PENGARUH PEMBELAJARAN DISCOVERY LEARNING TERHADAP MOTIVASI, AKTIVITAS, DAN HASIL BELAJAR IPS SISWA KELAS IV SDN 108 MONCONGLOE

Among the number of learning models that can be found in the literature; Discovery Learning is a learning model that is currently being developed quite a lot. This research was aimed at re-examining the influence of the Discovery Learning model on the Motivation, Activities and Social Studies Learning Outcomes of students in class IV at SDN Moncongloe. Relying on statistical accuracy and precision – explicitly, this research is a quantitative study with a Quasi Experimental Design approach for controlled experiments. The results show an increase in students' motivation, activity and learning outcomes after being given treatment, and the significance effect of the model is proven by the significance of all values variables with smaller than 0.05 or <0.05

Practical help for specifying the target difference in sample size calculations for RCTs: the DELTA2 five-stage study, including a workshop

BACKGROUND: The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest. This difference is called the 'target difference' and should be appropriate for the principal estimand of interest and determined by the primary aim of the study. The target difference between treatments should be considered realistic and/or important by one or more key stakeholder groups. OBJECTIVE: The objective of the report is to provide practical help on the choice of target difference used in the sample size calculation for a randomised controlled trial for researchers and funder representatives. METHODS: The Difference ELicitation in TriAls2 (DELTA2) recommendations and advice were developed through a five-stage process, which included two literature reviews of existing funder guidance and recent methodological literature; a Delphi process to engage with a wider group of stakeholders; a 2-day workshop; and finalising the core document. RESULTS: Advice is provided for definitive trials (Phase III/IV studies). Methods for choosing the target difference are reviewed. To aid those new to the topic, and to encourage better practice, 10 recommendations are made regarding choosing the target difference and undertaking a sample size calculation. Recommended reporting items for trial proposal, protocols and results papers under the conventional approach are also provided. Case studies reflecting different trial designs and covering different conditions are provided. Alternative trial designs and methods for choosing the sample size are also briefly considered. CONCLUSIONS: Choosing an appropriate sample size is crucial if a study is to inform clinical practice. The number of patients recruited into the trial needs to be sufficient to answer the objectives; however, the number should not be higher than necessary to avoid unnecessary burden on patients and wasting precious resources. The choice of the target difference is a key part of this process under the conventional approach to sample size calculations. This document provides advice and recommendations to improve practice and reporting regarding this aspect of trial design. Future work could extend the work to address other less common approaches to the sample size calculations, particularly in terms of appropriate reporting items. FUNDING: Funded by the Medical Research Council (MRC) UK and the National Institute for Health Research as part of the MRC-National Institute for Health Research Methodology Research programme

Theory and practice of mixed models applied to medical research

This thesis examines in depth the properties of mixed models and considers their application in a variety of designs used in medical research. Mixed models are a broad class of models which allow variation in the data to be modelled at several levels and take into account correlations occurring between observations. They offer several potential advantages over the more conventional fixed effects approaches: more efficient estimates, effective handling of missing data and more appropriate inference. The different types of mixed model are placed into a unified format and the properties of various fitting methods, including likelihood-based methods, least squares methods and the Bayesian approach, are considered in detail. The practical implications of using mixed models are examined and the submitted material would appear to be the first to consider these in such depth. The particular features of applying mixed models to a range of designs are considered including repeated measures, crossover, multi-centre, meta analysis, cluster randomised, hierarchical, bioequivalence and several more ad hoc designs. Novel approaches are introduced for sample size estimation and for analysing crossover designs with multiple periods, bioequivalence studies and case-control studies. Comparisons of mixed models with fixed effects models, which have often previously been the conventional approach, are given particular attention. Models suitable for both normal and non-normal data are considered and examples involving original analyses are used to illustrate the properties described. The published material comprises two editions of a textbook and ten journal publications

The evaluation of in vivo release rates of pharmaceutical preparations

Traditional methods for the assessment of drug delivery concentrate on the analysis of the absorption process, however, more recent techniques have enabled the actual release rates of the drugs to be determined. Direct evaluation of the release rate in vivo is not practical, as such an approach would be excessively invasive, therefore information about the in vivo release process must come from the manipulation of other data. Two methods in particular (Maximum Entropy and Deconvolution) have the ability to provide information about the whole time course of release and can separate the in vivo release process from that of absorption. The Maximum Entropy approach and various deconvolution algorithms were examined for stability to data noise and their ability to predict correctly both the form and values of unknown release rates. This examination was made using pseudo-experimental data, so that the true form of the unknown release rate was known prior to analysis, and using clinical data arising from the administration of controlled release metoprolol tablets. A comparison was made of all the methods tested to find the optimal method for the assessment of in vivo release. The results obtained showed that no one method is optimal for all aspects of the assessment of drug release, but that the method of choice is dependent on the information required. The Maximum Entropy method was shown to be preferred when the aim of the assessment was the study of the in vivo release rate as a function of time. However, if a less in depth assessment is required (eg the calculation of MDT or the fraction of dose released vs time) then there was no advantage shown to the use of the more complex methods and one of the simpler deconvolution algorithms becomes the method of choice