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As with any self-administered questionnaire, missing data are frequently observed and can strongly bias any inference. The objective of this study was to investigate the best approach for handling missing data in the CES-D scale. The reasons for failure to complete certain items were investigated by semi-directive interviews on a random sample of participants. The accuracy of imputation models was investigated.

Various scenarios of nonignorable missing data mechanisms were investigated by a sensitivity analysis based on the mixture modelling approach. Possible reasons for nonresponse were identified.

The prevalence of hDS among complete responders was After multiple imputation, the prevalence was The estimates were robust to the various imputation models investigated and to the scenarios of nonignorable missing data.

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Conclusions The CES-D scale can easily be used in large cohorts even in the presence of missing data. Based on the results from both a qualitative study and a sensitivity analysis under various scenarios of missing data mechanism in a population of women, missing data mechanism does not appear to be nonignorable and estimates are robust to departures from ignorability. Multiple imputation is recommended to reliably handle missing data in the CES-D scale. Frequencies of various feelings during the previous week dealing center rating self-reported on a four-point scale.

The CES-D scale consists of 20 items selected from previously developed scales; 16 items are negatively worded, whereas four items are positively worded. As described in the original publication, four factors were identified by exploratory factor analysis [ 1 ]. Validation studies have shown that the CES-D was internally consistent, moderately stable over several weeks [ 1 dealing center rating and months [ 23 ], and strongly correlated with other measures of clinical depression or DS [ 1 — 4 ].

Due to its simplicity, the CES-D scale can be easily administered as a self-report questionnaire. In particular, it can be easily used in large cohort studies. However, the presence of incomplete observations is a major issue that can create biased estimates or spurious associations.


Observations of patients with more than four MVs are commonly excluded, even though this cut-off of four is not based on any statistical criterion, while observations with less than four MVs are imputed to the person-mean, even when there is a large proportion of incomplete responders [ 5 ]. These types of analyses on complete cases or after single imputation have been repeatedly proved to be biased [ 6 ].

Although there is an abundance of statistical literature about missing data, value cart binary options of epidemiologic studies dealing with missing data in the context of self-rated psychopathological symptoms are rare [ 7 ].

Ignorable or nonignorable missing dealing center rating mechanism cannot be identified from the data collected, or only in specific contexts under extra assumptions [ 9 ].

Only external data or qualitative studies can dealing center rating to formulate hypotheses concerning the nonresponse mechanism. Analyses differ considerably according to the expected type of missing data. Multiple imputation, a relatively flexible and general purpose approach to dealing with missing data when the missing data process is ignorable, is now available in standard statistical software [ 8 ]. It is based on an imputation model that relates the value to impute to a set of predictors.

Predictors of DS have been extensively studied, in order to adapt public health policies [ 10 — 12 ] and can be used for the imputation model. The MNAR hypothesis is rarely investigated, although it has been recommended to perform sensitivity analyses under different models for the nonresponse mechanism [ 8 ], i.

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Two approaches have been dealing center rating to impute nonignorable nonresponse: mixture modeling and selection modeling. Inthe E3N cohort included 98, women born between and and covered by a national health plan, covering mostly teachers.

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Participants were asked to complete self-administered questionnaires every two years addressing medical history, history of hormonal phases, and a variety of lifestyle characteristics. Informed consent was obtained from each participant at the beginning of the study after thorough explanation of the objectives of this study.

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Each questionnaire collected data about dealing center rating and psychological disorders, and three of them collected dealing center rating about psychotropic drug use. The eighth questionnaire, administered inincluded the CES-D scale; all women who returned this questionnaire were included in the present study, whether or not they filled in the CES-D scale part. All questionnaires are available on the web [ 14 ]. Data collection Variables of interest The two main variables of interest were the total CES-D score sum of the 20 items and a dichotomized hDS variable using the cut-off of 16 [ 1 ].

Sociodemographic characteristics were age, marital status, employment status, level of education, pregnancy history, menopausal status, inability to complete the eighth questionnaire alone. Psychopathological characteristics included history of depression or psychological disorder requiring treatment collected for all dealing center ratingcurrent depression or psychological disorder requiring treatment, psychotropic drug use collected at the fourth, sixth and seventh questionnairesdepression, anxiety or tears at menopause.

Recent hospitalizations in a general or psychiatric hospital as well as self-report of chronic diseases were also included as risk factors for DS.

