A Framework for the Estimation of Disaggregated Statistical Indicators Using Tree-Based Machine Learning Methods

The thesis combines four papers that introduce a coherent framework based on MERFs for the estimation of spatially disaggregated economic and inequality indicators and associated uncertainties. Chapter 1 focusses on flexible domain prediction using MERFs. We discuss characteristics of semi-parametric point and uncertainty estimates for domain-specific means. Extensive model- and design-based simulations highlight advantages of MERFs in comparison to 'traditional' LMM-based SAE methods. Chapter 2 introduces the use of MERFs under limited covariate information. The access to population-level micro-data for auxiliary information imposes barriers for researchers and practitioners. We introduce an approach that adaptively incorporates aggregated auxiliary information using calibration-weights in the absence of unit-level auxiliary data. We apply the proposed method to German survey data and use aggregated covariate census information from the same year to estimate the average opportunity cost of care work for 96 planning regions in Germany. In Chapter 3, we discuss the estimation of non-linear poverty and inequality indicators. Our proposed method allows to estimate domain-specific cumulative distribution functions from which desired (non-linear) poverty estimators can be obtained. We evaluate proposed point and uncertainty estimators in a design-based simulation and focus on a case study uncovering spatial patterns of poverty for the Mexican state of Veracruz. Additionally, Chapter 3 informs a methodological discussion on differences and advantages between the use of predictive algorithms and (linear) statistical models in the context of SAE. The final Chapter 4 complements the previous research by implementing discussed methods for point and uncertainty estimates in the open-source R package SAEforest. The package facilitates the use of discussed methods and accessibly adds MERFs to the existing toolbox for SAE and official statistics. Overall, this work aims to synergize aspects from two statistical spheres (e.g. 'traditional' parametric models and nonparametric predictive algorithms) by critically discussing and adapting tree-based methods for applications in SAE. In this perspective, the thesis contributes to the existing literature along three dimensions: 1) The methodological development of alternative semi-parametric methods for the estimation of non-linear domain-specific indicators and means under unit-level and aggregated auxiliary covariates. 2) The proposition of a general framework that enables further discussions between 'traditional' and algorithmic approaches for SAE as well as an extensive comparison between LMM-based methods and MERFs in applications and several model and design-based simulations. 3) The provision of an open-source software package to facilitate the usability of methods and thus making MERFs and general SAE methodology accessible for tailored research applications of statistical, institutional and political practitioners

Flexible domain prediction using mixed effects random forests

This paper promotes the use of random forests as versatile tools for estimating spatially disaggregated indicators in the presence of small area-specific sample sizes. Small area estimators are predominantly conceptualised within the regression-setting and rely on linear mixed models to account for the hierarchical structure of the survey data. In contrast, machine learning methods offer non-linear and non-parametric alternatives, combining excellent predictive performance and a reduced risk of model-misspecification. Mixed effects random forests combine advantages of regression forests with the ability to model hierarchical dependencies. This paper provides a coherent framework based on mixed effects random forests for estimating small area averages and proposes a non-parametric bootstrap estimator for assessing the uncertainty of the estimates. We illustrate advantages of our proposed methodology using Mexican income-data from the state Nuevo León. Finally, the methodology is evaluated in model-based and design-based simulations comparing the proposed methodology to traditional regression-based approaches for estimating small area averages

