Prosthetic rehabilitation of maxillary lateral incisors agenesis using dental mini-implants: a multicenter 10-year follow-up

Objectives Implants are used to replace congenitally missing lateral incisors but often the space across the alveolar crest is too narrow to permit their use. This multicenter study (Dental Clinic of the University of Foggia, Odontostomatology Clinic of the University of L’Aquila) evaluated the efficacy of mini-implants in cases of maxillary lateral incisor agenesis with severe osseous atrophy in 10-year follow-up. Materials and methods Forty-seven mini-implants have been inserted in 35 patients affected by lateral incisors agenesis (23 single and 12 bilateral ageneses). All patients underwent orthodontic opening of the space of the upper lateral incisors. After the insertion of the implants, the immediate, non-functional loading, positioning of crowns, presence of pain during percussion and mini-implant function, horizontal and vertical movement when a force of 5 N was applied, ridge loss, and plaque index have been evaluated 1 month after loading, 1 year after loading, and then every 5 years in the following 10 years. Little’s test was used to evaluate the assumption that data of loss to follow-up implants are missing completely at random (MCAR) and that a complete-case scenario could be adopted. Wilcoxon test was carried out to look statistically significant differences between the various parameters resulting in the complete-case scenario and those assumed for the worst scenario. The software R (v. 3.6.1, 2019) was employed to perform the statistical analysis. Results The results obtained over 10 years range from 89% of success rate in a worst-case scenario to the 100% using a complete-case analysis with satisfactory values of marginal bone resorption and good conditions of the peri-implant tissue. Ten-year follow-up using complete-case analysis shows survival rates of 100% for implants with no signs of peri-implantitis, stability of the marginal bone levels and soft tissue around the dental implants. Conclusions The data collected show very good implant stability, absence of progressive peri-implantitis, and satisfactory aesthetical results in time (no signs of infraocclusion). Clinical relevance Mini-implants can be considered a valid and stable over time solution in the restorative treatment of maxillary lateral incisors agenesis.

The Effect of Multiple Imputations by Chained Equations on the Factors Associated with Immunization Coverage in India

Background: National Family Health Survey-4 (NFHS-4) revealed a significant improvement in the percentage of complete immunization attained in India. Even though determinants of immunization coverage in India are addressed by some studies, the impact of missing data in such large-scale surveys has not been accounted earlier. The present study aimed to identify the potential factors associated with immunization coverage in India using the complete case analysis (CCA) and multiple imputation by chained equations (MICE) analysis. Materials and methods: We created a dichotomous immunization variable based on the status of all the vaccines given to the child. All relevant variables were summarized using appropriate descriptive statistics along with the proportion of missingness. Further, MICE procedure was performed to impute the missing values after assessing the missing data mechanism. Multiple logistic regression after accounting for the sampling weights were used to report the estimates of odds-ratio (OR) and 95% confidence intervals (CI) for both CCA and MICE analysis and compared. Results: The percentage of children under five years of age who had total immunization was 69%. Further, we observed that female sex and rural habitation had higher odds of getting immunized in both CCA and MICE. Moreover, wealth index, number of antenatal visits, checkup after delivery and place of birth played an important role in the immunization coverage. Conclusion: MICE provided more precise risk estimates on potential factors associated with vaccination coverage compared to CCA, even if the major findings did not alter due to large sample size. Key words: Immunization, Health surveys, missing data, Logistic regression, complete case analysis, MICE.

Semiparametric estimation of the proportional rates model for recurrent events data with missing event category

Proportional rates models are frequently used for the analysis of recurrent event data with multiple event categories. When some of the event categories are missing, a conventional approach is to either exclude the missing data for a complete-case analysis or employ a parametric model for the missing event type. It is well known that the complete-case analysis is inconsistent when the missingness depends on covariates, and the parametric approach may incur bias when the model is misspecified. In this paper, we aim to provide a more robust approach using a rate proportion method for the imputation of missing event types. We show that the log-odds of the event type can be written as a semiparametric generalized linear model, facilitating a theoretically justified estimation framework. Comprehensive simulation studies were conducted demonstrating the improved performance of the semiparametric method over parametric procedures. Multiple types of Pseudomonas aeruginosa infections of young cystic fibrosis patients were analyzed to demonstrate the feasibility of our proposed approach.

Using Multiple Imputation and Inverse Probability Weighting to Adjust for Missing Data in HIV Prevalence Estimates: A Cross-Sectional Study in Mwanza, North Western Tanzania.

