mice: Multivariate Imputation by Chained Equations in R

The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems

Validating a Multi-criteria decision analysis (MCDA) framework for health care decision making (abstract)

OBJECTIVES: When evaluating healthcare interventions, decision-makers are increasingly asked to consider multiple criteria to support their decision. The MCDA-based EVIDEM framework was developed to support this process. It includes a simple weight elicitation technique, designed to be easily applicable by a broad range of users. The objective of this study was to compare the EVIDEM technique with more traditional techniques. METHODS: An online questionnaire was developed comparing the EVIDEM technique with four alternative techniques including AHP, best/worst scaling, ranking and point-allocation. A convenience sample of 60 Dutch and Canadian students were asked to fill out the questionnaires as if they were sitting in an advisory committee for reimbursement/prioritization of healthcare interventions. They were asked to provide weights for 14 criteria using two techniques, and to provide feedback on ease of use and clarity of concepts of the different techniques. RESULTS: Results based on the first 30 responses show that EVIDEM is easy to understand and takes little time to complete, three minutes on average. Criteria weights derived using the EVIDEM technique and best/worst scaling are divergent. Comparing the rank order of criteria respondents gave using these two techniques; there is more resemblance in rank order of criteria weighted with the EVIDEM technique. Compared to AHP/ranking/point-allocation, EVIDEM takes less time to complete but is only preferred by 33% of decision-makers. AHP/ranking and point allocation were often described as clearer and more reflective of the respondents’ opinion. CONCLUSIONS: The simple technique is proposed as a starting point for users wishing to adapt the EVIDEM framework to their own context. Other techniques may be preferred and their impact on the MCDA value estimate generated by applying the framework is being explored. This project is part of a large collaborative work that includes developing and validating this framework to facilitate sound and efficient MCDA-applications

Validating a Multi-criteria decision analysis (MCDA) framework for health care decision making: poster

OBJECTIVES: When evaluating healthcare interventions, decision-makers are increasingly asked to consider multiple criteria to support their decision. The MCDA-based EVIDEM framework was developed to support this process. It includes a simple weight elicitation technique, designed to be easily applicable by a broad range of users. The objective of this study was to compare the EVIDEM technique with more traditional techniques. METHODS: An online questionnaire was developed comparing the EVIDEM technique with four alternative techniques including AHP, best/worst scaling, ranking and point-allocation. A convenience sample of 60 Dutch and Canadian students were asked to fill out the questionnaires as if they were sitting in an advisory committee for reimbursement/prioritization of healthcare interventions. They were asked to provide weights for 14 criteria using two techniques, and to provide feedback on ease of use and clarity of concepts of the different techniques. RESULTS: Results based on the first 30 responses show that EVIDEM is easy to understand and takes little time to complete, three minutes on average. Criteria weights derived using the EVIDEM technique and best/worst scaling are divergent. Comparing the rank order of criteria respondents gave using these two techniques; there is more resemblance in rank order of criteria weighted with the EVIDEM technique. Compared to AHP/ranking/point-allocation, EVIDEM takes less time to complete but is only preferred by 33% of decision-makers. AHP/ranking and point allocation were often described as clearer and more reflective of the respondents’ opinion. CONCLUSIONS: The simple technique is proposed as a starting point for users wishing to adapt the EVIDEM framework to their own context. Other techniques may be preferred and their impact on the MCDA value estimate generated by applying the framework is being explored. This project is part of a large collaborative work that includes developing and validating this framework to facilitate sound and efficient MCDA-applications

Estimating preferences for medical devices: does the number of profile in choice experiments matter?

Background: Most applications of choice-based conjoint analysis in health use choice tasks with two profiles, while marketing studies routinely use three or more. This study reports on a randomized trial comparing paired with triplet profile choice formats focused on hearing aids. - Methods: Respondents with hearing loss were drawn from a nationally representative cohort, completed identical surveys, and were randomized to choice tasks with two or three profiles. The primary outcomes of differences in estimated preferences were explored using t-tests, likelihood ratio tests, and analyses of individual-level models estimated with ordinary least squares. - Results: 500 respondents were recruited. 127 had no hearing loss, 28 had profound loss and 22 declined to participate and were not analyzed. Of the remaining 323 participants, 146 individuals were randomized to the pairs and 177 to triplets. Pairs and triplets produced identical rankings of attribute importance but homogeneity was rejected (P<0.0001). Pairs led to more variation, and were systematically biased toward the null because a third (32.2%) of respondents focused on only one attribute. This is in contrast to respondents in the triplet design who traded across all attributes. - Discussion: The number of profiles in choice tasks affects the results of conjoint analysis studies. Here triplets are preferred to pairs as they avoid non-trading and allow for more accurate estimation of preferences models

HElmet therapy Assessment in infants with Deformed Skulls (HEADS): protocol for a randomised controlled trial

Background In The Netherlands, helmet therapy is a commonly used treatment in infants with skull deformation (deformational plagiocephaly or deformational brachycephaly). However, evidence of the effectiveness of this treatment remains lacking. The HEADS study (HElmet therapy Assessment in Deformed Skulls) aims to determine the effects and costs of helmet therapy compared to no helmet therapy in infants with moderate to severe skull deformation. Methods/design Pragmatic randomised controlled trial (RCT) nested in a cohort study. The cohort study included infants with a positional preference and/or skull deformation at two to four months (first assessment). At 5 months of age, all children were assessed again and infants meeting the criteria for helmet therapy were asked to participate in the RCT. Participants were randomly allocated to either helmet therapy or no helmet therapy. Parents of eligible infants that do not agree with enrolment in the RCT were invited to stay enrolled for follow up in a non-randomisedrandomised controlled trial (nRCT); they were then free to make the decision to start helmet therapy or not. Follow-up assessments took place at 8, 12 and 24 months of age. The main outcome will be head shape at 24 months that is measured using plagiocephalometry. Secondary outcomes will be satisfaction of parents and professionals with the appearance of the child, parental concerns about the future, anxiety level and satisfaction with the treatment, motor development and quality of life of the infant. Finally, compliance and costs will also be determined. Discussion HEADS will be the first study presenting data from an RCT on the effectiveness of helmet therapy. Outcomes will be important for affected children and their parents, health care professionals and future treatment policies. Our findings are likely to influence the reimbursement policies of health insurance companies. Besides these health outcomes, we will be able to address several methodological questions, e.g. do participants in an RCT represent the eligible target population and do outcomes of the RCT differ from outcomes found in the nRCT

Estimating Patients' Preferences for Medical Devices: Does the Number of Profile in Choice Experiments Matter?

Background: Most applications of choice-based conjoint analysis in health use choice tasks with only two profiles, while those in marketing routinely use three or more. This study reports on a randomized trial comparing paired with triplet profile choice formats focused on measuring patient preference for hearing aids. Methods: Respondents with hearing loss were drawn from a nationally representative cohort, completed identical surveys incorporating a conjoint analysis, but were randomized to choice tasks with two or three profiles. Baseline differences between the two groups were explored using ANOVA and chi-square tests. The primary outcomes of differences in estimated preferences were explored using t-tests, likelihood ratio tests, and analysis of individual-level models estimated with ordinary least squares. Results: 500 respondents were recruited. 127 had no hearing loss, 28 had profound loss and 22 declined to participate and were not analyzed. Of the remaining 323 participants, 146 individuals were randomized to the pairs and 177 to triplets. The only significant difference between the groups was time to complete the survey (11.5 and 21 minutes respectively). Pairs and triplets produced identical rankings of attribute importance but homogeneity was rejected (P