Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis

Amphiregulin and epiregulin mRNA expression in primary tumors predicts outcome in metastatic colorectal cancer treated with cetuximab.

Jacobs, B., De Roock, W., Piessevaux, H., Van Oirbeek, R., Biesmans, B., De Schutter, J., Fieuws, S., Vandesompele, J., Peeters, M., Van Laethem, J.-L., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.21.3744 · Cited by 368

Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers

Austin, P. C., & Steyerberg, E. W. (2014) · Statistics in Medicine · doi:10.1002/sim.5941 · Cited by 362

Can people with type 2 diabetes live longer than those without? A comparison of mortality in people initiated with metformin or sulphonylurea monotherapy and matched, non-diabetic controls

Bannister, C. A., Holden, S. E., Jenkins‐Jones, S., Morgan, C. L., Halcox, J. P., Schernthaner, G., Mukherjee, J., & Currie, C. J. (2014) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.12354 · Cited by 356

Evaluation of Newer Risk Markers for Coronary Heart Disease Risk Classification: A Cohort Study

Kavousi, M., Elias-Smale, S., Rutten, J. H. W., Leening, M. J. G., Vliegenthart, R., Verwoert, G. C., Krestin, G. P., Oudkerk, M., de Maat, M. P. M., Leebeek, F. W. G., et al. (2012) · Annals of Internal Medicine · doi:10.7326/0003-4819-156-6-201203200-00006 · Cited by 360

Limitations of Ejection Fraction for Prediction of Sudden Death Risk in Patients With Coronary Artery Disease

Buxton, A. E., Lee, K. L., Hafley, G. E., Pires, L. A., Fisher, J. D., Gold, M. R., Josephson, M. E., Lehmann, M. H., & Prystowsky, E. N. (2007) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2007.04.095 · Cited by 356

Association between age at diabetes onset and subsequent risk of dementia

Barbiellini Amidei, C., Fayosse, A., Dumurgier, J., Machado-Fragua, M. D., Tabak, A. G., van Sloten, T., Kivimäki, M., Dugravot, A., Sabia, S., & Singh-Manoux, A. (2021) · JAMA · doi:10.1001/jama.2021.4001 · Cited by 355

Association Between Bariatric Surgery and Macrovascular Disease Outcomes in Patients With Type 2 Diabetes and Severe Obesity

Fisher, D. P., Johnson, E., Haneuse, S., Arterburn, D., Coleman, K. J., O’Connor, P. J., O’Brien, R., Bogart, A., Theis, M. K., Anau, J., et al. (2018) · JAMA · doi:10.1001/jama.2018.14619 · Cited by 305

Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity

Brage, S., Ekelund, U., Brage, N., Hennings, M. A., Froberg, K., Franks, P. W., & Wareham, N. J. (2007) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00092.2006 · Cited by 303

Optimal amount of calories for critically ill patients: Depends on how you slice the cake!*

Heyland, D. K., Cahill, N., & Day, A. G. (2011) · Critical Care Medicine · doi:10.1097/ccm.0b013e318226641d · Cited by 293

Effect of Race on Cultural Justifications for Caregiving

Dilworth-Anderson, P., Brummett, B. H., Goodwin, P., Williams, S. W., Williams, R. B., & Siegler, I. C. (2005) · The Journals of Gerontology: Series B · doi:10.1093/geronb/60.5.s257 · Cited by 285

Good Research Practices for Comparative Effectiveness Research: Analytic Methods to Improve Causal Inference from Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—Part III

Johnson, M. L., Crown, W., Martin, B. C., Dormuth, C. R., & Siebert, U. (2009) · Value in Health · doi:10.1111/j.1524-4733.2009.00602.x · Cited by 285

Obstructive Sleep Apnea and Incident Diabetes. A Historical Cohort Study

Kendzerska, T., Gershon, A. S., Hawker, G., Tomlinson, G., & Leung, R. S. (2014) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201312-2209oc · Cited by 280

Generic, Simple Risk Stratification Model for Heart Valve Surgery

Ambler, G., Omar, R. Z., Royston, P., Kinsman, R., Keogh, B. E., & Taylor, K. M. (2005) · Circulation · doi:10.1161/circulationaha.104.515049 · Cited by 279

