scholarly journals Reporting and handling of incomplete outcome data in implant dentistry: A survey of randomized clinical trials

2019 ◽  
Vol 47 (2) ◽  
pp. 257-266 ◽  
Author(s):  
Ricarda Lieber ◽  
Nikolaos Pandis ◽  
Clovis Mariano Faggion
Keyword(s):  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Outcome Data ◽  
Implant Dentistry

scholarly journals EFFECTS OF POSTURAL EDUCATION IN ELEMENTARY SCHOOL CHILDREN: A SYSTEMATIC REVIEW

2021 ◽  
Vol 39 ◽  
Author(s):  
Paola Janeiro Valenciano ◽  
Fabíola Unbehaun Cibinello ◽  
Jessica Caroliny de Jesus Neves ◽  
Dirce Shizuko Fujisawa
Keyword(s):  
Clinical Trials ◽  
Elementary School ◽  
High Risk ◽  
School Children ◽  
Elementary School Children ◽  
Randomized Clinical Trials ◽  
Outcome Data ◽  
Risk Of Bias ◽  
Eligibility Criteria ◽  
Positive Effects

ABSTRACT Objective: To determine the effect of postural education on the learning and postural habits of elementary school children without physical intervention. Methods: We searched PubMed, Lilacs, SciELO, Cochrane, and Science Direct data bases and reference lists of studies in February 2020. The eligibility criteria were randomized clinical trials related to the effect of postural education in children aged between 6 and 12 years old. Two authors independently assessed trials for inclusion and risk of bias: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Data were extracted in standardized tables including information on author, publication year, country, sample size, age, sex, intervention characteristics, outcome measurements and results. Results: We found seven clinical trials (involving 2,568 children) for the review. The studies were conducted between 2000 and 2018: four in Belgium, two in Spain, and one in Germany. All seven included trials underwent evaluation: only one had a clear process of randomization and allocation concealment. All included studies were judged as having high risk of bias in at least one domain or have concerns for multiple domains. Conclusions: The positive effects of acquired knowledge and postural habits found in the studies cannot be used to reliably support postural education in elementary school children due to a high risk of bias in the evaluated studies.

scholarly journals The Application of Beta-Tricalcium Phosphate in Implant Dentistry: A Systematic Evaluation of Clinical Studies

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 655
Author(s):  
Elisabet Roca-Millan ◽  
Enric Jané-Salas ◽  
Antonio Marí-Roig ◽  
Álvaro Jiménez-Guerra ◽  
Iván Ortiz-García ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Survival Rate ◽  
Tricalcium Phosphate ◽  
Randomized Clinical Trials ◽  
Systematic Evaluation ◽  
Control Group ◽  
Cochrane Central Register ◽  
Graft Material ◽  
Synthetic Graft ◽  
Implant Dentistry

The demand for synthetic graft materials in implant dentistry is rising. This systematic review aims to evaluate the survival rate of dental implants placed simultaneously with bone regeneration procedures using the material β-tricalcium phosphate, one of the most promising synthetic graft materials. The electronic search was conducted in PubMed, Scielo, and the Cochrane Central Register of Controlled Trials. There were five randomized clinical trials, one of which was a non-randomized controlled clinical trial and four of which were observational studies without a control group included. Implant survival rate and other clinical, radiographic, and histological parameters did not differ from those of implants placed simultaneously with another type of graft material, or placed in blood clots or natural alveolar ridges. Based on the available literature, β-tricalcium phosphate seems to be a promising graft material in implant dentistry. Nevertheless, more randomized clinical trials, with long follow-up periods, preoperative and postoperative CBCT, and histological analysis, are necessary to assess its long-term behavior.

scholarly journals Evidence favoring the efficacy of convalescent plasma for COVID-19 therapy

2020 ◽  
Author(s):  
Stephen A. Klassen ◽  
Jonathon W. Senefeld ◽  
Patrick W. Johnson ◽  
Rickey E. Carter ◽  
Chad C. Wiggins ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Clinical Trials ◽  
Case Report ◽  
Mortality Rate ◽  
Therapeutic Agent ◽  
Randomized Clinical Trials ◽  
Matched Control ◽  
Case Series ◽  
Outcome Data ◽  
Control Case

AbstractTo determine the effect of COVID-19 convalescent plasma on mortality, we aggregated patient outcome data from randomized clinical trials (RCT), matched-control, case series, and case report studies. Random-effects analyses of RCT data demonstrated that hospitalized COVID-19 patients transfused with convalescent plasma exhibited a lower mortality rate compared to patients receiving standard treatments. These data provide evidence favoring the efficacy of human convalescent plasma as a therapeutic agent in hospitalized COVID-19 patients.

scholarly journals A novel complete-case analysis to determine statistical significance between treatments in an intention-to-treat population of randomized clinical trials involving missing data

2016 ◽  
Vol 27 (4) ◽  
pp. 1067-1075 ◽  
Author(s):  
Wei Liu ◽  
Jinhui Ding
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Randomized Clinical Trials ◽  
Statistical Significance ◽  
Outcome Data ◽  
Complete Case ◽  
Significant Treatment ◽  
Significance Level ◽  
Intention To Treat ◽  
The Impact

