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This section briefly reviews the main papers on R&D cooperation modeled as games, but it is not meant to be an exhaustive survey. It also compares the concept of trust as developed in this paper with the existing notions. 2.1. R&D cooperation between symmetric non-opportunist firms The first models on R&D cooperation considered the simple context of identical non-opportunist firms in order to examine the conditions under which R&D cooperation promotes private and social welfare. They showed that R&D cooperation might be motivated by cost and information sharing in the presence of spillovers. When spillovers are high, R&D cartels involving cost and information sharing ; call for high R&D expenditures. This leads to economies of scale in terms of cost reduction, since each firm also benefits from the increased R&D efforts of other firms, given the high level of spillovers. The seminal article by d'Aspremont and Jacquemin 1988 ; is one of the most influential papers in this group. Their basic R&D cartel model in a duopoly framework has now been extended along various lines, with the consideration of oligopolies, heterogeneous products, product innovation, vertical cooperation, participation in multiple R&D cartels, multiple participants, etc. Kamien et al. 1992; Motta, 1992; Suzumura, 1992; Ziss, 1994; DeBondt, 1997; Hinloopen, 2000 ; . More recent models have also confirmed the preceding results taking into account the endogenization of spillovers Katsoulacos and Ulph, 1998; Poyago-Theotoky, 1999 ; . Another set of models studies the impact of spillovers in a stochastic or dynamic context. When spillovers or imitations are likely, R&D cartels are again initiated due to the internalization of market spillovers of knowledge and non-duplication of research efforts Grossman and Shapiro, 1987; Beath et al. 1988; Katsoulacos, 1988; Choi, 1993 ; . A third set focuses on the incentives for R&D cooperation in the absence of spillovers. Marjit 1991 ; shows that when the, because extracting hydrocodone.
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1. Iglehart JK. The American health care system--expenditures. N Engl J Med. 1999; 340: 70-76. Kong D. US efforts urged to check high pharmaceutical costs. Boston Globe. July 28, 1999: A8. 3. Mehl B, Santell JP. Projecting future drug expenditures--1999. J Health Syst Pharm. 1999; 56: 31-39. Glickman L, Bruce EA, Caro FG, Avorn J. Physicians' knowledge of drug costs for the elderly. J Geriatr Soc. 1994; 42: 992-996. Hoffman J, Barefield FA, Ramamurthy S. A survey of physician knowledge of drug costs. J Pain Symptom Manage. 1995; 10: 432-435. Oppenheim GL, Erickson SH, Ashworth C. The family physician's knowledge of the cost of prescribed drugs. J Fam Pract. 1981; 12: 1027-1030. Weber ML, Auger C, Cleroux R. Knowledge of medical students, pediatric residents, and pediatricians about the cost of some medications. Pediatr Pharmacol. 1986; 5: 281-285. Rowe J, MacVicar S. Doctors' knowledge of the cost of common medications. J Clin Hosp Pharm. 1986; 11: 365-368. Miller LG, Blum A. Physician awareness of prescription drug costs: a missing element of drug advertising and promotion. J Fam Pract. 1993; 36: 33-36. Ryan M, Yule B, Bond C, Taylor RJ. Scottish general practitioners' attitudes and knowledge in respect of prescribing costs. BMJ. 1990; 300: 1316-1318. Fowkes FG. Doctors' knowledge of the costs of medical care. Med Educ. 1985; 19: 113-117. Beringer GB, Biel M, Ziegler DK. Neurologists' knowledge of medication costs. Neurology. 1984; 34: 121-122. Walzak D, Swindells S, Bhardwaj A. Primary care physicians and the cost of drugs: a study of prescribing practices based on recognition and information sources. J Clin Pharmacol. 1994; 34: 1159-1163. Stuart B, Grana J. Ability to pay and the decision to medicate. Med Care. 1998; 36: 202-211. Soumerai SB, Ross-Degnan D. Inadequate prescription-drug coverage for Medicare enrollees--a call to action. N Engl J Med. 1999; 340: 722-728. Rother J. A drug benefit: the necessary prescription for Medicare. Health Aff Millwood ; . July-August 1999; 18: 20-24. Etheredge L. Purchasing Medicare prescription drug benefits: a new proposal. Health Aff Millwood ; . July-August 1999; 18: 7-19. Gross D, Branagan N. Medicare Beneficiaries and Prescription Drug Coverage: Gaps and Barriers. Washington, DC: AARP Public Policy Institute; 1999. 19. Davis M, Poisal J, Chulis G, Zarabozo C, Cooper B. Prescription drug coverage, utilization, and spending among Medicare beneficiaries. Health Aff Millwood ; . January-February 1999; 18: 231-243. Cardinale V, ed. 1998 Drug Topics Red Book. Montvale, NJ: Medical Economics Co; 1998. 21. Ziegler MG, Lew P, Singer BC. The accuracy of drug information from pharmaceutical sales representatives. JAMA. 1995; 273: 1296-1298. Lexchin J. What information do physicians receive from pharmaceutical representatives [review]? Can Fam Physician. 1997; 43: 941-945. Caudill TS, Johnson MS, Rich EC, McKinney WP. Physicians, pharmaceutical sales representatives, and the cost of prescribing. Arch Fam Med. 1996; 5: 201-206. McKinney WP, Schiedermayer DL, Lurie N, Simpson, DE, Goodman JL, Rich, EC. Attitudes of internal medicine faculty and residents toward professional interaction with pharmaceutical sales representatives. JAMA. 1990; 264: 1693-1697.
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Multiple risk factor clustering syndrome, which accelerate the formation of atherosclerosis strikingly, has become a topic of interest. Several confounding variables, such as insulin resistance, physical inactivity, or obese, may affect this syndrome. But in Japanese population the detail has not been discussed yet. The purpose of this study was to assessthe relationship between this syndrome and the factors which might affect the risk factor clustering. Subjects were 141 so-called healthy males 52.5 -f 8.7 y.0. ; . Physical examination, lipid profile analysis and 75 g-OGTT were performed after an overnight fast. Insulin areas under the curve of GGlT were utilized as a parameter of insulin resistance. Physical fitness was measured by symptom limited treadmill exercise test and exercise time was utilized as a parameter of physical fitness. Risk factors were included hypertension systolic blood pressure 140 mmHg and or diastolic blood pressure 190 mmHg ; , total cholesterol ~220 mg dl, triglycerides 150 mg dl, HDL-C 40 mg dl, and abnormal pattern in OGTT. The number of risk factor was selected as a dependent variable and age, body mass index, and exercise time were selected as independent variables. Multiple regression analysis with stepwise forward selection method revealed that all the independent variables are significant R2 0.24 ; . But when insulin area added into the independent variables, insulin area was the only factor which was selected as significant and the others were neglected. These results suggested that obesity, aging, and physical fitness were actually important factors which affected the risk factors but the condition of insulin resistance was the most powerful determinant for the clustering of risk factors also in Japanesepopulation and hyzaar.
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