Occurrence of amyotrophic lateral sclerosis among Gulf War veterans

Horner, R. D., Kamins, K. G., Feussner, J. R., Grambow, S. C., Hoff-Lindquist, J., Harati, Y., Mitsumoto, H., Pascuzzi, R., Spencer, P. S., Tim, R., et al. (2003) · Neurology · doi:10.1212/01.wnl.0000069922.32557.ca · Cited by 277

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning

Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2018) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12916 · Cited by 264

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable

Austin, P. C., & Steyerberg, E. W. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-82 · Cited by 270

Impact of prenatal exercise on both prenatal and postnatal anxiety and depressive symptoms: a systematic review and meta-analysis

Davenport, M. H., McCurdy, A. P., Mottola, M. F., Skow, R. J., Meah, V. L., Poitras, V. J., Jaramillo Garcia, A., Gray, C. E., Barrowman, N., Riske, L., et al. (2018) · British Journal of Sports Medicine · doi:10.1136/bjsports-2018-099697 · Cited by 269

Optimal dose and type of exercise to improve cognitive function in older adults: a systematic review and Bayesian model-based network meta-analysis of RCTs

Gallardo-Gómez, D., del Pozo-Cruz, J., Noetel, M., Álvarez-Barbosa, F., Alfonso-Rosa, R. M., & del Pozo Cruz, B. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101591 · Cited by 268

Effect of home testing of international normalized ratio on clinical events

Matchar, D. B., Jacobson, A., Dolor, R., Edson, R., Uyeda, L., Phibbs, C. S., Vertrees, J. E., Shih, M.-C., Holodniy, M., & Lavori, P. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1002617 · Cited by 266

Fructosamine and glycated albumin for risk stratification and prediction of incident diabetes and microvascular complications: a prospective cohort analysis of the Atherosclerosis Risk in Communities (ARIC) study

Selvin, E., Rawlings, A. M., Grams, M., Klein, R., Sharrett, A. R., Steffes, M., & Coresh, J. (2014) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(13)70199-2 · Cited by 262

Association between systolic blood pressure and dementia in the Whitehall II cohort study: role of age, duration, and threshold used to define hypertension

Abell, J. G., Kivimäki, M., Dugravot, A., Tabak, A. G., Fayosse, A., Shipley, M., Sabia, S., & Singh-Manoux, A. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy288 · Cited by 252

Development and Validation of a Clinically Based Risk Calculator for the Transdiagnostic Prediction of Psychosis

Fusar-Poli, P., Rutigliano, G., Stahl, D., Davies, C., Bonoldi, I., Reilly, T., & McGuire, P. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0284 · Cited by 246

External Validity of Risk Models: Use of Benchmark Values to Disentangle a Case-Mix Effect From Incorrect Coefficients

Vergouwe, Y., Moons, K. G. M., & Steyerberg, E. W. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq223 · Cited by 243

L2 Vocabulary Learning From Reading: Explicit and Tacit Lexical Knowledge and the Role of Learner and Item Variables

Elgort, I., & Warren, P. (2014) · Language Learning · doi:10.1111/lang.12052 · Cited by 240

Association of blood pressure classification in Korean young adults according to the 2017 American College of Cardiology/American Heart Association guidelines with subsequent cardiovascular disease events.

Son, J. S., Choi, S., Kim, K., Kim, S. M., Choi, D., Lee, G., Jeong, S.-M., Park, S. Y., Kim, Y.-Y., Yun, J.-M., et al. (2018) · JAMA · doi:10.1001/jama.2018.16501 · Cited by 225

Suicide prediction models: a critical review of recent research with recommendations for the way forward

Kessler, R. C., Bossarte, R. M., Luedtke, A., Zaslavsky, A. M., & Zubizarreta, J. R. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0531-0 · Cited by 234

Moderation of Adult Depression by a Polymorphism in the FKBP5 Gene and Childhood Physical Abuse in the General Population

Appel, K., Schwahn, C., Mahler, J., Schulz, A., Spitzer, C., Fenske, K., Stender, J., Barnow, S., John, U., Teumer, A., et al. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.81 · Cited by 234

A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome

Ambler, G., Omar, R. Z., & Royston, P. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074466 · Cited by 233

Decreased Cancer Risk After Iron Reduction in Patients With Peripheral Arterial Disease: Results From a Randomized Trial