The application of the principle of the intention-to-treat (ITT) to the analysis of clinical trials is challenged in the presence of missing outcome data. The consequences of stopping an assigned treatment in a withdrawn subject are unknown. It is difficult to make a single assumption about missing mechanisms for all clinical trials because there are complicated reactions in the human body to drugs due to the presence of complex biological networks, leading to data missing randomly or non-randomly. Currently there is no statistical method that can tell whether a difference between two treatments in the ITT population of a randomized clinical trial with missing data is significant at a pre-specified level. Making no assumptions about the missing mechanisms, we propose a generalized complete-case (GCC) analysis based on the data of completers. An evaluation of the impact of missing data on the ITT analysis reveals that a statistically significant GCC result implies a significant treatment effect in the ITT population at a pre-specified significance level unless, relative to the comparator, the test drug is poisonous to the non-completers as documented in their medical records. Applications of the GCC analysis are illustrated using literature data, and its properties and limits are discussed.

scholarly journals Can We Explain Heterogeneity Among Randomized Clinical Trials of Exercise for Chronic Back Pain? A Meta-Regression Analysis of Randomized Controlled Trials

2010 ◽  
Vol 90 (10) ◽  
pp. 1383-1403 ◽  
Author(s):  
Manuela L. Ferreira ◽  
Rob J.E.M. Smeets ◽  
Steven J. Kamper ◽  
Paulo H. Ferreira ◽  
Luciana A.C. Machado
Keyword(s):  
Clinical Trials ◽  
Back Pain ◽  
Randomized Clinical Trials ◽  
Chronic Back Pain ◽  
Data Extraction ◽  
Demographic Data ◽  
Patient Characteristics ◽  
Outcome Data ◽  
Effect Sizes ◽  
Meta Regression

BackgroundExercise programs may vary in terms of duration, frequency, and dosage; whether they are supervised; and whether they include a home-based program. Uncritical pooling of heterogeneous exercise trials may result in misleading conclusions regarding the effects of exercise on chronic low back pain (CLBP).PurposeThe purpose of this study was to establish the effect of exercise on pain and disability in patients with CLBP, with a major aim of explaining between-trial heterogeneity.Data SourcesSix databases were searched up to August 2008 using a computerized search strategy.Study SelectionEligible studies needed to be randomized clinical trials evaluating the effects of exercise for nonspecific CLBP. Outcomes of interest were pain and disability measured on a continuous scale.Data ExtractionBaseline demographic data, exercise features, and outcome data were extracted from all included trials.Data SynthesisUnivariate meta-regressions were conducted to assess the associations between exercise effect sizes and 8 study-level variables: baseline severity of symptoms, number of exercise hours and sessions, supervision, individual tailoring, cognitive-behavioral component, intention-to-treat analysis, and concealment of allocation.LimitationsOnly study-level characteristics were included in the meta-regression analyses. Therefore, the implications of the findings should not be used to differentiate the likelihood of the effect of exercise based on patient characteristics.ConclusionsThe results show that, in general, when all types of exercise are analyzed, small but significant reductions in pain and disability are observed compared with minimal care or no treatment. Despite many possible sources of heterogeneity in exercise trials, only dosage was found to be significantly associated with effect sizes.

scholarly journals Addressing Missing Data in Randomized Clinical Trials: A Causal Inference Perspective

2019 ◽  
Author(s):  
Ilja Cornelisz ◽  
Pim Cuijpers ◽  
Tara Donker ◽  
Chris van Klaveren
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Causal Inference ◽  
Treatment Effect ◽  
Randomized Clinical Trials ◽  
Outcome Data ◽  
Average Treatment Effect ◽  
Predictor Variables ◽  
Average Treatment ◽  
Point Estimates

Abstract Background The importance of randomization in clinical trials has long been acknowledged for avoiding selection bias. Yet, bias concerns re-emerge with selective attrition. This study takes a causal inference perspective in addressing distinct scenarios of missing outcome data (MCAR, MAR and MNAR). Methods This study adopts a causal inference perspective in providing an overview of empirical strategies to estimate the average treatment effect, improve precision of the estimator, and to test whether the underlying identifying assumptions hold. We propose to use Random Forest Lee Bounds (RFLB) to address selective attrition and to obtain more precise average treatment effect intervals. Results When assuming MCAR or MAR, the often untenable identifying assumptions with respect to causal inference can hardly be verified empirically. Instead, missing outcome data in clinical trials should be considered as potentially non-random unobserved events (i.e. MNAR). Using simulated attrition data, we show how average treatment effect intervals can be tightened considerably using RFLB, by exploiting both continuous and discrete attrition predictor variables. Conclusions Bounding approaches should be used to acknowledge selective attrition in randomized clinical trials in acknowledging the resulting uncertainty with respect to causal inference. As such, Random Forest Lee Bounds estimates are more informative than point estimates obtained assuming MCAR or MAR.

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