, and the presence of food [70]. Descriptive statistics showed that the mean small intestinal transit time was 199 min with a standard deviation of 78 min and a 95% confidence interval of 7 min. The data set was then analyzed by arranging the data into 14 classes, each with a width of 40 min. Figure 9 shows the distribution of this data set. 

The XtraFOOD model calculates as output the food intake and resulting contaminant intake, independently for age and gender categories. Exposure can be calculated as being representative for a population or separately for local and background intake. All these intakes are linked to the model output. Additional intakes are provided to add concentration data in non-farm-related foods (e.g. fruit juice, fish, etc.). 

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