Zacharski, L. R., Chow, B. K., Howes, P. S., Shamayeva, G., Baron, J. A., Dalman, R. L., Malenka, D. J., Ozaki, C. K., & Lavori, P. W. (2008) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djn209 · Cited by 230

Diagnostic and prognostic prediction models

Hendriksen, J. M. T., Geersing, G. J., Moons, K. G. M., & de Groot, J. A. H. (2013) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.12262 · Cited by 229

Investigating clinical heterogeneity in systematic reviews: a methodologic review of guidance in the literature

Gagnier, J. J., Moher, D., Boon, H., Beyene, J., & Bombardier, C. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-111 · Cited by 222

Placental DNA hypomethylation in association with particulate air pollution in early life

Janssen, B. G., Godderis, L., Pieters, N., Poels, K., Kiciński, M., Cuypers, A., Fierens, F., Penders, J., Plusquin, M., Gyselaers, W., et al. (2013) · Particle and Fibre Toxicology · doi:10.1186/1743-8977-10-22 · Cited by 222

Assessing the incremental value of diagnostic and prognostic markers: a review and illustration

Steyerberg, E. W., Pencina, M. J., Lingsma, H. F., Kattan, M. W., Vickers, A. J., & Van Calster, B. (2012) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2011.02562.x · Cited by 219

Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.

Burton, A., & Altman, D. G. (2004) · British Journal of Cancer · doi:10.1038/sj.bjc.6601907 · Cited by 219

Reporting methods in studies developing prognostic models in cancer: a review

Mallett, S., Royston, P., Dutton, S., Waters, R., & Altman, D. G. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-20 · Cited by 206

Effect of low glycaemic index or load dietary patterns on glycaemic control and cardiometabolic risk factors in diabetes: systematic review and meta-analysis of randomised controlled trials

Chiavaroli, L., Lee, D., Ahmed, A., Cheung, A., Khan, T. A., Blanco, S., Mejia, Mirrahimi, A., Jenkins, D. J. A., Livesey, G., et al. (2021) · BMJ · doi:10.1136/bmj.n1651 · Cited by 215

Corporate bankruptcy prediction: a high dimensional analysis

Jones, S. (2017) · Review of Accounting Studies · doi:10.1007/s11142-017-9407-1 · Cited by 214

Association of Lithium in Drinking Water With the Incidence of Dementia

Kessing, L. V., Gerds, T. A., Knudsen, N. N., Jørgensen, L. F., Kristiansen, S. M., Voutchkova, D., Ernstsen, V., Schullehner, J., Hansen, B., Andersen, P. K., et al. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.2362 · Cited by 213

Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

Zahrt, A. F., Athavale, S. V., & Denmark, S. E. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00425 · Cited by 212

Overfitting, underfitting and general model overconfidence and under-performance pitfalls and best practices in machine learning and AI

Aliferis, C., & Simon, G. (2024) · Health Informatics · doi:10.1007/978-3-031-39355-6_10 · Cited by 212

Gene mutations and response to treatment with all-trans retinoic acid in elderly patients with acute myeloid leukemia. Results from the AMLSG Trial AML HD98B

Schlenk, R. F., Dohner, K., Kneba, M., Gotze, K., Hartmann, F., del Valle, F., Kirchen, H., Koller, E., Fischer, J. T., Bullinger, L., et al. (2009) · Haematologica · doi:10.3324/haematol.13378 · Cited by 210

Relationships of diabetes-specific emotional distress, depression, anxiety, and overall well-being with HbA1c in adult persons with type 1 diabetes

Strandberg, R. B., Graue, M., Wentzel-Larsen, T., Peyrot, M., & Rokne, B. (2014) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2014.06.015 · Cited by 208

A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods

Collins, G. S., Omar, O., Shanyinde, M., & Yu, L.-M. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.06.020 · Cited by 192

Association of ideal cardiovascular health at age 50 with incidence of dementia: 25 year follow-up of Whitehall II cohort study

Sabia, S., Fayosse, A., Dumurgier, J., Schnitzler, A., Empana, J.-P., Ebmeier, K. P., Dugravot, A., Kivimäki, M., & Singh-Manoux, A. (2019) · BMJ · doi:10.1136/bmj.l4414 · Cited by 205

