scholarly journals Performance of statistical methods to address treatment non-adherence in pragmatic clinical trials with point-treatment settings: a simulation study

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Md Belal Hossain ◽  
Lucy Mosquera ◽  
Mohammad Karim
Keyword(s):  
Mean Squared Error ◽  
Least Square ◽  
Childhood Mortality ◽  
Two Stage ◽  
Unmeasured Confounding ◽  
Exclusion Restriction ◽  
Pragmatic Clinical Trials ◽  
Confidence Interval Coverage ◽  
High Bias ◽  
Inverse Probability Weighted

Introduction: The instrumental variable (IV)-based methods (e.g., two-stage least square [2SLS], two-stage residual inclusion [2SRI], and nonparametric causal bound [NPCB]) can be used to address non-adherence in pragmatic trials. These methods require assumptions, e.g., exclusion restriction, although they are known to handle unmeasured confounding. The inverse probability-weighted per-protocol [IPW-PP] method is useful in the same setting but requires different assumptions (no unmeasured confounding). Although all these methods aim to address the same problem, comprehensive simulations to compare their performance are absent in the literature. We performed extensive simulations when (1) confounding is present, (2) confounder is unmeasured but exclusion restriction is met, (3) exclusion restriction is violated, and (4) non-adherence is one-sided and differential. Method: We compared the performance in terms of bias, standard error (SE), mean squared error (MSE), and 95% confidence interval coverage probability. Results: For setting-1, IPW-PP outperforms IV-methods in terms of bias, SE, MSE, and coverage for <80% non-adherence but produces high bias beyond that point. IPW-PP also has high biases, but 2SLS and 2SRI work well for setting-2. For setting-3, 2SLS and 2SRI perform the worst in all scenarios; IPW-PP produces unbiased estimates when necessary confounders are measured and adjusted. For setting-4, IPW-PP has less bias, but 2SLS and 2SRI have higher SE and MSE. NPCB has wider bounds in all scenarios. We also analyze a two-arm trial to estimate the effect of vitamin A supplementation on childhood mortality after addressing non-adherence. Conclusion: We need to be cautious using the IPW-PP when non-adherence is very high or strong unmeasured confounding and should avoid using the IV methods when the exclusion restriction assumption is violated or high differential non-adherence. Since assumptions are different and often untestable for IPW-PP and IV methods, we suggest analyzing data using both methods for a robust conclusion.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

scholarly journals ANALISIS PERMINTAAN DAN PENAWARAN UANG DI INDONESIA

2019 ◽  
Vol 5 (1) ◽  
pp. 1-8
Author(s):  
Arwin Arwin ◽  
Said Muhammad ◽  
Raja Masbar
Keyword(s):  
Interest Rate ◽  
Money Supply ◽  
World Bank ◽  
Money Demand ◽  
National Income ◽  
Secondary Data ◽  
Real Income ◽  
Least Square ◽  
Two Stage ◽  
Data Processing Method

This study aims to determine the determinants of the money demand and money supply function in Indonesia. To formulate the equation between money demand (Md) and money supply (Ms) using LM function by looking at the effect of real income and interest rate. The data in this study constitutes Indonesia's economic data from 1986 to 2015 drawn from secondary data sources such as Bank Indonesia (BI), Central Bureau of Statistics (BPS), International Financial Statistics (IFS), International Monetary Funds (IMF) and World Bank . The Data Processing method used is to use the equations and completed with Two Stage Least Square. The results showed that the balance occurred at the national income level of 277559.05 billion Rupiah with an interest rate of 7.05%. Keywords: Demand and Supply of Money, Gross Domestic Product, Interest Rate, Inflation, and Exchange Rate. Abstrak Penelitian ini bertujuan untuk megetahui determinan dari fungsi permintaan uang dan penawaran uang di Indonesia. Untuk merumuskan persamaan antara permintaan uang (Md) dengan penawaran uang (Ms) menggunakan fungsi LM dengan melihat pengaruh pendapatan riil dan tingkat suku bunga. Data dalam penelitian ini merupakan data perekonomian Indonesia dari tahun 1986 – 2015 yang diambilkan dari sumber data sekunder baik seperti Bank Indonesia(BI), Badan Pusat Statistik (BPS), Internasional Financial Statistics (IFS), International Monetary Funds ( IMF) dan World Bank. Metode Pengolahan datayang digunakan adalah menggunakan persamaan simultan dan diselesaikan dengan Two Stage Least Square. Hasil penelitian menunjukkan bahwa keseimbangan terjadi pada tingkat pendapatan nasional sebesar 277559.05 milyar Rupiah dengan tingkat bunga sebesar 7,05%. 

