Logistics Regression Modelling on Student Career Path Choices at the Statistics Department, FMIPA UNM Makassar

Survey of career path that students plan after completing their undergraduate in statistics department was carried out through questionnaire on google form from May 6 to May 24, 2021. There were 114 students who filled out the questionnaire, consisting of 20 students from class 2018, 23 students from class 2019, and 71 students from class 2020. Dependent variable is career path plan (Y), while independent variables are tendency to choose career paradigm (CPC), gender (GDR), Grade Point Average (GPA), parental occupation (POC), number of siblings (NOS), place of birth (POB) and year of university entrance (YOE). The data are analysed by binary logistic model with logit transformation and the result is g ( Y ) = ln [ π ( Y ) 1 − π ( Y ) ] = − 13 , 525 + 2 , 332 ( CPC ) − 1 , 036 ( GDR ) + 4 , 466 ( GPA ) + 2 , 421 ( POC 1 ) − 0 , 405 ( POC 2 ) + 2 , 390 ( POC 3 ) + 0 , 236 ( NOS ) − 1 , 817 ( POB ) + 0 , 448 ( YOE 1 ) − 2 , 660 ( YOE 2 ) Results of the analysis show that predictive power of the model to explain tendency of students to choose career according to statistics is around 34% to 56% based on variables in the model. Beside that tendency of career paradigm choice (CPC), Grade Point Average (GPA), parental occupation (POC1 civil servant), place of birth (POB), and year of entry (YOE2 2019) significantly affect chosen career goals by student.

A two-parameter hazard function to describe age patterns of mortality in ancient Northwestern Europe

When seeking to describe the age patterns of mortality for ancient populations, researchers are often confronted with small sample sizes or with missing data for several age groups. The traditional approach to dealing with these challenges is to smooth or complement such patterns by matching them to a model life table, either directly or through the Brass logit transformation. This procedure requires an appropriate model life table, which may not be available. We propose a hazard model that is both flexible enough to accurately describe an age pattern of mortality in ancient Northwestern Europe and restrictive enough to complement incomplete data. This paper presents a hazard function that contains four free-to-choose parameters. Tested against a large collection of life tables for northwestern European countries from the 17th to the 21st century, the number of free-to-choose parameters is stepwise reduced from four to only two. Compared with the Brass logit transformation with the Princeton Model West as its reference, the presented two-parameter hazard model is shown to fit the abovementioned dataset much better. The mean fitting error is found to be half the size. Moreover, this model is shown to fit a 13th-century mortality age pattern much better. The proposed two-parameter hazard model is capable of fitting a wide range of age patterns of mortality more closely than the traditional approach can. We therefore conclude that the proposed model facilitates the smoothing and the completion of age patterns of mortality in ancient Northwestern Europe even if they deviate substantially from well-documented patterns.

Clinical utility of DaTscan in patients with suspected Parkinsonian syndrome: a systematic review and meta-analysis

Images of DaTscan (ioflupane [123I] SPECT) have been used as an adjunct to clinical diagnosis to facilitate the differential diagnosis of neurodegenerative (ND) Parkinsonian Syndrome (PS) vs. non-dopamine deficiency aetiologies of Parkinsonism. Despite several systematic reviews having summarised the evidence on diagnostic accuracy, the impact of imaging results on clinical utility has not been systematically assessed. Our objective was to examine the available evidence on the clinical utility of DaTscan imaging in changing diagnosis and subsequent management of patients with suspected PS. We performed a systematic review of published studies of clinical utility from 2000 to 2019 without language restrictions. A meta-analysis of change in diagnosis and management rates reported from each study was performed using a random-effects model and logit transformation. Sub-group analysis, meta-regression and sensitivity analysis was performed to explore heterogeneity. Twenty studies met the inclusion criteria. Thirteen of these contributed to the meta-analyses including 950 and 779 patients with a reported change in management and change in diagnosis, respectively. The use of DaTscan imaging resulted in a change in management in 54% (95% CI: 47–61%) of patients. Change in diagnosis occurred in 31% (95% CI: 22–42%) of patients. The two pooled analyses were characterised by high levels of heterogeneity. Our systematic review and meta-analysis show that imaging with DaTscan was associated with a change in management in approximately half the patients tested and the diagnosis was modified in one third. Regardless of time from symptom onset to scan results, these changes were consistent. Further research focusing on specific patient subgroups could provide additional evidence on the impact on clinical outcomes.