Carotid plaque thickness and carotid plaque burden predict future cardiovascular events in asymptomatic adult Americans

Sillesen, H., Sartori, S., Sandholt, B., Baber, U., Mehran, R., & Fuster, V. (2018) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jex239 · Cited by 204

Augmented backward elimination: a pragmatic and purposeful way to develop statistical models

Dunkler, D., Plischke, M., Leffondré, K., & Heinze, G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113677 · Cited by 194

Minimum sample size for external validation of a clinical prediction model with a continuous outcome

Archer, L., Snell, K. I. E., Ensor, J., Hudda, M. T., Collins, G. S., & Riley, R. D. (2021) · Statistics in Medicine · doi:10.1002/sim.8766 · Cited by 198

Association of high amounts of physical activity with mortality risk: a systematic review and meta-analysis

Blond, K., Brinkløv, C. F., Ried-Larsen, M., Crippa, A., & Grøntved, A. (2020) · British Journal of Sports Medicine · doi:10.1136/bjsports-2018-100393 · Cited by 201

Predicting outcomes in idiopathic pulmonary fibrosis using automated computed tomographic analysis

Jacob, J., Bartholmai, B. J., Rajagopalan, S., van Moorsel, C. H. M., van Es, H. W., van Beek, F. T., Struik, M. H. L., Kokosi, M., Egashira, R., Brun, A. L., et al. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201711-2174oc · Cited by 198

A culture-brain link: negative age stereotypes predict Alzheimer's disease biomarkers

Levy, B. R., Ferrucci, L., Zonderman, A. B., Slade, M. D., Troncoso, J., & Resnick, S. M. (2016) · Psychology and Aging · doi:10.1037/pag0000062 · Cited by 198

Use of selective serotonin reuptake inhibitors during pregnancy and risk of autism

Hviid, A., Melbye, M., & Pasternak, B. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1301449 · Cited by 195

Reporting performance of prognostic models in cancer: a review.

Mallett, S., Royston, P., Waters, R., Dutton, S., & Altman, D. G. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-21 · Cited by 195

Shortfalls and opportunities in terrestrial vertebrate species discovery

Moura, M. R., & Jetz, W. (2021) · Nature Ecology & Evolution · doi:10.1038/s41559-021-01411-5 · Cited by 196

Estimated glomerular filtration rate and albuminuria are independent predictors of cardiovascular events and death in type 2 diabetes mellitus: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study

Drury, P. L., Ting, R., Zannino, D., Ehnholm, C., Flack, J., Whiting, M., Fassett, R., Ansquer, J.-C., Dixon, P., Davis, T. M. E., et al. (2011) · Diabetologia · doi:10.1007/s00125-010-1854-1 · Cited by 195

Global risk scores and exercise testing for predicting all-cause mortality in a preventive medicine program

Aktas, M. K. (2004) · JAMA · doi:10.1001/jama.292.12.1462 · Cited by 193

Clinical Effectiveness of Individual Cognitive Behavioral Therapy for Depressed Older People in Primary Care: A Randomized Controlled Trial

Serfaty, M. A., Haworth, D., Blanchard, M., Buszewicz, M., Murad, S., & King, M. (2009) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2009.165 · Cited by 190

A Population-Based Cohort Study Examining the Incidence and Impact of Psychotic Experiences From Childhood to Adulthood, and Prediction of Psychotic Disorder

Sullivan, S. A., Kounali, D., Cannon, M., David, A. S., Fletcher, P. C., Holmans, P., Jones, H., Jones, P. B., Linden, D. E. J., Lewis, G., et al. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.19060654 · Cited by 176

Maternal body mass index and gestational weight gain and their association with perinatal outcomes in Viet Nam

Ota, E., Haruna, M., Suzuki, M., Anh, D. D., Tho, L. H., Tam, N. T. T., Thiem, V. D., Anh, C. N. T. H., Isozaki, M., Shibuya, K., et al. (2011) · Bulletin of the World Health Organization · doi:10.2471/blt.10.077982 · Cited by 185

Sugar-sweetened beverages consumption and the risk of depression: A meta-analysis of observational studies

Hu, D., Cheng, L., & Jiang, W. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.11.015 · Cited by 181

Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model

Collins, G. S., Ogundimu, E. O., Cook, J. A., Manach, Y. L., & Altman, D. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6986 · Cited by 185

Habitual Coffee Consumption and Risk of Heart Failure: A Dose-Response Meta-Analysis

Mostofsky, E., Rice, M. S., Levitan, E. B., & Mittleman, M. A. (2012) · Circulation: Heart Failure · doi:10.1161/circheartfailure.112.967299 · Cited by 185

Prediction of First Events of Coronary Heart Disease and Stroke With Consideration of Adiposity

Wilson, P. W. F., Bozeman, S. R., Burton, T. M., Hoaglin, D. C., Ben-Joseph, R., & Pashos, C. L. (2008) · Circulation · doi:10.1161/circulationaha.108.772962 · Cited by 174

Long‐term psychological benefits of cognitive‐behavioral stress management for women with breast cancer: 11‐year follow‐up of a randomized controlled trial

Stagl, J. M., Bouchard, L. C., Lechner, S. C., Blomberg, B. B., Gudenkauf, L. M., Jutagir, D. R., Glück, S., Derhagopian, R. P., Carver, C. S., & Antoni, M. H. (2015) · Cancer · doi:10.1002/cncr.29076 · Cited by 174

Real-time suicide mortality data from police reports in Queensland, Australia, during the COVID-19 pandemic: an interrupted time-series analysis.

Leske, S., Kõlves, K., Crompton, D., Arensman, E., & de Leo, D. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30435-1 · Cited by 163

Integrated Analyses of Microbiome and Longitudinal Metabolome Data Reveal Microbial-Host Interactions on Sulfur Metabolism in Parkinson’s Disease

Hertel, J., Harms, A. C., Heinken, A., Baldini, F., Thinnes, C. C., Glaab, E., Vasco, D. A., Pietzner, M., Stewart, I. D., Wareham, N. J., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.035 · Cited by 162

Biobehavioral, immune, and health benefits following recurrence for psychological intervention participants

Andersen, B. L., Thornton, L. M., Shapiro, C. L., Farrar, W. B., Mundy, B. L., Yang, H.-C., & Carson, W. E. (2010) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-0278 · Cited by 158

Fructosamine and glycated albumin and the risk of cardiovascular outcomes and death

Selvin, E., Rawlings, A. M., Lutsey, P. L., Maruthur, N., Pankow, J. S., Steffes, M., & Coresh, J. (2015) · Circulation · doi:10.1161/circulationaha.115.015415 · Cited by 151

Hypertensive Disorders of Pregnancy and 10-Year Cardiovascular Risk Prediction

Stuart, J. J., Tanz, L. J., Cook, N. R., Spiegelman, D., Missmer, S. A., Rimm, E. B., Rexrode, K. M., Mukamal, K. J., & Rich-Edwards, J. W. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.05.077 · Cited by 154

Associations of the serotonin transporter promoter polymorphism with aggressivity, attention deficit, and conduct disorder in an adoptee population

Cadoret, R. J., Langbehn, D., Caspers, K., Troughton, E. P., Yucuis, R., Sandhu, H. K., & Philibert, R. (2003) · Comprehensive Psychiatry · doi:10.1053/comp.2003.50018 · Cited by 152

Rumination and Perceived Impairment Associated With Depressive Symptoms in a Verbal Adolescent–Adult ASD Sample

Gotham, K., Bishop, S. L., Brunwasser, S., & Lord, C. (2014) · Autism Research · doi:10.1002/aur.1377 · Cited by 149

Maternal antidepressant use and adverse outcomes: a cohort study of 228,876 pregnancies

Hayes, R. M., Wu, P., Shelton, R. C., Cooper, W. O., Dupont, W. D., Mitchel, E., & Hartert, T. V. (2012) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2012.04.028 · Cited by 149

Obstructive sleep apnoea and perioperative complications in bariatric patients

Weingarten, T. N., Flores, A. S., McKenzie, J. A., Nguyen, L. T., Robinson, W. B., Kinney, T. M., Siems, B. T., Wenzel, P. J., Sarr, M. G., Marienau, M. S., et al. (2011) · British Journal of Anaesthesia · doi:10.1093/bja/aeq290 · Cited by 146

