Statistical Methods Clause Examples
Statistical Methods. In the database congenital malformations are classified through a standard coding system by organ system into specific categories or into non-specific categories if no details are known. Eight different organ systems are distinguished for which there are 51 specified and 20 unspecified categories of congenital malformations. Logistic regression models were used to study the relationship between maternal ethnicity and congenital malformations. The overall relationship between ethnic group and the total prevalence of congenital malformations, the total prevalence within the eight organ systems and the prevalence of some specific congenital malformations was determined with the Likelihood Ratio Test (LRT). If this test was significant it showed the existence of an overall relationship between ethnicity and congenital malformations. Thereafter, the individual significance of the calculated odds ratios (ORs) expressing the observed risk differences in prevalence between the different ethnic groups and the Dutch group, used as reference group, was studied. Because maternal age is related to the ethnic group and to the occurrence of certain congenital malformations, we calculated the ORs both unadjusted and adjusted for the age of the mother. Because the prevalence of some malformations was low, even in this 5-year birth cohort, not all could be tested. We decided that the predicted number of malformations had to be at least 5 in each ethnic group to perform a worthwhile and clinically significant test. Therefore, from the 51 specific malformations registered in the linked national database only the following 15 were analysed for possible differences between ethnic groups: neural tube defects (NTD); congenital malformations of the ears; ventricular septal defect; single umbilical artery; cleft lip with/without cleft palate; cleft palate without cleft lip; intestinal/anorectal atresia; hypospadias and/or epispadias; undescended testes; polydactyly; syndactyly; deformities of the foot without NTD; Down’s syndrome; other chromosomal malformations; and multiple malformations. Many comparisons were performed to test for a possible ethnic difference in prevalence of any congenital malformations. To avoid chance findings resulting from to multiple testing we applied a Bonferroni correction in which the usual critical value of 0.05 is adapted to a lower one depending on the number of tests performed. For example, to determine in which of the ethnic groups a possible diff...
Statistical Methods. Two statistical methods were used to perform the GWAS, namely weighted single-step GBLUP (WssGBLUP; ▇▇▇▇ et al. 2012) and ▇▇▇▇▇▇ (▇▇▇▇▇▇ et al. 2011). The model adopted for WssGBLUP was: y=1µ+Zaa+e, where y is the vector of phenotypes, µ is the overall mean, a is the vector of additive genetic effects, 1 is a vector of ones, Za is an incidence matrix relating the phenotypes to a, and e is the vector of residuals. The covariance between a and e was assumed to be zero and their variances were considered to be Hσ 2 and Iσ 2, respectively, where σ 2 and σ 2 are the direct additive and residual variance, respectively, H is the matrix which combines pedigree and genomic information (▇▇▇▇▇▇▇ et al. 2010), and I is an identity matrix. The SNP effects (û) were calculated as in Stranden & ▇▇▇▇▇▇▇ (2009): û=DP’[PDP’]-1ag, where D is a diagonal matrix that contains the weights for the SNPs, P is a matrix relating genotypes of each locus (coded as 0, 1 or 2 according to the number of copies of allele B) and ag is a vector with the estimated breeding values of genotyped animals. D, â and û were iteratively recomputed over three iterations. In the first iteration (w1), the diagonal elements of D (di) were assumed to be 1 (i.e., the same weight for all markers). For the subsequent iterations (w2 and w3), di was calculated as: di=ûi2pi(1-pi), where ûi is the allele substitution effect of the ith marker, estimated from the previous iteration, and pi is the allele frequency of the second allele of the ith marker. The WssGBLUP was adopted using two sets of data, one considering all available phenotypic information (SI; n=45,000) and another considering phenotypes just from genotyped animals (SII; n=2,000). The three different weights for the SNPs (w1 to w3) and the two sets of data (SI and SII) resulted in six different solutions for the SNP effects obtained under the WssGBLUP method. BayesC was applied under the model:
Statistical Methods. A randomization analysis was conducted to check the comparability of the different conditions at baseline. This was done by chi-square statistics for categorical and dichotomous variables, while t-tests were used for continuous variables. An attrition analysis was conducted to see whether there were differences in baseline scores between the participants who remained in the study and those who withdrew at post-test. This was done by analyses of variance and chi-square. Finally, to check the effectiveness of the tailored letters, logistic regression analyses were conducted with benzodiazepine cessation at post-test as the dependent variable (‘0’- did not quit and ‘1’ – did quit) and condition as the independent variable. All comparisons between the intervention conditions were adjusted for age, gender and benzodiazepine dose (in diazepam equivalents).
