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. 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; Xxxx et al. 2012) and XxxxxX (Xxxxxx 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 (Xxxxxxx et al. 2010), and I is an identity matrix. The SNP effects (û) were calculated as in Stranden & Xxxxxxx (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. 12.1.1 Comparisons of Interest [***]
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. NIH-PA Author Manuscript χ2 tests were used to assess differences in the distributions of categorical phenotypes between the random sample of sibling used in this study and the cohort of concordant siblings from which the random sample was taken. A 2-sample t test for independent samples was used to compare age between the 2 groups. Interrater reliability was assessed using the kappa (κ) statistic. We used the guidelines proposed by Xxxxxx and Xxxx to interpret the level of agreement: κ>0.80, excellent agreement; κ=0.60 to 0.80, substantial agreement; κ=0.40 to 0.60, moderate agreement; and κ<0.40, poor to fair agreement (17). The following stroke subtypes were used: 1) large vessel, 2) cardioembolism, 3) small vessel, 4) other determined cause, and 5) undetermined cause. A sixth category—no stroke/missing—was included to account for instances in which reviewers did not provide a diagnosis. Reasons for missing data were not systematically collected. Separate estimates for κ were obtained by treating the no stroke/missing category as missing data using the algorithm written by Xxxx et al (18). These analyses were repeated on the subset of the participants who self-reported having a stroke. Xxxxxxx et al. Page 4 NIH-PA Author Manuscript The randomly sampled siblings in this study were screened for participation in SWISS between March 31, 2000, and November 6, 2003. The 30 siblings included were from 30 separate pedigrees. The probands of the siblings were enrolled at centers in 8 different states in the United States. Among the 30 siblings evaluated in this study, head computed tomography (CT) was performed on 80% (24/30) and MRI was performed on 60% (18/30). A cervical arterial imaging study (carotid ultrasonography, magnetic resonance angiography, or digital subtraction angiography) was performed on 97% (29/30). Intracranial arterial study (magnetic resonance angiography, digital subtraction angiography, CT angiography, or transcranial Doppler ultrasonography) was performed on 100% (30/30). Electrocardiography was performed on 73% (22/30). Cardiac ultrasonography (transthoracic or transesophageal) was performed on 67% (20/30). NIH-PA Author Manuscript Subject characteristics for the siblings included in this study are summarized in Table 1. The majority of subjects were male (67%) and white (83%). Ages ranged from 46 to 88 years, with a median of 70 years. Seventy-three percent smoked, 70% had hypertension, 27% had diabetes mellitus, 33% had chronic fibril...
Statistical Methods. To gain insights about how the acoustic matching features con- tribute to the expression of affiliation (categorical variable Af- filiation with the values AG and DG; cf., first vs. second row in Table 1) and to the expression of Preference (with the values preferred and dispreferred; cf., left vs. right column in Table 1), we built two separate mixed effects logistic regression models, one predicting Affiliation and one predicting Preference, using the lme4 package in R [33]. The maximum combination of in- dependent variables were the matching features match(F0span), match(F0med) and match(AR), as well as their two-ways inter- actions, and Speaker as a random effect (the speaker producing the second assessment in each pair; n = 12). To analyze how the acoustic matching features match(F0span), match(F0med) and match(AR) are related to both Affiliation and Preference in more detail, we built linear mixed effects regression models with the lme4 package in R. To account for possible long-term effects (e.g., due to tiredness), we calculated the relative position of each assessment in the one-hour long conversations (normalized between 0 and 1; Position). The maximum model contained the respective matching feature as the dependent variable, Affiliation, Pref- erence and Position as independent variables, as well as their two-way interactions, and Speaker as a random effect . To obtain the best models, we performed best subsets re- gression and selected the model with the lowest AIC [34]. Only these models are presented in Section 3.
Statistical Methods. The explanatory variables, as described previously, were descriptively summarized at baseline comparing black and white MSM using chi-square, xxxxxx’x exact and t-tests.
Statistical Methods. A detailed SAP will be finalized before the primary database lock and unblinding. This analysis plan may modify what is outlined in the protocol; however, any major modifications of the primary endpoint definition or its analysis will also be reflected in a protocol amendment.
Statistical Methods. We performed a descriptive analysis of the total population in the Netherlands, in Slovenia and in the European Union between 2013 and 2019, and calculated the total number of residents living in each individual country. Then, we stratified the total population of each individual country by age, which was grouped into five age categories: from 0 to 14 years, from 15 to 24 years, from 25 to 44 years, from 45 to 64 years and more than 65 years, and sex. These results were presented as total numbers and as a proportion of the total population. Then, we identified the number of individuals to whom opioids, and NSAIDs were prescribed and calculated an annual prevalence percentage with corresponding 95% confidence interval (CI) for each individual country through the observation period. To explore time-trends of opioids, and NSAIDs prescriptions in each individual country we calculated relative risks (RR) with corresponding 95% CI in which we selected the calendar year 2013 as a reference. In order to make the annual prevalence calculations as well as the time- trend analysis comparable between the Netherlands and Slovenia, we corrected for demographic differences (age and sex) between these two countries with direct standardization where we utilized the population of European Union of 2013 as weights. We presented results of the latter analysis as standardized prevalence percentage with corresponding 95% CI, and standardized RR with corresponding 95% CI where we took the calendar year of 2013 as a reference. There were no individuals lost to follow-up nor were any 6 data lost in the merging process. All statistical analyses were performed with SPSS for Windows, release 25.0 (SPSS, Chicago, IL, USA). Figures were created with R studio (A Language and Environment for Statistical Computing, R Core Team, R Foundation for Statistical Computing, Vienna, Austria, xxxxx://xxx.X- xxxxxxx.xxx), using R package ggplot2 version 3.2.125 [28]. The STROBE statement checklist for cohort studies was used to guide reporting of the findings.
