The objectives of this systematic review are to examine how researchers report missing data in questionnaires and to provide an overview of current methods for dealing with missing data. We included 262 studies published in 2010 in 3 leading epidemiologic journals. Information was extracted on how missing data were reported, types of missing, and methods for dealing with missing data. Seventy-eight percent of the studies lacked clear information about the measurement instruments. Missing data in multi-item instruments were not handled differently from other missing data. Complete-case analysis was most frequently reported (81% of the studies), and the selectivity of missing data was seldom examined. Although there are specific methods for handling missing data in item scores and in total scores of multi-item instruments, these are seldom applied. Researchers mainly use complete-case analysis for both types of missing, which may seriously bias the study results.

Logistic regression is often used for mediation analysis with a dichotomous outcome. However, previous studies showed that the indirect effect and proportion mediated are often affected by a change of scales in logistic regression models. To circumvent this, standardization has been proposed. The aim of this study was to show the relative performance of the unstandardized and standardized estimates of the indirect effect and proportion mediated based on multiple regression, structural equation modeling, and the potential outcomes framework for mediation models with a dichotomous outcome. We compared the performance of the effect estimates yielded by the three methods using a simulation study and two real-life data examples from an observational cohort study (n = 360). Lowest bias and highest efficiency were observed for the estimates from the potential outcomes framework and for the crude indirect effect ab and the proportion mediated ab/(ab + c’) based on multiple regression and SEM. We advise the use of either the potential outcomes framework estimates or the ab estimate of the indirect effect and the ab/(ab + c’) estimate of the proportion mediated based on multiple regression and SEM when mediation analysis is based on logistic regression. Standardization of the coefficients prior to estimating the indirect effect and the proportion mediated may not increase the performance of these estimates.

Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels significantly contributes to the model, different methods are available. For example pooling chi-square tests with multiple degrees of freedom, pooling likelihood ratio test statistics, and pooling based on the covariance matrix of the regression model. These methods are more complex than RR and are not available in all mainstream statistical software packages. In addition, they do not always obtain optimal power levels. We argue that the median of the p-values from the overall significance tests from the analyses on the imputed datasets can be used as an alternative pooling rule for categorical variables. The aim of the current study is to compare different methods to test a categorical variable for significance after multiple imputation on applicability and power. In a large simulation study, we demonstrated the control of the type I error and power levels of different pooling methods for categorical variables. This simulation study showed that for non-significant categorical covariates the type I error is controlled and the statistical power of the median pooling rule was at least equal to current multiple parameter tests. An empirical data example showed similar results. It can therefore be concluded that using the median of the p-values from the imputed data analyses is an attractive and easy to use alternative method for significance testing of categorical variables.

Background: In 2010, the human papillomavirus (HPV) vaccination was introduced in the Dutch National Immunization Program for 12-year-old girls, aiming to reduce the incidence of cervical cancer in women. HPV vaccination uptake turned out to be lower than expected: 61% versus 70%, respectively. Mothers were shown to play the most important role in the immunization decision about this vaccination. They had also expressed their need for interactive personal information about the HPV vaccination over and above the existing universal general information. To improve the effectiveness of the existing education about the HPV vaccination, we systematically developed a Web-based tailored intervention with virtual assistants providing mothers of girls to be invited with tailored feedback on their decision making about the HPV vaccination. Objective: The aim of this study was to evaluate the effectiveness of the Web-based tailored intervention for promoting HPV vaccination acceptance by means of a randomized controlled trial (RCT). Methods: Mothers were recruited via the Dutch vaccination register (Praeventis) (n=36,000) and three Web-based panels (n=2483). Those who gave informed consent (N=8062) were randomly assigned to the control (n=4067) or intervention condition (n=3995). HPV vaccination uptake, as registered by Praeventis once the HPV vaccination round was completed, was used as the primary outcome. Secondary outcomes were differential scores across conditions between baseline (before the provided access to the new tailored intervention) and follow-up (just before the first vaccination) regarding the mothers’ degree of informed decision making (IDM), decisional conflict, and critical determinants of HPV vaccination uptake among which are intention, attitude, risk perception, and outcome beliefs. Results: Intention-to-treat analysis (N=8062) showed a significant positive effect of the intervention on IDM, decisional conflict, and nearly all determinants of HPV vaccination uptake (P<.001). No effect was found on uptake (P=.60). This may be attributed to the overall high uptake rates in both conditions. Mothers evaluated the intervention as highly positive, including the website as well as the virtual assistants that were used to deliver the tailored feedback. Conclusions: This computer-tailored intervention has the potential to improve HPV vaccination acceptability and IDM and to decrease decisional conflict among mothers of invited girls. Implications for future research are discussed. Trial Registration: Trialregister.nl NTR4935; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4935 (Archived by WebCite at http://www.webcitation.org/6srT7l9EM)