Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response

Instruction is a multidimensional procedure including a quantity of features, e.g., tutor qualities, that occasionally are hard to assess. In certain points, education efficiency, that is a part of instructing, is affected by a combination of teacher features for example, capacity and clarity to encourage the students to make them study of his subjects, capacity to establish the lesson also with trainings and lectures. These aspects are not only attributable to motivate students, but also age, gender, prior experiences, As more and more the effectiveness of teaching is becoming even more significant in school evaluation system, it is, indeed, essential to discover how to assess it and find the significant related factors for giving the best rank of feedback to their tutors. This paper focuses the assessment of teaching effectiveness forum from students’ perspective “Feedback”, examine the questionnaires provided to the students of Cihan University at the end of their courses and discover the most effected teacher’s characteristics. The outcome variable (Student’s Feedback) was recorded on well-ordered, five-point scale of Likert provided by the students, to a set of independent variables to teacher level. The major techniques elaborated in the model fitting for ordinal regression were stating which independent variables are most likely to be kept in the model and selecting the link function such as, (logit link, complementary log-log link, negative log-log link and probit link) which verified the model suitability. Further to that, several statistical diagnoses have conducted like the model fitting, classification accuracy and the validity assumptions of the model, which is parallel lines, were fundamentally calculated in choosing the best fitted model. The dataset implemented in the analysis entails of almost (21566) respondents to the formed questionnaire in relation to courses of Cihan University for the 2018 – 2019 academic year.

Identifiability of causal effects with multiple causes and a binary outcome

Unobserved confounding presents a major threat to causal inference from observational studies. Recently, several authors suggest that this problem may be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that the causal effect is indeed identifiable if we assume a general binary choice model for the outcome with a non-probit link. Our identification approach is based on the incongruence between Gaussianity of the treatments and latent confounder, andnon-Gaussianity of a latent outcome variable. We further develop a two-step likelihood-based estimation procedure.

Longitudinal assessment of utilities in patients with migraine: an analysis of erenumab randomized controlled trials

Background Cost-effectiveness analyses in patients with migraine require estimates of patients’ utility values and how these relate to monthly migraine days (MMDs). This analysis examined four different modelling approaches to assess utility values as a function of MMDs. Methods Disease-specific patient-reported outcomes from three erenumab clinical studies (two in episodic migraine [NCT02456740 and NCT02483585] and one in chronic migraine [NCT02066415]) were mapped to the 5-dimension EuroQol questionnaire (EQ-5D) as a function of the Migraine-Specific Quality of Life Questionnaire (MSQ) and the Headache Impact Test (HIT-6™) using published algorithms. The mapped utility values were used to estimate generic, preference-based utility values suitable for use in economic models. Four models were assessed to explain utility values as a function of MMDs: a linear mixed effects model with restricted maximum likelihood (REML), a fractional response model with logit link, a fractional response model with probit link and a beta regression model. Results All models tested showed very similar fittings. Root mean squared errors were similar in the four models assessed (0.115, 0.114, 0.114 and 0.114, for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model respectively), when mapped from MSQ. Mean absolute errors for the four models tested were also similar when mapped from MSQ (0.085, 0.086, 0.085 and 0.085) and HIT-6 and (0.087, 0.088, 0.088 and 0.089) for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model, respectively. Conclusions This analysis describes the assessment of longitudinal approaches in modelling utility values and the four models proposed fitted the observed data well. Mapped utility values for patients treated with erenumab were generally higher than those for individuals treated with placebo with equivalent number of MMDs. Linking patient utility values to MMDs allows utility estimates for different levels of MMD to be predicted, for use in economic evaluations of preventive therapies. Trial registration ClinicalTrials.gov numbers of the trials used in this study: STRIVE, NCT02456740 (registered May 14, 2015), ARISE, NCT02483585 (registered June 12, 2015) and NCT02066415 (registered Feb 17, 2014).

Rheumatoid arthritis treated with 6-months of first-line biologic or biosimilar therapy: an updated systematic review and network meta-analysis

ObjectivesThe aim of this study was to estimate the effectiveness of first-line biologic disease modifying drugs(boDMARDs), and their approved biosimilars (bsDMARDs), compared with conventional (csDMARD) treatment, in terms of ACR (American College of Rheumatology) and EULAR (European League against Rheumatism) responses.MethodsSystematic literature search, on eight databases to January 2017, sought ACR and EULAR data from randomized controlled trials (RCTs) of boDMARDs / bsDMARDs (in combination with csDMARDs, or monotherapy). Two adult populations: methotrexate (MTX)-naïve patients with severe active RA; and csDMARD-experienced patients with moderate-to-severe active RA. Network meta-analyses (NMA) were conducted using a Bayesian Markov chain Monte Carlo simulation using a random effects model with a probit link function for ordered categorical.ResultsForty-six RCTs met the eligibility criteria. In the MTX-naïve severe active RA population, no biosimilar trials meeting the inclusion criteria were identified. MTX plus methylprednisolone (MP) was most likely to achieve the best ACR response. There was insufficient evidence that combination boDMARDs was superior to intensive (two or more) csDMARDs. In the csDMARD-experienced, moderate-to-severe RA population, the greatest effects for ACR responses were associated with tocilizumab (TCZ) monotherapy, and combination therapy (plus MTX) with bsDMARD etanercept (ETN) SB4, boDMARD ETN and TCZ. These treatments also had the greatest effects on EULAR responses. No clear differences were found between the boDMARDs and their bsDMARDs.ConclusionsIn MTX-naïve patients, there was insufficient evidence that combination boDMARDs was superior to two or more csDMARDs. In csDMARD-experienced patients, boDMARDs and bsDMARDs were comparable and all combination boDMARDs / bsDMARDs were superior to single csDMARD.

Testing Hardy–Weinberg disequilibrium using the generalized linear model

SummaryCurrent methods for detecting Hardy–Weinberg disequilibrium (HWD) only deal with one locus at a time. We developed a method that can jointly detect HWD for multiple loci. The method was developed under the generalized linear model (GLM) using the probit link function. When applied to a single locus, the new method is more powerful than the exact test. When applied to two or more loci, the method can reduce false positives caused by linkage disequilibrium (LD). We applied the method to 24 single nucleotide polymorphism (SNP) markers of a single human gene and eliminated several false positive HWDs due to LD. We developed an R package ‘hwdglm’ for joint HWD detection, which can be downloaded from our personal website (www.statgen.ucr.edu).