Bias for Treatment Effect by Measurement Error in Pretest in ANCOVA Analysis

This paper investigated consequences of measurement error in the pretest on the estimate of the treatment effect in a pretest–posttest design with the analysis of covariance (ANCOVA) model, focusing on both the direction and magnitude of its bias. Some prior studies have examined the magnitude of the bias due to measurement error and suggested ways to correct it. However, none of them clarified how the direction of bias is affected by measurement error. This study analytically derived a formula for the asymptotic bias for the treatment effect. The derived formula is a function of the reliability of the pretest, the standardized population group mean difference for the pretest, and the correlation between pretest and posttest true scores. It revealed a concerning consequence of ignoring measurement errors in pretest scores: treatment effects could be overestimated or underestimated, and positive treatment effects can be estimated as negative effects in certain conditions. A simulation study was also conducted to verify the derived bias formula.

THE IMPACT OF SEASONALITY ON TOURIST’S ARRIVAL IN BANGLADESH BY USING SANCOVA MODEL

In this study, we aim to highlight the impact of climate change as well as seasonality on tourist’s arrival in Bangladesh. The SANCOVA modeling framework modified by the ANCOVA model is used to examine the impact of climate change on tourists’ arrivals. The results show seasonality has a 91% effect on tourist’s arrival in Bangladesh. The maximum and minimum variation of climatic variables on tourists’ arrival in Bangladesh is rainfall and humidity, respectively. The winter and summer seasons have similar and more impact on tourist’s arrival in Bangladesh. Our findings indicate that the tourism industry of Bangladesh is more vulnerable to seasonal variation than the overall economy. The present study has significant implications for both policymakers and tourisms destination alike to plan for tourism in Bangladesh.

Impact assessment of political administrations on the performance of the construction sector: a time series analysis

Purpose The complex interaction of politics and the economy is a critical factor for the sustainable growth and development of the construction sector (CNS). This study aims to investigate the effects of type of political administration including democracy and military on the performance of CNS using the Nigerian Construction Sector (NCS) as a case study. Design/methodology/approach A 48 year (1970–2017) time series data (TSD) on the NCS and the gross domestic product (GDP) based on 2010 constant USD were extracted from the United Nations Statistical Department database. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models were used to analyze the TSD. The ANCOVA model includes the GDP as correlational variable or covariate. Findings The estimates of the ANOVA model indicate that democratic administration is significantly better than military administration in construction performance. However, the ANCOVA model indicates that the GDP is more important than political administration in the performance of the CNS. The study recommends for a new national construction policy, favourable fiscal and monetary policy, local content development policy and construction credit guaranty scheme for the rapid growth and development of the NCS. Originality/value Hitherto, little is known about the influence of political administration on the performance of the CNS. This study provides empirical evidence from a developing economy perspective. It presents the relationships and highlights recommendations for driving growth in the construction industry.

Proposed Anti-Inflammatory Diet Reduces Inflammation in Compliant, Weight-Stable Patients with Rheumatoid Arthritis in a Randomized Controlled Crossover Trial

ABSTRACT Background It is unclear to what extent adjuvant dietary intervention can influence inflammation in rheumatoid arthritis (RA). Objectives The objective was to assess the effects of dietary manipulation on inflammation in patients with RA. Methods In a crossover design, participants [n = 50, 78% females, median BMI (in kg/m2) 27, median age 63 y] were randomly assigned to begin with either a 10-wk portfolio diet of proposed anti-inflammatory foods (i.e., a high intake of fatty fish, whole grains, fruits, nuts, and berries) or a control diet resembling a Western diet with a 4-mo washout in between. This report evaluates the secondary outcome markers of inflammation among participants with stable medication. Analyses were performed using a linear mixed ANCOVA model. Results There were no significant effects on CRP or ESR in the group as a whole. In those with high compliance (n = 29), changes in ESR within the intervention diet period differed significantly compared with changes within the control diet period (mean: –5.490; 95% CI: –10.310, –0.669; P = 0.027). During the intervention diet period, there were lowered serum concentrations of C-X-C motif ligand 1 (CXCL1) (mean: –0.268; 95% CI: –0.452, –0.084;P = 0.006), CXCL5 (mean: –0.278; 95% CI: –0.530, –0.026 P = 0.031), CXCL6 (mean: –0.251; 95% CI: –0.433, –0.069; P = 0.009), and tumor necrosis factor ligand superfamily member 14 (TNFSF14) (mean: –0.139; 95% CI: –0.275, –0.002; P = 0.047) compared with changes within the control diet period. Conclusion A proposed anti-inflammatory diet likely reduced systemic inflammation, as indicated by a decreased ESR in those who completed the study with high compliance (n = 29). These findings warrant further studies to validate our results, and to evaluate the clinical relevance of changes in CXCL1, CXCL5, CXCL6, and TNFSF14 in patients with RA.

