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Journal articles on the topic 'Heterogeneity effects'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Matthews, Robert A. J. "Heterogeneity in Treatment Effects." Annals of Internal Medicine 166, no. 12 (2017): 914. http://dx.doi.org/10.7326/l17-0155.

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2

Davidoff, Frank. "Heterogeneity in Treatment Effects." Annals of Internal Medicine 166, no. 12 (2017): 914. http://dx.doi.org/10.7326/l17-0156.

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3

Lanza, Andrea, Antonella Pellegrino, and Guiseppina Simone. "Heterogeneous effects of heterogeneity." International Journal of Organizational Analysis 16, no. 1/2 (2008): 18–41. http://dx.doi.org/10.1108/19348830810915479.

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4

Trampisch, Hans J., Henrik Rudolf, and Stefan Lange. "Heterogeneity of therapeutic effects." Atherosclerosis 255 (December 2016): 124–25. http://dx.doi.org/10.1016/j.atherosclerosis.2016.10.034.

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5

Ni, Xu Dong, Ji Hong Yu, and Xian An Li. "Effects of Speciality Heterogeneity, Education Heterogeneity and Work Experience Heterogeneity on Team Performance." Applied Mechanics and Materials 411-414 (September 2013): 2451–57. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2451.

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This paper started with theories of team heterogeneity and team performance, according to questionnaire survey method, reviewed three dimensions of knowledge heterogeneity (speciality heterogeneity, education heterogeneity and work experience heterogeneity) and their effects on team performance. The result showed that speciality heterogeneity was positively related to task performance and contextual performance, while work experience heterogeneity was positively related to contextual performance significantly, but had no significant relationship with task performance. In addition, education he

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6

Turner, Dana P., and Timothy T. Houle. "Reporting heterogeneity of treatment effects." Headache: The Journal of Head and Face Pain 61, no. 3 (2021): 407–8. http://dx.doi.org/10.1111/head.14083.

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7

Vijan, Sandeep. "Evaluating heterogeneity of treatment effects." Biostatistics & Epidemiology 4, no. 1 (2020): 98–104. http://dx.doi.org/10.1080/24709360.2020.1724003.

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8

Aktas *, Nihat, Eric de Bodt, and Michel Levasseur. "Heterogeneity effects from market interventions." European Journal of Finance 10, no. 5 (2004): 412–36. http://dx.doi.org/10.1080/1351847032000166922.

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9

Watson, Derrick G., and Elizabeth A. Maylor. "Effects of color heterogeneity on subitization." Perception & Psychophysics 68, no. 2 (2006): 319–26. http://dx.doi.org/10.3758/bf03193679.

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10

Butvin, P., B. Butvinová, E. Illeková, and G. Vlasák. "Macroscopic heterogeneity effects in FeNiNbCuBSi ribbons." Journal of Magnetism and Magnetic Materials 237, no. 3 (2001): 237–40. http://dx.doi.org/10.1016/s0304-8853(01)00706-5.

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11

Stroup, T. Scott. "Heterogeneity of Treatment Effects in Schizophrenia." American Journal of Medicine 120, no. 4 (2007): S26—S31. http://dx.doi.org/10.1016/j.amjmed.2007.02.005.

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12

Dressen, Mark H. C. L., Bastiaan H. P. van de Kruijs, Jan Meuldijk, Jef A. J. M. Vekemans, and Lumbertus A. Hulshof. "Vanishing Microwave Effects: Influence of Heterogeneity." Organic Process Research & Development 11, no. 5 (2007): 865–69. http://dx.doi.org/10.1021/op700080t.

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13

Pearl, Judea. "Detecting Latent Heterogeneity." Sociological Methods & Research 46, no. 3 (2015): 370–89. http://dx.doi.org/10.1177/0049124115600597.

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We address the task of determining, from statistical averages alone, whether a population under study consists of several subpopulations, unknown to the investigator, each responding to a given treatment markedly differently. We show that such determination is feasible in three cases: (1) randomized trials with binary treatments, (2) models where treatment effects can be identified by adjustment for covariates, and (3) models in which treatment effects can be identified by mediating instruments. In each of these cases, we provide an explicit condition which, if confirmed empirically, proves th

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14

Tutz, Gerhard, and Margret-Ruth Oelker. "Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures." International Statistical Review 85, no. 2 (2016): 204–27. http://dx.doi.org/10.1111/insr.12161.

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15

Vihinen, Mauno. "Individual Genetic Heterogeneity." Genes 13, no. 9 (2022): 1626. http://dx.doi.org/10.3390/genes13091626.

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Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbein

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16

Jensen, Christian. "On the macroeconomic effects of heterogeneous productivity shocks." B.E. Journal of Macroeconomics 16, no. 1 (2016): 1–23. http://dx.doi.org/10.1515/bejm-2013-0160.

