3. Materials and Methods
3.6 Statistical analysis
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additionally utilized a cutoff score greater than 2.0 in the SCL-5 in Q1 and greater than 1.85 in the SCL-8 in Q3 to define the disease comparison group, i.e. women with depressive and anxiety symptoms throughout the pregnancy but not exposed to antidepressants.200
3.5 Use and translation of psychometric instruments
In the Multinational Medication Use in Pregnancy Study we used translated versions of the following psychometric instruments: the MMAS-8, the EPDS, and the BMQ-Specific.
Copyright agreements were signed with Prof. DE. Morisky and Prof. R. Horne in order to utilize the MMAS-8 and BMQ-Specific, respectively. Use of the EPDS for research purposes could be done without seeking permissions from Prof. J. Cox.204 Information about validation properties and translation process has been described in study III.
3.6 Statistical analysis
The statistical analyses were performed using the Statistical Package for Social Sciences SPSS (IBM® SPSS® Statistics) version 20.0 (studies I, III, IV) and 22.0 (study II).
Descriptive analyses were performed in all studies. The Pearson chi-square or Fisher exact tests, and the Student's t-test or one-way analysis of variance (ANOVA) were utilized to compare proportions and mean scores between independent groups, respectively. In all analyses, missing values were less than 5% of the total. In studies I, III and IV, a two-tailed p-value of < 0.05 was considered statistically significant. Because of the numerous analyses conducted in study II, we undertook a conservative approach and considered two-tailed p-YDOXHVRIVWDWLVWLFDOO\VLJQLILFDQW
3.6.1 Associations between explanatory and outcome variables x Logistic regression
Logistic regression analyses were utilized to determine any association between the explanatory and outcome variables (studies I, IV). In study I, we first fit the univariate logistic regression model for all explanatory variables. The multivariate model was then built and adjusted for all remaining covariates.
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In study IV, we first fit the univariate logistic regression model for the exposure and candidate confounding variables. Candidate variables were selected based on a univariate p-value of <0.15 and added into the multivariate model. Variables having no role (p-value >0.05) or yielding a change smaller than 15% in the beta coefficients of the retained variables were removed.
In both studies, the main effect model was checked for the presence of clinically relevant interactions. The final multivariate model included statistically significant independent variables and clinically significant variables. Goodness of fit of the final multivariate model was assessed by using the Hosmer and Lemeshow test.205
x Generalized Estimating Equation
Generalized Estimating Equation (GEE) with a Poisson distribution was utilized in study II.
A Poisson regression provides direct estimates of relative risks and was therefore considered the preferable choice compared to logistic regression. However, a Poisson regression applied to binary data (without adjustments) provides conservative results by overestimating the standard error for the risk estimates. To remove this bias, a robust variance estimator was used. We carried out two sets of analyses: in Model 1, we computed the total association between eating disorders and the outcomes of interest by adjusting for the the minimal sufficient set of variables; these variables were identified via utilization of Directed Acyclic Graphs (DAGs) using DAGitty version 2.2 (one DAG for each medication-outcome pair).206 In Model 2 we entered the set of confounders from Model 1 plus additional covariates (as directed by the DAGs) in order to estimate the direct association between eating disorders and the outcomes of interest.
In study III, a GEE analysis with a binomial distribution was performed to take into account clustering on region of residency. The multivariate GEE model was built as follows: candidate variables were selected based on a univariate p-value<0.15; variables having no role (p-value>0.05) or yielding a change smaller than 15% in the beta coefficients of the retained variables were removed; continuous variables were checked for linearity in the logit link. Because of non-linearity, the variable antidepressant risk perception was categorized according to the non-linearity midpoints (risk 0-3; 4-
The final multivariate model included statistically significant independent variables and potential confounders.
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x Directed Acyclic Graphs
DAGs were employed in study II with the aid of DAGitty version 2.2206 in order to identify potential confounding and mediating factors of the association between eating disorder subtypes before and/or during pregnancy and medication use during pregnancy or postpartum.
DAGs graphically encode relationships between variables, and they enable us: 1) to identify whether there is confounding; 2) to identify which variables need to be controlled for; 3) identify which variables should not be controlled for. Employing DAGs require to clearly setting down assumptions about causal relationship and direction of the association between variables.175,207 A description of our assumptions about the direction of the association between variables is provided in study II. The six individual DAGs utilized in study II are outlined in Appendix 3.
x Correlation analyses
,QVWXG\,,,ZHXVHGWKH6SHDUPDQ¶VUDQNFRUUHODWLRQFRHIIicient to explore the correlation between the medication adherence sum scores and beliefs about medications.
3.6.2 Sensitivity analysis
In study I, we built multivariate models of factors associated with the outcomes of interest separately for each region. In these instances, region of residency was not included in the model. We also carried out GEE analyses with a binomial distribution taking into account clustering on region of residency, in order to evaluate whether the measure of association between the other explanatory variables and the outcomes of interest differed substantially from those obtained in the logistic regression analyses.
In study II, we included BMI at conception as additional covariate in Model 1 because of the uncertainty in the direction of the association between BMI and eating disorders. In this study we excluded from the sample those pregnancies ending in a stillbirth, and therefore we could not evaluate patterns and factors associated with medication use among these women.
In study III, we explored the role of the explanatory variables, namely smoking during pregnancy, depressive symptoms or risk perception of antidepressant exposure, for which a
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clustering effect on individual country of residency could not be ruled out. Hence, we performed sensitivity analyses taking into account clustering on country of residency, even though this led to lower statistical power.
In study IV, we explored whether there was a difference in the mean duration (in days) of vaginal bleeding episodes between the exposed and non-exposed women. Additional analyses on individual antidepressant level were also performed when statistical power allowed doing so. Antidepressants were also regrouped according to the level of affinity to the serotonin transporter. We also explored the association between antidepressant exposure and postpartum hemorrhage among women who delivered vaginally with or without instrumental intervention (i.e. forceps and/or vacuum). Since we included women with multiple participations in the MoBa study, we performed sensitivity analyses restricted to women who participated only one time in the study, leading to the exclusion of 18.5% of the MoBa population. We additionally carried out sensitivity analyses including only the first pregnancy of those women participating more than once in the MoBa study, leading to the exclusion of 9.3% of the MoBa population. We also carried out GEE analyses taking into account such dependency within the data, with the maternal id being the repeated measure.
3.6.3 Power calculation
Information about sample size calculation (using 5% precision with 95% confidence interval) for the prevalence of medication use in pregnancy on country and region level has been described in Appendix 4 of study I. Sample size calculations were performed in Epi Info TM 7.208
In study II, no power calculation was carried out due to the lack of previous studies about medication use in pregnancy among women with eating disorders.
In study III, the overall prevalence of low adherence to psychotropics could be calculated with a precision of ±8%. No power calculation about the minimal detectable magnitude of the association between the various maternal factors and low medication adherence was performed due to lack of previous similar studies. Sample size calculations were performed in Epi Info TM 7.208
In study IV, post hoc sample power analysis for the exposure group SSRIs/SNRIs revealed that we could detect a 25% and 50% increase in the odds of vaginal bleeding in early
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pregnancy and midpregnancy, respectively, with an 80% power. With respect to the outcome of postpartum hemorrhage, we had power to detect a 60% increase in the odds.
The sample size calculator developed by Dr. Stigum was utilized.209 3.6.4 Imputation
In studies II, III and IV we imputed missing values on scale variables, namely the SCL-5, SCL-8, MMAS-8, and BMQ-Specific, using the estimation-maximization algorithm.210 Information about the percentage of imputed values and the criteria applied for each imputation is provided in each individual study.
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