Engine Vibration Data Increases Prognosis Accuracy on Emission Loads: A Novel Statistical Regressions Algorithm Approach for Vibration Analysis in Time Domain

Statistical regression models have rarely been used for engine exhaust emission parameters. This paper presents a three-step statistical analysis algorithm, which shows increased prediction accuracy when using vibration and sound pressure data as a covariate variable in the exhaust emission prediction model. The first step evaluates the best time domain statistic and the point of collection of engine data. The univariate linear regression model revealed that non-negative time domain statistics are the best predictors. Also, only one statistic evaluated in this study was a statistically significant predictor for all 11 exhaust parameters. The ecological and energy parameters of the engine were analyzed by statistical analysis. The symmetry of the methods was applied in the analysis both in terms of fuel type and in terms of adjustable engine parameters. A three-step statistical analysis algorithm with symmetric statistical regression analysis was used. Fixed engine parameters were evaluated in the second algorithm step. ANOVA revealed that engine power was a strong predictor for fuel mass flow, CO, CO2, NOx, THC, COSick, O2, air mass flow, texhaust, whereas type of fuel was only a predictor of tair and tfuel. Injection timing did not allow predicting any exhaust parameters. In the third step, the best fixed engine parameter and the best time domain statistic was used as a model covariate in ANCOVA model. ANCOVA model showed increased prediction accuracy in all 11 exhausted emission parameters. Moreover, vibration covariate was found to increase model accuracy under higher engine power (12 kW and 20 kW) and using several types of fuels (HVO30, HVO50, SME30, and SME50). Vibration characteristics of diesel engines running on alternative fuels show reliable relationships with engine performance characteristics, including amounts and characteristics of exhaust emissions. Thus, the results received can be used to develop a reliable and inexpensive method to evaluate the impact of various alternative fuel blends on important parameters of diesel engines.

Meta Analisis Efektivitas Model Problem Based Learning dan Problem Solving Terhadap Kemampuan Berpikir Kritis Mata Pelajaran Matematika Siswa Sekolah Dasar

Tujuan dari penelitian ini yaitu untuk mengetahui komparasi model pembelajaran Problem Based Learning dan Problem Solving terhadap kemampuan berpikir kritis matematika siswa. Jenis penelitian yang digunakan dalam penelitian ini adalah penelitian Meta Analisis. Data analisis didapatkan melalui jurnal online di google Cendekia dan Scholar. Kemudian dilanjutkan dengan menyeleksi beberapa artikel yang didapatkan, dan setelah dilakukan penyeleksian ditetapkan sampel sebanyak 20 artikel yang signifikan untuk dianalisis. Ada 5 langkah untuk mengukur kemampuan berpikir kritis, yaitu sebagai berikut: (1) mengidentifikasi permasalahan, (2) menentukan permasalahan, (3) strategi, (4) mengumpulkan data, (5) menyimpulkan. Agar dapat diketahui perbedaan pengaruh antara model Problem Based Learning dan Problem Solving maka langkah selanjutnya yaitu melakukan uji Ancova. Hasil uji Effect Size dengan menggunakan model model Problem Based Learning dan Problem Solving terdapat hasil Sig. sebanyak 0,003 dengan nilai Partial Eta Squared sebesar 0,864. Hasil uji tesebut menunjukkan bahwa model pembelajaran Problem Based Learning dan Problem Solving tergolong sangat besar dalam memberikan pegaruh terhadap kemampuan berpikir kritis matematika. Jika dilihat dari hasil uji Ancova model pembelajaran Problem Based Learning dengan nilai rata-rata yaitu sebesar 64,9173 dan nilai rata-rata model pembelajaran Problem Solving sebesar 68.6220. Sehingga bisa disimpulkan bahwa model pembelajaran Problem Solving lebih efektif jika dibandingkan dengan model Problem Based Learning yang ditinjau dari kemampuan berpikir kritis matematika siswa Sekolah Dasar.