Post-stratification as a tool for enhancing the predictive power of classification methods

It is well known that, in classification problems, the predictive capacity of any decision-making model decreases rapidly with increasing asymmetry of the target variable (Sonquist et al., 1973; Fielding 1977). In particular, in segmentation analysis with a categorical target variable, very poor improvements of purity are obtained when the least represented modality counts less than 1/4 of the cases of the most represented modality. The same problem arises with other (theoretically more exhaustive) techniques such as Artificial Neural Networks. Actually, the optimal situation for classification analyses is the maximum uncertainty, that is, equidistribution of the target variable. Some classification techniques are more robust, by using, for example, the less sensitive logit transformation of the target variable (Fabbris & Martini 2002); however, also the logit transformation is strongly affected by the distributive asymmetry of the target variable. In this paper, starting from the results of a direct survey in which the target (binary) variable was extremely asymmetrical (10% vs. 90%, or greater asymmetry), we noted that also the logit model with the most significant parameters had very reduced fitting measures and almost zero predictive power. To solve this predictive issue, we tested post-stratification techniques, artificially symmetrizing a training sample. In this way, a substantially increase of fitting and predictive capacity was achieved, both in the symmetrized sample and, above all, in the original sample. In conclusion of the paper, an application of the same technique to a dataset of very different nature and size is described, demonstrating that the method is stable even in the case of analysis executed with all data of a population.

Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

Quadriceps activation during maximal isometric and isokinetic contractions: The minimal real difference and its implications

BACKGROUND: A method of measurement of voluntary activation (VA, percent of full muscle recruitment) during isometric and isokinetic concentric contractions of the quadriceps femoris (QF) at 60∘/s and 120∘/s was previously validated. OBJECTIVE: This study aimed to quantify the test-retest minimal real difference (MRD) of VA during isometric (ISOM) and isokinetic concentric contractions of QF (100∘/s, ISOK) in a sample of healthy individuals. METHODS: VA was measured through the interpolated twitch technique. Pairs of electrical stimuli were delivered to the QF at 40∘ of knee flexion during maximal voluntary contractions. Twenty-five healthy participants (20–38 years, 12 women, 13 men) completed two testing sessions with a 14-day interval. VA values were linearized through logit transformation (VAl). The MRD was estimated from intraclass correlation coefficients (model 2.1). RESULTS: The VA (median, range) was 84.20% (38.2–99.9%) in ISOM and 94.22% (33.8-100%) in ISOK. MRD was 0.78 and 1.12 logit for ISOM and ISOK, respectively. As an example, in terms of percent VA these values correspond to a change from 76% to 95% and from 79% to 98% in ISOM and in ISOK, respectively. CONCLUSIONS: The provided MRD values allow to detect significant individual changes in VA, as expected after training and rehabilitation programs.

Maintenance Therapy in Newly Diagnosed and Transplant Ineligible Multiple Myeloma Patients: A Meta-Analysis

Introduction: Multiple myeloma (MM) is an incurable plasma cell disorder with more than two-thirds of the newly diagnosed multiple myeloma (NDMM) patients being ≥65 years of age. Recent advancements in the treatment of MM with novel agents and autologous stem cell transplantation (ASCT) have significantly improved survival in those patients. However, the management of NDMM in many elderly patients remains a challenge due to frailty, multiple comorbidities, and their ineligibility for ASCT. Our aim in this study is to review and analyze the safety and efficacy of different maintenance regimes for NDMM patients who are ineligible for ASCT. Methods: A comprehensive literature search was done on four databases (PubMed, Embase, Cochrane, and Clinicaltrials.gov). A search was performed without the use of filters and using MeSH terms for multiple myeloma and maintenance therapy (MT). Studies involving transplant-ineligible NDMM patients which reported overall response rate (ORR), complete response (CR), very good partial response (VGPR), or partial response (PR) along with adverse effects were included. A pooled analysis of the extracted data was performed using the "meta" package by Schwarzer et al. in the R programming language (version 4.0.2). The event rates were pooled using the inverse variance method and logit transformation. The between-studies variance was calculated using the DerSimonian-Laird estimator. The random-effects model was used for the analysis. Results: Lenalidomide based MT: Four studies involving 950 transplant-ineligible NDMM patients were included. All patients received lenalidomide (R) based MT following induction therapy. A pooled analysis of these studies showed ORR of 88% (95% confidence interval (CI): 81%-93%, p < 0.01) with 67% of the patients showing ≥VGPR (95% CI: 48%-82%, p <0.01) (Figure 1 A). The most common ≥ grade 3 hematological adverse effects (AE) included neutropenia, anemia, and thrombocytopenia while most common ≥ grade 3 non-hematological AE were infections, diarrhea, and fatigue (Table 1). Bortezomib based MT: Five clinical trials involving 1171 NDMM patients who received bortezomib (V) based MT were included and evaluated in our study (Figure 1 B). Pooled analysis showed ORR of 84% (95% CI: 74%-91%, p <0.01) with 32% patients showing CR (95% CI: 25%-41%, p <0.01). The most common ≥ grade 3 hematological and non-hematological AE's are reported in table 1. Ixazomib based MT: A total of 202 transplant-ineligible NDMM patients were evaluated for a response after receiving ixazomib (I) based MT in 5 clinical trials (Figure 1 C). A pooled analysis of these trials showed an ORR of 86% (95% CI: 69%-94%, p: <0.01) with 60% patients having ≥ VGPR (95% CI: 40%-76%, p <0.01). The most common ≥ grade 3 hematological AE included anemia (9%), neutropenia (15%), and thrombocytopenia (3%). (Table 1) Conclusion: V, R and I based MT has shown promising efficacy with an acceptable safety profile in transplant-ineligible NDMM patients. R based MT has shown superior ORR compared to V or I based MT. However, data from more clinical trials are needed. Disclosures Anwer: Incyte, Seattle Genetics, Acetylon Pharmaceuticals, AbbVie Pharma, Astellas Pharma, Celegene, Millennium Pharmaceuticals.: Honoraria, Research Funding, Speakers Bureau.