Changes in the facial soft tissue profile after maxillary orthognathic surgery

OBJECTIVES To compare the changes of the soft tissue profile in relation to the displacement of the underlying hard structures in maxillary orthognathic surgery and to contribute to the esthetic prediction of the facial profile after surgical procedures. MATERIALS AND METHODS We analyzed the sagittal changes in the facial soft tissue profile related to surgical changes in skeletal structures after maxillary osteotomy in a retrospective study. The study sample comprised 115 adult patients between the ages of 18-50 years who had undergone maxillary orthognathic surgery and interdisciplinary orthodontic treatment at the Department of Orthodontics, Ludwig-Maximilians University of Munich, Germany. LeFort I osteotomy cases in both maxillary monognathic and bignathic osteotomy procedures were included. All subjects had received rigid fixation. A cephalometric analysis of presurgical and postsurgical cephalograms was performed and the correlations between hard tissue and soft tissue change ratios were evaluated using a bivariate linear regression analysis. A vertical line through the landmark sella (S) perpendicular to the nasion-sella line (NSL) served as the reference plane. RESULTS The subnasale (Sn) followed the A point (A) by 57%, the soft tissue A point (A') followed the A point (A) by 73% and the upper lip, represented by the landmark labrale superius (Ls) followed the upper incisor (Is) by 73%; all three in a linear correlation with a mean prediction error of nearly 2 mm. CONCLUSION The scatterplots show a linear correlation with a wide spread for all three pairs of reference points. The wide spread and the high prediction error of almost 2 mm indicate low predictability of the expected lip position and Sn.ZUSAMMENFASSUNG ZIELE: Die Veränderungen des Weichgewebeprofils in Relation zur Verlagerung der darunter liegenden Hartgewebe durch maxilläre orthognathe Chirurgie zu vergleichen und einen Beitrag zur ästhetischen Prognose des Gesichtsprofils nach chirurgischen Maßnahmen zu leisten. MATERIAL UND METHODE In einer retrospektiven Studie analysierten wir die sagittalen Veränderungen des fazialen Weichgewebeprofils in Beziehung zu den chirurgischen Veränderungen der skelettalen Strukturen nach maxillärer Osteotomie. Die Studienprobe besteht aus 115 erwachsenen Patienten im Alter von 18–50 Jahren, die sich interdisziplinär maxillärer orthognather Chirurgie und kieferorthopädischer Therapie an der Poliklinik für Kieferorthopädie der Ludwig-Maximilians-Universität München unterzogen hatten. LeFort-I-Osteotomie-Fälle sowohl maxillärer monognather als auch bignather Osteotomieverfahren wurden in die Studie aufgenommen. Alle Patienten hatten eine starre Fixierung erhalten. Es wurde eine kephalometrische Analyse von präoperativen und postoperativen Fernröntgenseitenbildern durchgeführt, die Korrelationen zwischen Hart- und Weichgewebeveränderungen wurden mittels einer bivariaten linearen Regressionsanalyse ausgewertet. Als Referenzebene diente eine vertikale Linie durch den Referenzpunkt Sella (S), rechtwinklig zur Nasion-Sella-Linie (NSL). ERGEBNISSE Der Punkt Subnasale (Sn) folgte dem A\hbox-Punkt (A) um 57 %, der Weichgewebe-A-Punkt (A′) folgte dem A-Punkt (A) um 73 %, und die Oberlippe, repräsentiert durch den Referenzpunkt Labrale superius (Ls), folgte dem Inzision superius (Is) um 73 %, alle 3 in einer linearen Korrelation bei einer mittleren Abweichung von fast 2 mm. FAZIT Die Punktdiagramme zeigen eine lineare Korrelation mit einer breiten Streuung aller 3 Referenzpunktpaare. Die breite Streuung und die hohe mittlere Abweichung von fast 2 mm lassen auf eine schwache Vorhersagbarkeit der zu erwartenden Position von Oberlippe und Sn schließen

The use of definitive implant abutments for the fabrication of provisional crowns: a case series

Purpose: The anterior region is a challenge for most clinicians to achieve optimal esthetics with dental implants. The provisional crown is a key factor in the success of obtaining pink esthetics around restorations with single implants, by soft tissue and inter-proximal papilla shaping. Provisional abutments bring additional costs and make the treatment more expensive. Since one of the aims of the clinician is to reduce costs and find more economic ways to raise patient satisfaction, this paper describes a practical method for chair-side fabrication of non-occlusal loaded provisional crowns used by the authors for several years successfully

Is there an effect of crown-to-implant ratio on implant treatment outcomes? A systematic review

Objectives: High crown-to-implant ratios may lead to complications due to unfavorable occlusal forces, including nonaxial forces, on the bone surrounding the neck of the implant and within the connection of the crown and implant itself. The aim of this study was to perform a systematic review on the influence of crown-to-implant ratio of single-tooth, nonsplinted, implants on biological and technical complications. Materials and Methods: MEDLINE (1950-January 2018), EMBASE (1966-January 2018), and Cochrane Central Register of Controlled Trials database (1800-January 2018) were searched to identify eligible studies. Inclusion criteria were as follows: crown-to-implant ratio of single-tooth, nonsplinted, implant-supported restorations in the posterior maxilla or mandible and follow-up of at least 1 year. Main outcome measures were as follows: implant survival rate, marginal bone level changes, biological complications, and technical complications. Two reviewers independently assessed the articles. A meta-analysis was carried out for implant survival rate and peri-implant bone changes. Results: Of 154 primarily selected articles, eight studies fulfilled the inclusion criteria. Study groups presented a mean crown-to-implant ratio varying from 0.86 (with 10-mm implants) to 2.14 (with 6-mm implants). The meta-analysis showed an implant survival of more than 99% per year and mean peri-implant bone changes of Conclusion: Data reviewed in the current manuscript on crown-to-implant ratio, ranging from 0.86 to 2.14, of single-tooth, nonsplinted, implants did not demonstrate a high occurrence of biological or technical complications