scholarly journals Modelling of Horizontal Flexible Plate Structure Using Artificial Bee Colony Algorithm

2018 ◽  
Vol 7 (4.36) ◽  
pp. 415
Author(s):  
Muhamad Sukri Hadi ◽  
Sukri Hadi Zaurah Mat Darus
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Mean Squared Error ◽  
Least Square ◽  
Flexible Plate ◽  
Plate Structure ◽  
Bee Colony ◽  
Squared Error ◽  
Plate System

This paper presents the performance of system identification for modeling the horizontal flexible plate system using artificial bee colony and recursive least square algorithms. Initially, the experimental rig of flexible plate was designed and fabricated with all edges clamped boundary condition at the horizontal position. Then, the instrumentation and data acquisition systems were integrated into the rig for acquiring the input-output vibration experimentally. The collected data in the experiment will be used later for modeling the dynamic system of horizontal flexible plate system using system identification. The effectiveness of the developed model will be validated using mean squared error, one step ahead prediction, correlation tests and pole zero diagram stability. The estimated of the developed models were found are acceptable and possible to be used as a platform of controller development for vibration suppression of the undesirable vibration in the flexible plate structure. It was found that the artificial bee colony algorithm has performed better in this study by achieving the lowest mean squared error, good correlation test and high stability in the pole zero diagram.  

scholarly journals KETERKAITAN MANAGERIAL OWNERSHIP DAN LEVERAGE TERHADAP NILAI PERUSAHAAN: DALAM KONTEK HUBUNGAN KEAGENAN PERUSAHAAN DI INDONESIA

2021 ◽  
Vol 5 (1) ◽  
pp. 160
Author(s):  
Margarita Ekadjaja ◽  
Rorlen Rorlen ◽  
Fanny Andriani Setiawan ◽  
Kartika Nuringsih
Keyword(s):  
Decision Making ◽  
Firm Value ◽  
Agency Costs ◽  
Least Square ◽  
Managerial Ownership ◽  
Corporate Value ◽  
Manufacturing Companies ◽  
Positive Interaction ◽  
Two Stage ◽  
The Relationship

Manajemen dan nilai perusahaan memiliki keterkaitan yang tidak dapat dipisahkan.  Dimana manajemen perusahaan merupakan penggerak roda perusahaan dan berorientasi pada nilai perusahaan. Peran seorang manajer adalah memaksimalkan kekayaan bagi pemegang saham.  Namun, manajer yang tidak memiliki kepemilikan saham yang signifikan di perusahaan dapat memilih untuk memaksimalkan keuntungan bersih mereka sendiri dengan mengorbankan pemilik perusahaan. Akibatnya, pemilik terpaksa mengeluarkan biaya agensi untuk memastikan bahwa manajemen perusahaan bertindak dengan cara yang tepat. Cara untuk mengurangi biaya agensi adalah memaksa perusahaan untuk meningkatkan hutang. Tujuan penelitian adalah  menguji hubungan simultan pertukaran antara ownership, leverage, dan nilai perusahaan sehubungan dengan keagenan pada perusahaan manufaktur di Indonesia dari tahun 2012-2018. Penelitian ini menambah pemahaman mengenai keterkaitan antara ownership dengan leverage, dan nilai perusahaan. Analisis data untuk argumen tentang keterkaitan simultan antara  ownership, leverage, dan nilai perusahaan melalui data panel regresi berganda 2 SLS (Two Stage Least Square). Bidang penelitian ini diperluas dengan mempertimbangkan model empiris di mana ownership dan leverage masing-masing diperlakukan sebagai variabel endogen atau ditentukan bersama.  Dalam metode 2 SLS ada 2 kali variabel yang diobservasi secara simultan untuk menghindari bayes sehingga variabel tersebut tidak bias, di mana variabel managerial ownership dan Leverage merupakan determinan non linier nilai perusahaan sebagai bagian integral dari pengambilan keputusan perusahaan dalam kerangka keagenan.  Persamaan Regresi hasil uji 2SLS memunjukkan keterkaitan nilai perusahaan dengan managerial ownership dan leverage. Hasil menunjukkan interaksi positif  tidak signifikan antara managerial ownerhip dengan nilai perusahaan, interaksi positif signifikan antara nilai perusahaan dengan leverage, dan interaksi yang negatif signifkan antara managerial ownership dengan leverage. Management and corporate value have an inseparable relationship. Where the company management is the driving force of the company and oriented to corporate values. The role of a manager is to maximize wealth of shareholders. However, managers who do not have a significant share in the company may choose to maximize their own net profits at the expense of the company owners. As a result, the owners are forced to incur agency costs to ensure that company management acts in an appropriate manner. The way to reduce agency costs is to force the company to increase debt. The research objective is to examine the exchange simultaneous relationship between ownership, leverage, and corporate value with respect to agency in manufacturing companies in Indonesia from 2012-2018. This study adds to the understanding of the relationship between ownership and leverage, and corporate value. Data analysis for arguments about the simultaneous relationship between ownership, leverage, and firm value through 2 SLS (Two Stage Least Square) multiple regression panel data. This field of research is extended by considering empirical models in which ownership and leverage are treated as endogenous or co-determined variables, respectively. Ownership and Leverage as an integral part of corporate decision making within an agency framework, which in turn will affect the value of the company. In the SLS 2 method, there are 2 variables that are observed simultaneously to avoid bayes so that the variable is not biased, in which the managerial ownership and leverage variables are nonlinear determinant corporate value as an integral part of corporate decision making within the agency framework, which in turn will affect firm value. The 2SLS regression equation results show the relationship between firm value and managerial ownership and leverage. The results prove that there is a positive interaction between managerial ownership between firm value, a significant positive interaction between firm value and leverage, and a significant negative interaction between managerial ownership and leverage.