Primary care autism screening and later autism diagnosis

Carbone, P. S., Campbell, K., Wilkes, J., Stoddard, G. J., Huynh, K., Young, P. C., & Gabrielsen, T. P. (2020) · Pediatrics · doi:10.1542/peds.2019-2314 · Cited by 144

Homelessness and Housing Insecurity Among Former Prisoners

Herbert, C. W., Morenoff, J. D., & Harding, D. J. (2015) · RSF: The Russell Sage Foundation Journal of the Social Sciences · doi:10.7758/rsf.2015.1.2.04 · Cited by 146

Social and psychological factors influenced the course of arm, neck and shoulder complaints

Karels, C. H., Bierma-Zeinstra, S. M. A., Burdorf, A., Verhagen, A. P., Nauta, A. P., & Koes, B. W. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.11.012 · Cited by 140

Can one assess whether missing data are missing at random in medical studies?

Potthoff, R. F., Tudor, G. E., Pieper, K. S., & Hasselblad, V. (2006) · Statistical Methods in Medical Research · doi:10.1191/0962280206sm448oa · Cited by 141

Validation of clinical prediction models: what does the “calibration slope” really measure?

Stevens, R. J., & Poppe, K. K. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.09.016 · Cited by 146

Determinants of child morbidity in Latin America: A pooled analysis of interactions between parental education and economic status

Hatt, L. E., & Waters, H. R. (2006) · Social Science & Medicine · doi:10.1016/j.socscimed.2005.06.007 · Cited by 140

Novel genetic markers improve measures of atrial fibrillation risk prediction

Everett, B. M., Cook, N. R., Conen, D., Chasman, D. I., Ridker, P. M., & Albert, C. M. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht033 · Cited by 141

Exposure–Response Relationships for Select Cancer and Non- Cancer Health Outcomes in a Cohort of US Firefighters from San Francisco, Chicago and Philadelphia (1950–2009)

Daniels, R. D., Bertke, S., Dahm, M. M., Yiin, J. H., Kubale, T. L., Hales, T. R., Baris, D., Zahm, S. H., Beaumont, J. J., Waters, K. M., et al. (2015) · Occupational and Environmental Medicine · doi:10.1136/oemed-2014-102671 · Cited by 140

Profile and predictors of service needs for families of children with autism spectrum disorders

Hodgetts, S., Zwaigenbaum, L., & Nicholas, D. (2015) · Autism · doi:10.1177/1362361314543531 · Cited by 138

Predicting Defective Lines Using a Model-Agnostic Technique

Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H., & Matsumoto, K. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3023177 · Cited by 137

Measurement in clinical trials: A neglected issue for statisticians?

Senn, S., & Julious, S. (2009) · Statistics in Medicine · doi:10.1002/sim.3603 · Cited by 136

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

Ruderfer, D. M., Walsh, C. G., Aguirre, M. W., Tanigawa, Y., Ribeiro, J. D., Franklin, J. C., & Rivas, M. A. (2020) · Molecular Psychiatry · doi:10.1038/s41380-018-0326-8 · Cited by 134

The Advanced Dementia Prognostic Tool: A Risk Score to Estimate Survival in Nursing Home Residents with Advanced Dementia

Mitchell, S. L., Miller, S. C., Teno, J. M., Davis, R. B., & Shaffer, M. L. (2010) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2010.02.014 · Cited by 134

Optimising risk stratification in primary biliary cirrhosis: AST/platelet ratio index predicts outcome independent of ursodeoxycholic acid response

Trivedi, P. J., Bruns, T., Cheung, A., Li, K.-K., Kittler, C., Kumagi, T., Shah, H., Corbett, C., Al-Harthy, N., Acarsu, U., et al. (2014) · Journal of Hepatology · doi:10.1016/j.jhep.2014.01.029 · Cited by 132

Measuring neural representations with fMRI: practices and pitfalls

Davis, T., & Poldrack, R. A. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12156 · Cited by 132

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery: Systematic Review and Validation Study

Ettema, R. G. A., Peelen, L. M., Schuurmans, M. J., Nierich, A. P., Kalkman, C. J., & Moons, K. G. M. (2010) · Circulation · doi:10.1161/circulationaha.109.926808 · Cited by 130