Cigarette Smoking Is More Prevalent in Patients With Brain Arteriovenous Malformations Compared to General Population: A Cross-Sectional Population-Based Study

BACKGROUND Research on the prevalence of smokers in patients with brain arteriovenous malformation (AVM) remains nonexistent, even though smoking is a well-known risk factor for intracranial aneurysms. OBJECTIVE To examine the prevalence and smoking habits of AVM patients. METHODS Data on smoking habits were collected with a quality-of-life questionnaire mailed in 2016 to all patients in our large AVM database. These smoking data were supplemented with registry data derived from medical records. The prevalence of smokers was compared to that of the general population, derived from statistics of National Institute for Health and Welfare. Logit transformation of proportions and Students t distribution were used to calculate the 95% CIs for prevalence estimates. RESULTS Of the 384 patients aged over 18 yr on admission, 277 (72.1%) returned the questionnaires in 2016. When compared to age, sex, and admission year matched general population, the proportion of smokers in AVM patients was 48% (CI = 41%-55%) and 19% (CI = 16%-21%) in the general population. The difference increased in older age groups; in those aged 65 to 77 yr, the percentage of smokers reached 73% (CI = 46%-90%), while the corresponding percentage in the general population was 7% (CI = 5%-9%). CONCLUSION We observed considerably higher rates of smoking among AVM patients when compared to age, sex, and admission year matched general population. Our results suggest that in the development of AVMs, the role played by nicotine and other substances in tobacco smoke should be examined. Cigarette smoking could potentially be a common cerebrovascular risk factor.

Unmet device reprogramming needs at the end of life among patients with implantable cardioverter defibrillator: A systematic review and meta-analysis

Background: Use of implantable cardioverter defibrillators is increasingly common. As patients approach the end of life, it is appropriate to deactivate the shock function. Aim: To assess the prevalence of implantable cardioverter defibrillator reprogramming to deactivate the shock function at the end of life and the prevalence of advance directives among this population. Design: Following a previously established protocol available in PROSPERO, we performed a narrative synthesis of our findings and used the logit transformation method to perform our quantitative synthesis. Data sources: We searched seven bibliographic databases (Embase, Cochrane Central register of controlled Trials, Medline-Ovid, Web-of-Science, Scopus, PsychInfo, and CINAHL) and additional sources until April 2019. Results: Of the references we identified, 14 were included. We found a pooled prevalence of implantable cardioverter defibrillator reprogramming at the end of life of 28% (95% confidence interval, 22%–36%) with higher reprogramming rates after the recommendations for managing the device at the end of life were published. Among patients with advance directives, the pooled prevalence of advance directives that explicitly mentioned the device was 1% (95% confidence interval, 1%–3%). Conclusions: The prevalence of implantable cardioverter defibrillator reprogramming and advance directives that explicitly mentioned the device was very low. Study data suggested reprogramming decisions were made very late, after the patient experienced multiple shocks. Patient suffering could be ameliorated if physicians and other healthcare professionals adhere to clinical guidelines for the good management of the device at the end of life and include deactivating the shock function in the discussion that leads to the advance directive.