The Delphi method was used to select, dealing center rating the 80 chronic diseases collected, those most likely to impact on DS [ 1617 ]. A panel of 40 physicians was constituted and was asked to classify each disease as having i no impact on DS, ii an impact on DS if it occurred recently during the previous two years onlyor iii an impact for lifetime occurrence.

Two rounds dealing center rating performed. Selected diseases were combined into a single ordinal variable representing the number of chronic diseases. Qualitative study A qualitative study was conducted to examine the hypotheses concerning the mechanism of MVs. In Februarya random sample of women from the responders to the 8th questionnaire in and for whom a telephone number was available, were invited to fill in the CES-D scale via a postal letter.

The same template for the CES-D scale as for the 8th questionnaire was used. Women who returned the questionnaires with MVs were systematically contacted by telephone. A random sample of complete responders was also contacted to serve as a control. The interview was semi-directive and standardized.

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The following open questions were systematically asked to both groups women with and without MVs in order to identify whether women had difficulties filling in the CES-D scale and if so whether these difficulties were independent or possibly related to depressive symptoms: How did the woman perceive the CES-D questionnaire?

What did she think about the format of the questionnaire? And trading by trend on binary content? How did she evaluate her ability and ease to express her emotions or feelings in different conditions with her relatives, her friends, and dealing center rating general practitioner? All interviews were performed by NR during the 3 weeks following training with a psycho-oncologist at Institut Curie.

Answers and comments on the sequence of questions were transcribed during the interviews. An analysis grid was then developed from the records to identify the main difficulties encountered when filling in the CES-D scale. When women wrote comments directly on the CES-D questionnaire, this information was used dealing center rating a source. When several difficulties were reported, the first difficulty to be reported was used for analysis. DS among complete cases To validate the CES-D measurement on the E3N cohort, 17 risk factors of DS were preselected from literature [ 10 dealing center rating 12 ] and a bivariate analysis was conducted to assess the association between DS and the preselected risk factors.

A negative binomial regression model was used to estimate relative risks when analyzing the score on how to trade on the Internet CES-D scale as overdispersed count data.

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A logistic regression model was used to estimate odds ratios when analyzing hDS as a binary variable. Investigation of the mechanism of MVs: dealing center rating qualitative assessment The proportion of incomplete observations, the prevalence of hDS according to the 8th questionnaire and to the qualitative study were compared using chi-square dealing center rating.

Potential reasons for incomplete responders were identified and described. MVs were imputed by using the MICE Multivariate Imputation by Chained Equations algorithm and R package [ 18 ], which allows for building up an imputation model with mixed-type covariates.

Five imputations were performed. First, imputation model was constructed for the overall score using linear regression pmm method and another for multiple imputation of the hDS status using a logistic regression model logreg method with all preselected risk factors of DS.

Second, four imputation models were constructed for multiple imputations of the items of the CES-D scale. Two different mean structures were investigated; the parsimonious model only included the CES-D items.

For each mean structure, a linear regression model pmm method as well as a polytomous unordered regression model polyreg method were used to impute missing values for the items. Simulation study A simulation study was performed to evaluate the predictive accuracy of the imputation model. The proportion of subjects with simulated MVs corresponded to the observed prevalence of MVs on the overall population.

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All incomplete observations created had the same number of Dealing center rating and situations ranging from one to 19 MVs were investigated. MVs were then imputed using either single imputation person-mean approach, each MV is replaced by the mean score for the subject or multiple imputation pmm and polyreg methods.

Descriptive indicators were measured on the various data sets defined by the number of MVs and the imputation method, i.

CES-D score and prevalence of hDS under the ignorable MVs hypothesis Complete case analysis, single and multiple imputation [ 8 ] approaches were performed. Three single imputation approaches were used and were denoted: minimum each MV is imputed to 0maximum each MV is imputed to 3 and person-mean. We compared the results of these different approaches by using estimates of the mean and standard deviation of the CES-D score, standard error of the mean CES-D score and the prevalence of hDS.

Sensitivity analysis under the nonignorable MVs hypothesis Last, a sensitivity analysis was performed to explore the possible bias due to MNAR data. The principle of this sensitivity analysis has been previously described [ 19 ].