ANALISIS PENGARUH TRANSACTION COST TERHADAP HOLDING PERIOD SAHAM BIASA.

2008 ◽  
Vol 4 (2) ◽  
pp. 116
Author(s):  
Eko Budi Santoso
Keyword(s):  
Statistical Method ◽  
Transaction Cost ◽  
Time Horizon ◽  
Market Value ◽  
Least Square ◽  
Common Stock ◽  
Two Stage ◽  
Holding Period ◽  
Bid Ask Spread

The moin pwpose of this sndy is to test the effect of transaction cost totntdins period of common stock This srudy iwestigates whether investorswith longer (shorter) hwestment time horizon lold common stockswith higher (owe) bid-qsk spred as a prory of tronsaction cost. Besides,thk study also added two independent voiables such as marketvalue and variance ofrefirn-The statistical method ued in this study is two-stage least square regressionsbecause the itnestorb tnlding period md the bid-ask spreadfor each stoch are simultoteously determined. The result shows that bidask spred related positivefu ord significott to holding period. The bidask spread, morket yalue, and varianee of return have a significant Kqruords: Trqtsaction Cost, Bid-Ask Spread, Holding Period, Market Value, Variance of Return

Identification of continuous-time hybrid “Box–Jenkins” systems with multiple unknown time delays using two-stage parameter estimation algorithm

2019 ◽  
Vol 36 (6) ◽  
pp. 2111-2130
Author(s):  
Yamna Ghoul
Keyword(s):  
Continuous Time ◽  
Time Delays ◽  
Least Square ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Two Stage ◽  
Content Type ◽  
Single Output ◽  
Multiple Input ◽  
Unknown Time Delays

Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm. Findings The effectiveness of the proposed scheme is shown with application to a simulation example. Originality/value A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.

scholarly journals Polynomial Load Model Development for Analysing Residential Electric Energy Use Behaviour

2018 ◽  
Vol 218 ◽  
pp. 01007 ◽  
Author(s):  
Erwin Nashrullah ◽  
Abdul Halim
Keyword(s):  
Energy Use ◽  
Mean Squared Error ◽  
Median Filter ◽  
Energy System ◽  
Least Square ◽  
Model Parameters ◽  
Data Sampling ◽  
Data Set ◽  
System Designer ◽  
Load Model

Analysing and simulating the dynamic behaviour of home power system as a part of community-based energy system needs load model of either aggregate or dis-aggregate power use. Moreover, in the context of home energy efficiency, development of specific and accurate residential load model can help system designer to develop a tool for reducing energy consumption effectively. In this paper, a new method for developing two types of residential polynomial load model is presented. In the research, computation technique of model parameters is provided based on median filter and least square estimation and implemented by MATLAB. We use AMPDs data set, which have 1-minute data sampling, to show the effectiveness of proposed method. After simulation is carried out, the performance evaluation of model is provided through exploring root mean-squared error between original data and model output. From simulation results, it could be concluded that proposed model is enough for helping system designer to analyse home power energy use.

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