Test of an Interpersonal Model of Hypochondriasis

Noyes, R., Stuart, S. P., Langbehn, D. R., Happel, R. L., Longley, S. L., Muller, B. A., & Yagla, S. J. (2003) · Psychosomatic Medicine · doi:10.1097/01.psy.0000058377.50240.64 · Cited by 130

Improving GRADE evidence tables part 1: a randomized trial shows improved understanding of content in summary of findings tables with a new format

Carrasco-Labra, A., Brignardello-Petersen, R., Santesso, N., Neumann, I., Mustafa, R. A., Mbuagbaw, L., Etxeandia Ikobaltzeta, I., De Stio, C., McCullagh, L. J., Alonso-Coello, P., et al. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.12.007 · Cited by 129

A pilot randomized controlled trial of mindfulness-based stress reduction for caregivers of family members with dementia.

Brown, K. W., Coogle, C. L., & Wegelin, J. (2016) · Aging & Mental Health · doi:10.1080/13607863.2015.1065790 · Cited by 126

The IntCal20 approach to radiocarbon calibration curve construction: A new methodology using Bayesian splines and errors-in-variables

Heaton, T. J., Blaauw, M., Blackwell, P. G., Bronk Ramsey, C., Reimer, P. J., & Scott, E. M. (2020) · Radiocarbon · doi:10.1017/rdc.2020.46 · Cited by 124

Longer Wait Times Increase Overall Mortality in Patients With Bladder Cancer

Kulkarni, G. S., Urbach, D. R., Austin, P. C., Fleshner, N. E., & Laupacis, A. (2009) · Journal of Urology · doi:10.1016/j.juro.2009.06.041 · Cited by 123

NEO personality domains and gender predict levels and trends in body mass index over 14 years during midlife

Brummett, B. H., Babyak, M. A., Williams, R. B., Barefoot, J. C., Costa, P. T., & Siegler, I. C. (2006) · Journal of Research in Personality · doi:10.1016/j.jrp.2004.12.002 · Cited by 122

Hypertensive disorders of pregnancy and the recent increase in obstetric acute renal failure in Canada: population based retrospective cohort study

Mehrabadi, A., Liu, S., Bartholomew, S., Hutcheon, J. A., Magee, L. A., Kramer, M. S., Liston, R. M., Joseph, K. S., & for the Canadian Perinatal Surveillance System (Public Health Agency of Canada) (2014) · BMJ · doi:10.1136/bmj.g4731 · Cited by 121

Spirituality, Religion, and Clinical Outcomes in Patients Recovering From an Acute Myocardial Infarction

Blumenthal, J. A., Babyak, M. A., Ironson, G., Thoresen, C., Powell, L., Czajkowski, S., Burg, M., Keefe, F. J., Steffen, P., & Catellier, D. (2007) · Psychosomatic Medicine · doi:10.1097/psy.0b013e3180cab76c · Cited by 119

Large‐scale, long‐term trends in British river macroinvertebrates

Vaughan, I. P., & Ormerod, S. J. (2012) · Global Change Biology · doi:10.1111/j.1365-2486.2012.02662.x · Cited by 119

Prognostic utility of novel biomarkers of cardiovascular stress in patients with aortic stenosis undergoing valve replacement

Lindman, B. R., Breyley, J. G., Schilling, J. D., Vatterott, A. M., Zajarias, A., Maniar, H. S., Damiano, R. J., Moon, M. R., Lawton, J. S., Gage, B. F., et al. (2015) · Heart · doi:10.1136/heartjnl-2015-307742 · Cited by 114

Optimal Dose and Type of Physical Activity to Improve Glycemic Control in People Diagnosed With Type 2 Diabetes: A Systematic Review and Meta-analysis

Gallardo-Gómez, D., Salazar-Martínez, E., Alfonso-Rosa, R. M., Ramos-Munell, J., del Pozo-Cruz, J., del Pozo Cruz, B., & Álvarez-Barbosa, F. (2024) · Diabetes Care · doi:10.2337/dc23-0800 · Cited by 137

Predicting Low Accrual in the National Cancer Institute’s Cooperative Group Clinical Trials

Bennette, C. S., Ramsey, S. D., McDermott, C. L., Carlson, J. J., Basu, A., & Veenstra, D. L. (2016) · Journal of the National Cancer Institute · doi:10.1093/jnci/djv324 · Cited by 108