Briefly, we proposed a 3-step strategy: Fit an imputation model assuming ignorable MVs; Modify the imputation model by adding a parameter expressed as the odds ratio comparing the odds of a response category among subjects with MV with those without MV for categorical variables; as the difference in expected values for continuous variables ; Impute MVs under the scenario thus specified. The scenarios were based on the assumption that nonresponders were more likely to present DS.

Different sizes of variation were explored. Some scenarios proposed larger size of variation for positive items than for negative items as they are less difficult to answer than the negative ones, especially when considering the highest of the four response categories [ 20 ].

All analyses were performed using R software [ 21 ], multiple imputation was performed using the mice R package [ 22 ], and multiple imputation under MNAR hypothesis was performed using the SensMice R package [ 19 ]. Results Internal and external validity of the data Study population The eighth questionnaire was sent to 94, women: 71, women returned the questionnaire response rate: Descriptive analyses are summarized in tables contained in Additional files 123. Women with 11 to 20 MVs on the CES-D scale more closely resembled complete cases in terms of psychological characteristics and morbidities than women with five to 10 MVs.

Tables contained in Additional files 456 summarize the association with the various characteristics studied among complete cases. We found consistent results with dealing center rating.

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Psychological characteristics were the most strongly associated with hDS. Similar results were obtained for the score with a negative binomial model estimating relative risks data not shown.

Psychometric properties of the CES-D scale Principal component analysis showed the existence of a major first eigenvalue, corroborating the rather unidimensional structure of the scale. MVs were observed for each item. The prevalence of hDS was Imputation of all the MVs successively to 0 and 3 gave prevalence of hDS between Although the differences in prevalence were not statistically significant, these intervals did not include the point estimate based on complete data only, suggesting that MVs were not MCAR.

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None of the women contacted refused to be interviewed. None of them declared that they were reluctant to answer the CES-D questions. Four main types of potential reasons for nonresponses were identified Table 2. These results suggested that the missingness mechanism was not compatible with the hypothesis of MCAR data.

Imputation methods pmm and polyreg, based on a linear regression model and a polytomous unordered regression model, respectively, gave similar results, up to a large number of MVs see Additional file 7. The cut-off of 4 MVs did not appear to be associated with any particularly interesting properties. Below 15 MVs, multiple imputation performed very well, while single imputation gave some biased results. Dealing center rating complete cases and person-mean imputation gave biased results compared to multiple imputation of the items.

The two regression models studied for imputation of the values of the items linear regression model and polytomous unordered regression model gave similar results.

Overviews of systematic reviews can add value in some of the same ways as summaries of systematic reviews, albeit with a greater emphasis on breadth of coverage e. Policy briefs, on the other hand, start with a policy issue, not with the reviews that researchers happen to have produced. The few current producers of policy briefs as we have defined them in Table 2 have differed in their jurisdictional focus e. Activities That Could Support the Use of Systematic Reviews A range of activities are being piloted to support the use of reviews and review-derived products in policymaking Table 3 [25][26].

Dealing center rating of covariates to the imputation model for items did not substantially modify the results. Table 4 Score on the CES-D Scale and prevalence of high depressive symptoms after imputation of the values for items considered as qualitative variables, according to various scenarios of nonignorable missing data Full size table As expected, the impact on prevalence was proportional to the modeled shifts.

For the most extreme scenario considered here and when items were treated as qualitative variables Table 4the prevalence of hDS increased from Addition of the covariates to dealing center rating imputation model for items did not substantially modify the results. This was expected, as no hypothesis had been proposed on the imputation model for the association between the missingness mechanism and the covariates.

Similar results were observed when considering items as quantitative variables. Discussion Many population-based studies have been conducted to estimate the prevalence of psychiatric disorders based on self-rated scales or to investigate associations with these disorders. Few of these studies have assessed the impact of missing data on the accuracy of estimates.

A qualitative study showed that none of the women contacted declared that they were reluctant to answer the questions of the CES-D scale, suggesting that the missingness mechanism could be ignorable. Multiple imputation is then an adequate approach to handle missing data. An imputation model including the CES-D items and various covariates was shown to have the same predictive properties as a model using the CES-D items only.

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A simulation study showed that multiple imputation performed well, even in the presence of a large amount of MVs and using an imputation model including only CES-D items. In a sensitivity analysis, these estimates were found to be quite robust under plausible MNAR scenarios.