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sources of dependencies among variables and show in a series of data simulations how the estimate of the target causal parameter diverges from the truth when the necessary assumptions for a given identifiability result fail to hold. Finally, we apply a semi-parametric efficient estimator (targeted maximum likelihood) for each of the three estimands to the observed data from Madagascar to estimate the average treatment effect (ATE) of the program. We demonstrate that the choice of causal model and corresponding identification strategy have important implications for

1 Introduction In recent years, researchers have paid increased attention to the properties of treatment effect estimators for randomized experiments under the design-based model (see, e.g. Freedman 2008a,b). Under the design-based model (Neyman 1923, 1934; Sarndal 1978), potential outcomes are fixed and the only source of stochasticity lies in the random administration of a treatment to a finite population. Importantly, Freedman (2008a) demonstrated that, under a such a model, regression adjustment is generally biased (though consistent) and may reduce

DOI 10.1515/jpem-2012-0224       J Pediatr Endocr Met 2012; 25(11-12): 1119–1122 Shuwen Hu , Zidi Xu , Jie Yan , Min Liu , Bing Sun , Wenjing Li and Yanmei Sang * The treatment effect of diazoxide on 44 patients with congenital hyperinsulinism Abstract Objective: To study the treatment effect and safety of diazox- ide on patients with congenital hyperinsulinism (CHI). Research design and methods: A total of 44 patients who have been hospitalized to our hospital and used diazoxide as a trial after diagnosis of CHI were chosen as research

1 Introduction The goal in a randomized experiment is often to estimate the total or global average treatment effect (GATE) of a binary treatment variable on a response variable. The GATE is the difference in average outcomes when all units are exposed to treatment versus when all units are exposed to control. Under the standard assumption that units do not interfere with each other [ 1 ], which forms a key part of the stable unit treatment value assumption (SUTVA) [ 2 ], [ 3 ], the global average treatment effect reduces to the standard average treatment

arms) is based on statistical efficiency and not population proportions, there is no reason to believe that the study subjects are collectively representative of the population of all HBV patients. This lack of representativeness can result in a serious bias in commonly used estimators of the average treatment effect, as demonstrated by Hu and Qin [ 17 ]. Causal inference under choice-based sampling has been considered by Heckman and Todd [ 18 ], Kennedy, Sjolander and Small [ 19 ], and Hu and Qin [ 17 ]. Heckman and Todd [ 18 ] note that the PS in a choice

and Raudenbush 2006 ), or infectious disease ( Hudgens and Halloran 2008 ; Perez-Heydrich et al. 2014 ), among others. In the presence of interference, one cannot assume that the outcomes of the control units represent the outcomes of the treated units had they not received treatment. In this setting, the observed treatment effect is a function not only of the direct effect of treatment but also indirect effects from spillover or contamination ( Halloran and Struchiner 1991 ). This paper deals specifically with the context of cluster-randomized trials of an

ITT effects are important, there are other relevant causal quantities when there is noncompliance. In JOBS II only 61 % of those assigned to the intervention actually attended the training seminars, while those assigned to control could not access the treatment. It is therefore relevant to focus on whether the intervention was efficacious among those who actually attended the job search seminar, which requires conditioning on post-treatment information [ 8 , 9 ]. In addition to accounting for noncompliance, we also evaluate post-treatment effect modification, an

Cryogenic Treatment Effect on Stress Distribution in Interference Fitted Assemblies 'Irappa Sogalad, 2N.G. Subramanya Udupa 1 Department of Mechanical Engineering, University BDT College of Engineering, Davangere-577 004, India 2 Department of Mechanical Engineering, Nagarjuna College of Engineering and Technology, Bangalore-562110, India ABSTRACT This paper describes the details of analysis carried out to study the effect of cryogenic treatment on stress distribution at the mating surface of interference fitted assemblies. This study includes analysis

Glassy Carbon/KCl-Solution Interface Impedance. Mechanical Surface Treatment Effect S. V. Mentus Institute of Physical Chemistry, Faculty of Science, Belgrade, Yugoslavia Z. Naturforsch. 38 a, 252 - 255 (1983); received October 7, 1982 Dedicated to Professor A If red Klemm on the occasion of his 70th birthday The dependence of complex impedance behaviour on mechanical surface treatment of glassy carbon electrodes in standard water KCl solutions at room temperature was investigated. A considerable frequency dependence of both the real and the imaginary

References [1] AGRESTI, A.-CAFFO, B.: Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures, Amer. Statist. 54 (2000), 280-288. [2] DOKOUPILOVÁ, P.: A confidence interval for the probability difference of overall treatment effect-simulation study, Acta Univ. M. Belii Ser. Math. 18 (2011), 7-16. [3] PAULE, R. C.-MANDEL, J.: Consensus values and weighting factors, J. Res. Natl. Bur. Stand. 87 (1982), 377-385. [4] WIMMER, G.-WITKOVSKÝ, V.: Konfidenčné intervaly pre efekt ošetrenia v