Statistical Methods. All subjects who are randomized, take one or more doses of test material and have at least one post treatment efficacy measurement will be included in the analysis. Comparisons between treatments will be assessed using an *** with factors of ***, *** and *** and with *** for percent weight loss, and *** for percentage of subjects with at least 5% weight loss. A step-down multiple comparison procedure will be used to compare each dose group with ***. That is, comparison with *** will start at the ***. If the statistical test is significant at *** for both co-primary endpoints, then the test will proceed to the *** also at the ***. If the statistical comparison is not significant at the ***, then the statistical test will be stopped and the *** will not be tested. If both dose groups are significantly better than ***, then the two active dose groups will be compared. A *** of difference in response rate between treatment groups will be derived. The *** for subjects who discontinue treatment prior to completion of the study, ***. INTERNAL PROTOCOL APPROVAL 2 PRINCIPAL INVESTIGATOR SIGNATURE 3 PROTOCOL SYNOPSIS 4
Statistical Methods. 12.1.1 Comparisons of Interest [***]
Statistical Methods. This is a Phase 3 study designed to evaluate the efficacy of MIRV compared with that of standard of care IC Chemo in patients with in high FRα expressing, platinum-resistant, advanced high-grade epithelial ovarian, primary peritoneal, or fallopian tube cancers. All randomized patients will comprise the intent-to-treat (ITT) population. Efficacy will be evaluated using the ITT population. Safety will be evaluated in the population of randomized patients who have received at least one dose of study drug. All statistical analyses will be performed using the most recently released SAS statistical software, unless otherwise noted. For categorical variables, the number (n) and percent of each category within a parameter will be presented. For continuous variables, the sample size (n), mean, median, and standard deviation, as well as the minimum and maximum values, will be presented. Missing data will not be imputed unless otherwise stated. There will be a detailed description of patient disposition, patient demographics, and baseline characteristics.
Statistical Methods. The physical addresses of all 242 Title X publicly-funded family planning clinic sites in Georgia for the year 2006 were geocoded using ArcGis (ArcMapVersion 10). Next, we used the American Community Survey data files to characterize each zip code in Georgia with regard to key population-level characteristics (e.g., proportion of households below the federal poverty line, and with characteristics such as race/ethnicity and age). The NSFG race- and age-specific estimates were used to estimate the proportion of the base population “at risk” of unintended pregnancy in each census tract and HUD tract-zip code crosswalk file were used to aggregate the population at risk in each zip code area. We then utilized Georgia’s pregnancy termination file to calculate the number pregnancy terminations occurring for each of the state’s zip codes for 2006. For each ZCTA, an abortion rate was calculated based upon the total number of terminated pregnancies over the total number of women of reproductive age (15-44 years) at risk of unintended pregnancy residing in the geographic area. The covariates were variables that were both included in our dataset and in past studies shown to be associated with unintended pregnancies. These variables included the woman’s age category (15-17, 18-19, 20-24, 25-29, 30- 34, and 35-44), ethnicity and race (non-Hispanic White, non-Hispanic Black, Hispanic, and other), percentage poverty per zip code (0-10%, 10-20%, 20-30%, and 30+%), geographical characteristic (metropolitan/urban, micropolitan/large rural, and small/isolated rural town), and distance in meters away from the closest Title X family planning clinic (0-4000, 4000-7000, 7000-11000, 11000-15000, and 15000+ meters). Distance categories were based on the quintile distribution of abortion rates occurring from the ITOP file. The sum of all abortions per variable was calculated using Statistical Analysis System (SAS Institute, Inc., Cary, NC). The abortion rate per variable was analyzed using a Poisson model (SAS Proc Genmod) utilizing robust standard errors to account for multiple observations within each zip code area. The study was approved by the Emory University Institutional Review Board [8].
Statistical Methods. [*]: The [*] analysis will be based on all subjects who have evaluable [*]. The individual concentration-time profiles of [*] will be evaluated using [*]. Data permitting, the following pharmacokinetic parameters will be determined: [*] Descriptive statistics (N, mean, standard deviation, CV, median, minimum, and maximum) will be used to summarize [*] concentration data at each planned sampling time point for each treatment. [*] parameters calculated from the concentrations will also be summarized by treatment using descriptive statistics. Bioequivalence will be evaluated for [*] (prepared from [*])] compared to [*] (prepared from [*] with an analysis of their log-transformed [*]. [*] with terms for subject and treatment will be performed for the parameters [*]. From these analyses, 90% confidence intervals (CIs) for the geometric test/reference mean ratios will be obtained. Group 1 will be compared with Group 2, with Treatment Group 2 as the reference. Bioequivalence will be declared if the 90% confidence limits for the test to reference ratios fall within [*]. Bioeavailability will be evaluated for [*] (prepared from [*])] compared to both [*] (prepared from [*] (prepared from [*] using the same [*] model described above. Group 3 will be compared with Group 1 as well as Group 2, with Treatment Group 1 and Treatment Group 2 as the reference, respectively. For these comparisons, the bioavailability ratio and 95% C.I. will be calculated using the difference between the [*]. A sample size of [*] per group has been calculated to provide greater than 90% power to demonstrate equivalence using a CV of [*]. The CV of [*] was the largest CV calculated for the log transformed [*] parameters using data from the following study: [*] Safety: Descriptive summaries will be provided by treatment group for demographics. The frequency of adverse events will be tabulated. Baseline, within study and end-of-study, and change from baseline clinical laboratories, and ▇▇▇▇▇ ▇▇▇▇▇ will be summarized. Descriptive statistics will be computed for safety parameters as appropriate. Further statistical evaluations will be applied for select endpoints, if warranted. All baseline data and safety data collected during the study will be listed for each subject and dose group. *Confidential Treatment Requested. Material has been omitted and filed separately with the Commission.
Statistical Methods. All statistical analyses will be performed using Statistical Analysis Software (SAS®) Version
Statistical Methods. Database statistics were calculated using the SPSS package v.6.0