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AbstractThe conventional wisdom that producer heterogeneity washes out, and is therefore irrelevant for the aggregate economy, does not apply when producers compete monopolistically. Despite this, the effects of such heterogeneity can be reproduced with an appropriately redefined representative-agent framework where the equilibrium values of aggregates are expressed in terms of the moment generating function of the distribution of heterogeneity, or its asymptotic distribution. Increased heterogeneity raises aggregate productivity and production, more so the fiercer competition is. We propose a

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17

Choi, Jae-Hwan, Sung-Hye Kim, and Seung-Hyeon Moon. "Heterogeneity of Ion-Exchange Membranes: The Effects of Membrane Heterogeneity on Transport Properties." Journal of Colloid and Interface Science 241, no. 1 (2001): 120–26. http://dx.doi.org/10.1006/jcis.2001.7710.

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18

Collins, Beverly, and Gary Wein. "Soil Resource Heterogeneity Effects on Early Succession." Oikos 82, no. 2 (1998): 238. http://dx.doi.org/10.2307/3546963.

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19

YUAN TIAN. "HETEROGENEITY EFFECTS OF DEVELOPMENT FUNDING ON MICROENTERPRISES." Journal of Economic Development 40, no. 3 (2015): 1–26. http://dx.doi.org/10.35866/caujed.2015.40.3.001.

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20

Nugent, Bridget M., Rajanikanth Madabushi, Barbara Buch, et al. "Heterogeneity in treatment effects across diverse populations." Pharmaceutical Statistics 20, no. 5 (2021): 929–38. http://dx.doi.org/10.1002/pst.2161.

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21

Rhee, In-Kyu, and Woo Kim. "Effects of Numerical Modeling on Concrete Heterogeneity." Journal of the Korea Concrete Institute 18, no. 2 (2006): 189–98. http://dx.doi.org/10.4334/jkci.2006.18.2.189.

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22

McCoy, John J. "Effects of random heterogeneity on wave fields." Journal of the Acoustical Society of America 84, S1 (1988): S148. http://dx.doi.org/10.1121/1.2025852.

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23

Dodson, Stanley I. "Effects of environmental heterogeneity in aquatic ecology." SIL Proceedings, 1922-2010 27, no. 5 (2000): 3260–63. http://dx.doi.org/10.1080/03680770.1998.11898284.

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24

Aksoy, Ozan. "Effects of Heterogeneity and Homophily on Cooperation." Social Psychology Quarterly 78, no. 4 (2015): 324–44. http://dx.doi.org/10.1177/0190272515612403.

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25

Obaidurrahman, K., and S. K. Gupta. "Reactor core heterogeneity effects on radionuclide inventory." Annals of Nuclear Energy 53 (March 2013): 244–53. http://dx.doi.org/10.1016/j.anucene.2012.09.016.

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26

Kaptein, Maurits, and Dean Eckles. "Heterogeneity in the Effects of Online Persuasion." Journal of Interactive Marketing 26, no. 3 (2012): 176–88. http://dx.doi.org/10.1016/j.intmar.2012.02.002.

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27

Bos, J. W. B., M. Koetter, J. W. Kolari, and C. J. M. Kool. "Effects of heterogeneity on bank efficiency scores." European Journal of Operational Research 195, no. 1 (2009): 251–61. http://dx.doi.org/10.1016/j.ejor.2008.01.019.

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28

Landini, Simone, and Mauro Gallegati. "Heterogeneity, interaction and emergence: effects of composition." International Journal of Computational Economics and Econometrics 4, no. 3/4 (2014): 339. http://dx.doi.org/10.1504/ijcee.2014.064787.

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29

Altman, D. G., and J. N. S. Matthews. "Statistics Notes: Interaction 1: heterogeneity of effects." BMJ 313, no. 7055 (1996): 486. http://dx.doi.org/10.1136/bmj.313.7055.486.

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30

Erhardt, Katharina. "On home market effects and firm heterogeneity." European Economic Review 98 (September 2017): 316–40. http://dx.doi.org/10.1016/j.euroecorev.2017.07.007.

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31

Borukhovich, A. S., V. G. Bamburov, and A. A. Sidorov. "Magnetic heterogeneity effects in EuO-based phases." Journal of Magnetism and Magnetic Materials 73, no. 1 (1988): 106–14. http://dx.doi.org/10.1016/0304-8853(88)90176-x.

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32

Morrill, S. M., R. G. Lane, M. Langer, and I. I. Rosen. "Tissue heterogeneity effects in treatment plan optimization." International Journal of Radiation Oncology*Biology*Physics 24 (January 1992): 174. http://dx.doi.org/10.1016/0360-3016(92)90219-8.

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33

De Vries, G. "Diffusively Coupled Bursters: Effects of Cell Heterogeneity." Bulletin of Mathematical Biology 60, no. 6 (1998): 1167–200. http://dx.doi.org/10.1006/bulm.1998.0057.

