Multivariate Analyses Clause Samples

The Multivariate Analyses clause defines the terms and conditions under which statistical analyses involving multiple variables are conducted within the scope of an agreement. Typically, this clause outlines the methodologies to be used, the responsibilities of each party in providing data, and any requirements for reporting or interpreting results. For example, it may specify that all analyses must be pre-approved or that raw data must be shared for verification. Its core function is to ensure that complex data analyses are performed consistently and transparently, reducing the risk of misinterpretation and disputes over analytical methods or outcomes.
Multivariate Analyses. With other factors controlled, urban/rural residence has no significant association with spousal agreement on waiting time to next birth in any of the 13 countries included in the multivariate analysis (Table 4). Wife’s education and education gap between the husband and wife also do not have any significant associations, except in Malawi where wife’s education is significantly positively associated with the likelihood of both partners wanting similar waiting time to next birth. Independent of other factors, wife’s age is significantly positively associated with spousal agreement on waiting time to next birth in 8 of the 13 countries: Ghana, Mali, Ethiopia, Kenya, Uganda, Mozambique, Zimbabwe, and Zambia. The association is particularly strong in Ethiopia (aOR=3.03; p<0.01), Zambia (aOR=3.86; p<0.01), and Zimbabwe (aOR=5.66; p<0.01), where couples with wives age 35-49 are 3-6 times more likely to have spousal agreement on waiting time to next birth than couples with wives age 15-34. The age gap between the partners has no significant relationship with spousal agreement on waiting time to next birth in any of the 13 countries. In most countries, employment status of the wife or that of the husband has no significant association with spousal agreement on waiting time to next birth. In the case of the wife’s employment, working for cash is significantly positively associated with spousal agreement in only Rwanda and Uganda. In the case of the husband’s employment, working for cash is significantly positively associated with spousal agreement in only Malawi and significantly negatively in Zambia. In Benin, Ghana, and Mali, spouses in polygynous marriages are less likely to agree on waiting time to next birth than those in monogamous marriages. The number of living children has a significant relationship with spousal agreement on waiting time to next birth in 7 of the 13 countries (Benin, Burkina Faso, Mali, Ethiopia, Uganda, Zimbabwe, and Zambia). In these countries, spousal agreement is less common among couples with more living children. Infecundibility is strongly negatively associated with spousal agreement on waiting time to next birth in 9 of the 13 countries. With other factors controlled, the spousal agreement was 2-7 times more common in couples where the wife was infecund. In the remaining four countries (▇▇▇▇, Uganda, Mozambique, and Zimbabwe) the association between infecundability and spousal agreement is positive but not statistically significant...
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Multivariate Analyses. In logistic regression models adjusting for age, every scale was associated with decision-making in the same magnitude and direction as in the bivariate logistic analyses (Table 3). Over 80 percent of clients who participated in the IN-CARE baseline surveys reported joint decision-making about HIV treatment with their providers. This is a much higher level than reported in another recent study measuring perceived role in decisional control. ▇▇▇▇▇▇ et al. found that only 32% of their participants practiced joint decision- making. To measure this outcome, ▇▇▇▇▇▇ et al. used the Control Preferences Scale developed by ▇▇▇▇▇▇ et al., which gave five different options for level of control, ranging from statements described as “fully passive” to “fully active.” It may also be of interest to note that while the participants in that particular study experienced a very low level of shared decision-making, almost twice as many of them actually preferred the joint role. There were differences in the study population as well, which may also contribute to the substantial difference between ▇▇▇▇▇▇ et al.‟s study and the IN-CARE study in the proportion of clients who experienced making choices about treatment in collaboration with their providers. The population was older and included women and fewer African Americans (17). In addition, we must also consider that the clients in the IN-CARE study did not take the survey immediately after their last appointment with their HIV provider. In fact, since only men who were either lost to care or newly diagnosed were eligible to participate, some of them may have had their last appointment several months or even years ago, and others may only have had just one appointment. This temporality issue may have several implications. Preferred and perceived role in decision-making may be blurred for some clients. Furthermore, clients who are either newly diagnosed or lost to care may participate differently in decision-making about treatment than do their counterparts who do not fall under either of those categories. Despite a high level of shared decision-making reported, we found associations with a few of our covariates. Black or African American men were over 3.5 times more likely to play a passive as opposed to collaborative role in making decisions about treatment when compared to white men. In addition, 92% of the white men in the study reported experiencing joint decision-making with their providers; 80% of black men reported thi...
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Multivariate Analyses. Results of the multivariate regression models are presented in Table 4. 4.2.1. Note on interpreting multivariate models with interactions:
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Multivariate Analyses. A logistic regression analysis was performed with the report of chronic pain at follow-up as dependent variable and the level of education and an abuse history as independent variables. The two variables did produce a significant regression model (Model: χ2(2) = 12.36, p = .001) and did account for 15% of the total variance in pain at follow-up which indicates a medium effect size according to ▇▇▇▇▇ [▇▇▇▇▇ 1988]. Both variables, low edu- cation level (Exp(β) = 4.23, p = .015) and abuse history (Exp(β) = 2.81, p = .021), signifi- cantly contributed to the model.
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Multivariate Analyses. Sequential logistic regression examining factors associated with alternative tobacco product use among all participants are reported in Table 3. The crude model (Model A) examined the association between the outcome variable – alternative tobacco product use – and the exposure variable – smoking status, demonstrating that nondaily and daily smokers were at increased risk of alternative tobacco use (OR=9.70, 95% Confidence [CI]: 7.87- 12.07; p<0.001; OR=4.33, CI: 3.39-5.54; p<0.001, respectively). After adjusting for the aforementioned covariates (Model B), the odds of using alternative tobacco products was higher among nondaily smokers and daily smokers in comparison to nonsmokers (OR=6.43, CI: 4.92-8.40; p<0.001; OR=2.79, CI: 1.92-4.05; p<0.001, respectively). In addition, younger age (p<0.001), being male (p<0.001), being Black (p<0.001), lower attitudes towards smoking scores (p<0.001), higher sensation seeking scores (p=0.008), higher classifying a smoker scale scores (p=0.004), more frequent alcohol use (p<0.001), and recent marijuana use (p<0.001) were all significantly associated with alternative tobacco product use. Table 4 presents the binary logistic regression examining factors associated with alternative tobacco product use among current smokers. Alternative tobacco product use was associated with being NDNS vs. FDNS (OR=0.47, CI: 0.31, 0.73, p=0.001) or daily smokers (OR=0.34, CI: 0.21, 0.54, p<0.001), after controlling for all possible covariates in the adjusted model (Model B). In addition, younger age (p<0.001), being male (p<0.001), being Black (p<0.001), and any marijuana use in the past 30 days (p=0.002) were associated with alternative tobacco product use among current smokers.
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Multivariate Analyses. The results of our multivariate logistic models are presented in Table 6. Model 1 represents our attempt to replicate previous multivariate studies with the available data. When considering the factors that determine having any retail clinic within a PCSA, our conceptual model proved accurate. An increase of one standard deviation (1.4%) in the
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