Background Population surveys and demographic studies are the gold standard for estimating HIV prevalence. However, non-response in these surveys is of major concern especially if it is not random and complete case analysis becomes an inappropriate method to analyse the data. Therefore, a comprehensive analysis that will account for the missing data must be used to obtain unbiased HIV prevalence estimates. MethodsSerological samples were collected from participants who were resident in a Demographic Surveillance System (DSS) in Kisesa, Tanzania. HIV prevalence was estimated using three methods. Firstly, using the Complete case analysis (CCA), assuming data were Missing Completely at Random (MCAR). The other two methods, multiple imputations (MI) and inverse probability weighting (IPW), assumed that non-response was missing at random (MAR). For MI, a logistic regression model adjusting for age, sex, residence, and marital status was used to impute 20 datasets to re-estimate the HIV prevalence. Propensity for participating in the sero-survey and being tested for HIV given age, sex, residence, and marital status were generated using logistic regression models. Using the propensity scores, inverse probability weights were derived for participants who were tested for HIV.ResultsThe overall CCA HIV prevalence estimate was 6.6% (95% CI: 6.0-7.2), with 5.4% (95% CI: 4.6-6.3) in males and 7.3% (95% CI: 6.6-8.1) in females. Using MI, the overall HIV prevalence was 6.8% (95% CI: 6.2-7.5), 6.2% (95% CI: 5.1-7.3) in males and 7.4% (95% CI: 6.6-8.2) in females. Using IPW the overall HIV prevalence was 6.7% (95% CI: 6.1-7.4), with 5.5% (95% CI: 4.7-6.5) in males and 7.7% (95% CI: 7.0 - 8.6) in females. HIV prevalence differed significantly between age groups (p<0.001), with the highest estimate in males aged 35-39 and females aged 40-44, and lowest in both males and females aged 15-19 years.DiscussionThe results showed that both MI and IPW are reliable methods for estimating HIV prevalence in the presence of missing data. MI is superior to CCA and the IPW approaches as it had smaller standard errors and narrower 95% confidence intervals. Therefore, we recommend use of MI in estimating HIV prevalence to address the problem of varied types of missing data. However, further research is needed to determine the bias in estimates from MI and IPW.ConclusionsComplete case analysis underestimates HIV prevalence compared to methods that adjust for missing data. The best method to adjust for missing data in population surveys is through the use of multiple imputations.

Likelihood Approach for Bayesian Logistic Weighted Model

Increasing the response rate and minimizing non-response rates represent the primary challenges to researchers in performing longitudinal and cohort research. This is most obvious in the area of paediatric medicine. When there are missing data, complete case analysis makes findings biased. Inverse Probability Weighting (IPW) is one of many available approaches for reducing the bias using a complete case analysis. Here, a complete case is weighted by probability inverse of complete cases. The data of this work is collected from the neonatal intensive care unit at Erbil maternity hospital for the years 2012 to 2017. In total, 570 babies (288 male and 282 females) were born very preterm. The aim of this paper is to use inverse probability weighting on the Bayesian logistic model developmental outcome. The Mental Development Index (MDI) approach is used for assessing the cognitive development of those born very preterm. Almost half of the information for the babies was missing, meaning that we do not know whether they have cognitive development issues or they have not. We obtained greater precision in results and standard deviation of parameter estimates which are less in the posterior weighted model in comparison with frequent analysis.

When Is a Complete-Case Approach to Missing Data Valid? The Importance of Effect-Measure Modification

When estimating causal effects, careful handling of missing data is needed to avoid bias. Complete-case analysis is commonly used in epidemiologic analyses. Previous work has shown that covariate-stratified effect estimates from complete-case analysis are unbiased when missingness is independent of the outcome conditional on the exposure and covariates. Here, we assess the bias of complete-case analysis for adjusted marginal effects when confounding is present under various causal structures of missing data. We show that estimation of the marginal risk difference requires an unbiased estimate of the unconditional joint distribution of confounders and any other covariates required for conditional independence of missingness and outcome. The dependence of missing data on these covariates must be considered to obtain a valid estimate of the covariate distribution. If none of these covariates are effect-measure modifiers on the absolute scale, however, the marginal risk difference will equal the stratified risk differences and the complete-case analysis will be unbiased when the stratified effect estimates are unbiased. Estimation of unbiased marginal effects in complete-case analysis therefore requires close consideration of causal structure and effect-measure modification.

Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods

Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis—and especially the process of factor retention. Determining the correct number of factors is crucial for the analysis, yet little is known about how to deal with missingness in this process. Therefore, in a simulation study, six missing data methods (an expectation–maximization algorithm, predictive mean matching, Bayesian regression, random forest imputation, complete case analysis, and pairwise complete observations) were compared with respect to the accuracy of the parallel analysis chosen as retention criterion. Data were simulated for correlated and uncorrelated factor structures with two, four, or six factors; 12, 24, or 48 variables; 250, 500, or 1,000 observations and three different missing data mechanisms. Two different procedures combining multiply imputed data sets were tested. The results showed that no missing data method was always superior, yet random forest imputation performed best for the majority of conditions—in particular when parallel analysis was applied to the averaged correlation matrix rather than to each imputed data set separately. Complete case analysis and pairwise complete observations were often inferior to multiple imputation.

Missing data reporting in clinical pharmacy research

Purpose This study aimed to document the ways by which missing data were handled in clinical pharmacy research to provide an insight into the amount of attention paid to the importance of missing data in this field of research. Methods Our cross-sectional descriptive report evaluated 10 journals affiliated with pharmacy organizations in the United States, Canada, the United Kingdom, and Australia. Randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies published in 2018 were included. The primary outcome measure was the proportion of studies that reported the handling of missing data in their methods or results. Results A total of 178 studies were included in the analysis. Of these, 19.7% (n = 35) mentioned missing data either in their methods (3.4%, n = 6), results (15.2%, n = 27), or in both sections (1.1%, n = 2). Only 4.5% (n = 8) of the studies mentioned how they handled missing data, the most common method being multiple imputation (n = 3), followed by indicator (n = 2), complete case analysis (n = 2), and simple imputation (n = 1). One study using multiple imputation and both studies using an indicator method also combined other strategies to account for missing data. One study only used complete case analysis for subgroup analysis, and the other study only used this method if a specific baseline variable was missing. Conclusions Very few studies in clinical pharmacy literature report any handling of missing data. This has the potential to lead to biased results. We advocate that researchers should report how missing data were handled to increase the transparency of findings and minimize bias.

The Use of Multiple Imputation Techniques on Short-Term Clinical Complications of Patients Presenting with Traumatic Spinal Cord Injuries

Background: With the increase in the use of secondary data in epidemiological studies, the inquiry of how to manage missing data has become more relevant. Our study applied imputation techniques on traumatic spinal cord injuries data; a medical problem where data is generally sporadic. Traumatic spinal cord injuries due to blunt force cause widespread physiological impairments, medical and non-medical problems. The effects of spinal cord injuries are a burden not only to the victims but to their families and to the entire health system of a country. This study also evaluated the causes of traumatic spinal cord injuries in patients admitted to the University Teaching Hospital and factors associated with clinical complications in these patients. Methods: The study used data from medical records of patients who were admitted to the University Teaching Hospital in Lusaka, Zambia. Patients presenting with traumatic spinal cord injuries between 1st January 2013 and 31st December 2017 were part of the study. The data was first analysed using complete case analysis, then multiple imputation techniques were applied, to account for the missing data. Thereafter, both descriptive and inferential analyses were performed on the imputed data. Results: During the study period of interest, a total of 176 patients were identified as having suffered from spinal cord injuries. Road traffic accidents accounted for 56% (101) of the injuries. Clinical complications suffered by these patients included paralysis, death, bowel and bladder dysfunction and pressure sores among other things. Eighty-eight (50%) patients had paralysis. Patients with cervical spine injuries compared to patients with thoracic spine injuries had 87% reduced odds of suffering from clinical complications (OR=0.13, 95% CI{0.08, 0.22}p<.0001). Being paraplegic at discharge increased the odds of developing a clinical complication by 8.1 times (OR=8.01, 95% CI{2.74, 23.99}, p<.001). Under-going an operation increased the odds of having a clinical complication (OR=3.71, 95% CI{=1.99, 6.88}, p<.0001). A patient who presented with Frankel Grade C or E had a 96% reduction in the odds of having a clinical complication (OR=.04, 95% CI{0.02, 0.09} and {0.02, 0.12} respectively, p<.0001) compared to a patient who presented with Frankel Grade A. Conclusion: A comparison of estimates obtained from complete case analysis and from multiple imputations revealed that when there are a lot of missing values, estimates obtained from complete case analysis are unreliable and lack power. Efforts should be made to use ideas to deal with missing values such as multiple imputation techniques. The most common cause of traumatic spinal cord injuries was road traffic accidents. Findings suggest that paralysis had the greatest negative effect on clinical complications. When the category of Frankel Grade increased from A-E, the less likely a patient was likely to succumb to clinical complications. No evidence of an association was found between age, sex and developing a clinical complication.