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34

De Vries, G. "Diffusively Coupled Bursters: Effects of Cell Heterogeneity." Bulletin of Mathematical Biology 61, no. 5 (1999): 1017. http://dx.doi.org/10.1006/bulm.1999.0125.

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35

Morrill, S. M., M. L. Langer, R. G. Lane, and I. I. Rosen. "Tissue heterogeneity effects in treatment plan optimization." International Journal of Radiation Oncology*Biology*Physics 30, no. 3 (1994): 699–706. http://dx.doi.org/10.1016/0360-3016(92)90958-k.

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36

Garlick, Robert, John Walker, and Kate Orkin. "Heterogeneity in Effects of Poverty-Alleviating Interventions." AEA Papers and Proceedings 115 (May 1, 2025): 335–39. https://doi.org/10.1257/pandp.20251061.

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To what extent are poverty-alleviating interventions general tools? We revisit a 415-village cluster-randomized field experiment involving 8,300 women living in poverty in rural Kenya. We estimate the extent of heterogeneity in a cash transfer intervention, an aspirations-improving workshop, and their combination using median split regressions and causal forests. We find no evidence of heterogeneity with respect to a prespecified set of demographic, psychological, and economic welfare characteristics. We conclude that the poverty-alleviating interventions we consider are general tools that can

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37

Tsai, Fu-Sheng, and I.-Chieh Hsu. "The effects of social capital on knowledge heterogeneity." Management Decision 57, no. 5 (2019): 1237–53. http://dx.doi.org/10.1108/md-12-2016-0909.

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Purpose The purpose of this paper is to examine the influences of social capital on knowledge heterogeneity in order to advance the understanding of the effects and to reconcile existing inconsistent findings. Design/methodology/approach Survey data collected from 105 new product development (NPD) projects were analyzed with regression-based methods. Findings The results indicated that trust, centralization and shared vision as the three social capital dimensions generally have negative impacts on the domain and presentation dimensions of knowledge heterogeneity. However, the three dimensions

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38

Olarreaga, Marcelo, Stefan Sperlich, and Virginie Trachsel. "Exploring the Heterogeneous Effects of Export Promotion." World Bank Economic Review 34, no. 2 (2019): 332–50. http://dx.doi.org/10.1093/wber/lhy034.

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Abstract A semiparametric varying coefficient model is used to explore the heterogeneity in returns to export promotion across countries. Differences in characteristics of export-promotion agencies drive the heterogeneity in returns. Interestingly, characteristics that matter for export growth do not necessarily matter for GDP per capita growth. A 1 percent increase in export-promotion budgets is associated with an average increase in exports of 0.10 percent and an average increase in GDP per capita of 0.06 percent. However, these average returns hide a lot of heterogeneity. Returns in terms o

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39

Xi, Nianxun, Chunhui Zhang, and Juliette M. G. Bloor. "Species richness alters spatial nutrient heterogeneity effects on above-ground plant biomass." Biology Letters 13, no. 12 (2017): 20170510. http://dx.doi.org/10.1098/rsbl.2017.0510.

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Previous studies have suggested that spatial nutrient heterogeneity promotes plant nutrient capture and growth. However, little is known about how spatial nutrient heterogeneity interacts with key community attributes to affect plant community production. We conducted a meta-analysis to investigate how nitrogen heterogeneity effects vary with species richness and plant density. Effect size was calculated using the natural log of the ratio in plant biomass between heterogeneous and homogeneous conditions. Effect sizes were significantly above zero, reflecting positive effects of spatial nutrien

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40

Stanley, T. D., Hristos Doucouliagos, and John P. A. Ioannidis. "Beyond Random Effects: When Small-Study Findings Are More Heterogeneous." Advances in Methods and Practices in Psychological Science 5, no. 4 (2022): 251524592211204. http://dx.doi.org/10.1177/25152459221120427.

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New meta-regression methods are introduced that identify whether the magnitude of heterogeneity across study findings is correlated with their standard errors. Evidence from dozens of meta-analyses finds robust evidence of this correlation and that small-sample studies typically have higher heterogeneity. This correlated heterogeneity violates the random-effects (RE) model of additive and independent heterogeneity. When small studies not only have inadequate statistical power but also high heterogeneity, their scientific contribution is even more dubious. When the heterogeneity variance is cor

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41

Li, Lingcheng, Gautam Bisht, and L. Ruby Leung. "Spatial heterogeneity effects on land surface modeling of water and energy partitioning." Geoscientific Model Development 15, no. 14 (2022): 5489–510. http://dx.doi.org/10.5194/gmd-15-5489-2022.