Step Length Is a Promising Progression Marker in Parkinson’s Disease

Current research on Parkinson’s disease (PD) is increasingly concerned with the identification of objective and specific markers to make reliable statements about the effect of therapy and disease progression. Parameters from inertial measurement units (IMUs) are objective and accurate, and thus an interesting option to be included in the regular assessment of these patients. In this study, 68 patients with PD (PwP) in Hoehn and Yahr (H&Y) stages 1–4 were assessed with two gait tasks—20 m straight walk and circular walk—using IMUs. In an ANCOVA model, we found a significant and large effect of the H&Y scores on step length in both tasks, and only a minor effect on step time. This study provides evidence that from the two potentially most important gait parameters currently accessible with wearable technology under supervised assessment strategies, step length changes substantially over the course of PD, while step time shows surprisingly little change in the progression of PD. These results show the importance of carefully evaluating quantitative gait parameters to make assumptions about disease progression, and the potential of the granular evaluation of symptoms such as gait deficits when monitoring chronic progressive diseases such as PD.

Adaptation of Winter Wheat Cultivars to Different Environments: A Case Study in Poland

A proper understanding of cultivar adaptation to different environments is of great relevance in agronomy and plant breeding. As wheat is the most important crop in Poland, with a total of about 22% of the total sown area, the study of its performance in environments with different productivity levels for consequent cultivar recommendation is of major importance. In this paper, we assess the relative performance of winter wheat cultivars in environments with different productivity and propose a method for cultivar recommendation, by considering the information of environmental conditions and drought stress. This is performed in the following steps: (1) calculation of expected wheat productivity, depending on environmental factors, (2) calculation of relative productivity of cultivars in the environments, and (3) recommendation of cultivars of a specific type and range of adaptation. Soil and weather conditions were confirmed as the most important factors affecting winter wheat yield. The weather factors should be considered rather in shorter (e.g., 10 day) than longer (e.g., 60 day) time periods and in relation to growth stages. The ANCOVA model with genotype and management intensity as fixed factors, and soil and weather parameters as covariates was proposed to assess the expected wheat productivity in particular environments and the expected performance of each genotype (cultivar). The recommendation of cultivars for locations of specified productivity was proposed based on the difference between the expected cultivar yield and the mean wheat productivity, and compared with the Polish official cultivar recommendation list.

Statin Use in the Early Phase of ST-Segment Elevation Myocardial Infarction Is Associated With Decreased QTc Dispersion

Background: Although there is strong evidence supporting the use of statin therapy after myocardial infarction (MI), some mechanistic gaps exist regarding the benefits of this therapy at the very onset of MI. Among the potential beneficial mechanisms, statins may improve myocardial electrical stability and reduce life-threatening ventricular arrhythmia, as reported in stable clinical conditions. This study was designed to evaluate whether this mechanism could also occur during the acute phase of MI. Methods: Consecutive patients with ST-segment elevation MI were treated without statin (n = 57) or with a simvastatin dose of 20 to 80 mg (n = 87) within the first 24 hours after MI symptom onset. Patients underwent digital electrocardiography within the first 24 hours and at the third and fifth days after MI. The QTC dispersion (QTcD) was measured both with and without the U waves. Results: Although QTcD values were equivalent between the groups at the first day (80.6 ± 36.0 vs 80.0 ± 32.1; P = 0.36), they were shorter among individuals using simvastatin than in those receiving no statins on the third (90.4 ± 38.6 vs 86.5 ± 36.9; P = .036) and fifth days (73.1 ± 31 vs 69.2 ± 32.6; P = .049). We obtained similar results when analyzing the QTcD duration including the U wave. All values were adjusted by an ANCOVA model after propensity-score matching. Conclusions: Statins administered within 24 hours of ST-segment elevation MI reduced QTc dispersion, which may potentially attenuate the substrate for life-threatening ventricular arrhythmias.