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Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and e

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42

Atchley, Adam L., Rodman Linn, Alex Jonko, et al. "Effects of fuel spatial distribution on wildland fire behaviour." International Journal of Wildland Fire 30, no. 3 (2021): 179. http://dx.doi.org/10.1071/wf20096.

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The distribution of fuels is recognised as a key driver of wildland fire behaviour. However, our understanding of how fuel density heterogeneity affects fire behaviour is limited because of the challenges associated with experiments that isolate fuel heterogeneity from other factors. Advances in fire behaviour modelling and computational resources provide a means to explore fire behaviour responses to fuel heterogeneity. Using an ensemble approach to simulate fire behaviour in a coupled fire–atmosphere model, we systematically tested how fuel density fidelity and heterogeneity shape effective

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43

Ivanov, Igor, Alejandro Cruz, Alexander Zhuravlev, et al. "Conformational Heterogeneity and Cooperative Effects of Mammalian ALOX15." International Journal of Molecular Sciences 22, no. 6 (2021): 3285. http://dx.doi.org/10.3390/ijms22063285.

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Arachidonic acid lipoxygenases (ALOXs) have been suggested to function as monomeric enzymes, but more recent data on rabbit ALOX15 indicated that there is a dynamic monomer-dimer equilibrium in aqueous solution. In the presence of an active site ligand (the ALOX15 inhibitor RS7) rabbit ALOX15 was crystalized as heterodimer and the X-ray coordinates of the two monomers within the dimer exhibit subtle structural differences. Using native polyacrylamide electrophoresis, we here observed that highly purified and predominantly monomeric rabbit ALOX15 and human ALOX15B are present in two conformers

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44

Campanella, Mario, Serge P. Hoogendoorn, and Winnie Daamen. "Effects of Heterogeneity on Self-Organized Pedestrian Flows." Transportation Research Record: Journal of the Transportation Research Board 2124, no. 1 (2009): 148–56. http://dx.doi.org/10.3141/2124-14.

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45

Riemsdijk, W. H. van, L. K. Koopal, and J. C. M. de Wit. "Heterogeneity and electrolyte adsorption: intrinsic and electrostatic effects." Netherlands Journal of Agricultural Science 35, no. 3 (1987): 241–57. http://dx.doi.org/10.18174/njas.v35i3.16722.

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Adsorption of electrolytes in natural systems is an important phenomenon. Natural systems contain sorbents with a predominantly constant surface charge, such as clay minerals, as well as a complex mixture of mainly variable charge sorbents like organic matter, both in the dissolved and in the soil solid phase, and metal oxides. The effect of a combination of an intrinsic-affinity distribution (both discrete and continuous) and a variable surface potential on adsorption behaviour of protons is discussed both for random and patchwise heterogeneity. It is shown that the variable charge (potential

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46

Hoshino, Tadao, and Takahide Yanagi. "Estimating marginal treatment effects under unobserved group heterogeneity." Journal of Causal Inference 10, no. 1 (2022): 197–216. http://dx.doi.org/10.1515/jci-2021-0052.

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Abstract This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the treatment choice and outcome equations can be heterogeneous across groups. Under the availability of instrumental variables specific to each group, we show that the MTE for each group can be separately identified. On the basis of our identification result, we propose a two-step semiparametric procedure for estimating the group-wise MTE.

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47

Di Maio, Massimo, and Marco Tagliamento. "Heterogeneity of treatment effects in malignant pleural mesothelioma." Lancet 398, no. 10297 (2021): 301–2. http://dx.doi.org/10.1016/s0140-6736(21)00882-5.

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48

Munoz, Gérald, Alain Dequidt, Nicolas Martzel, et al. "Heterogeneity Effects in Highly Cross-Linked Polymer Networks." Polymers 13, no. 5 (2021): 757. http://dx.doi.org/10.3390/polym13050757.

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Despite their level of refinement, micro-mechanical, stretch-based and invariant-based models, still fail to capture and describe all aspects of the mechanical properties of polymer networks for which they were developed. This is for an important part caused by the way the microscopic inhomogeneities are treated. The Elastic Network Model (ENM) approach of reintroducing the spatial resolution by considering the network at the level of its topological constraints, is able to predict the macroscopic properties of polymer networks up to the point of failure. We here demonstrate the ability of ENM

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49

You-Zhi, WANG Zheng-Quan* and GU Jia-Cun, and $author.xingMing_EN. "THE EFFECTS OF SPATIAL HETEROGENEITY OF UNDERSTOREY LIGHT." Chinese Journal of Plant Ecology 28, no. 4 (2004): 468–75. http://dx.doi.org/10.17521/cjpe.2004.0064.

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50

Barajas, Carlos, and Domitilla Del Vecchio. "Effects of spatial heterogeneity on bacterial genetic circuits." PLOS Computational Biology 16, no. 9 (2020): e1008159. http://dx.doi.org/10.1371/journal.pcbi.1008159.

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