There are often multiple discipline-specific terms for a given statistical concept, which can sow confusion in multidisciplinary teams or study sections if researchers are not aware of the synonyms from other disciplines. This article incorporates synonyms and a uniform definition of terminology related to study designs, elements of an equation, and types of bias. Greater multidisciplinary collaboration and exploration of new methods can be facilitated by this methods thesaurus.
Abadie, A., & Imbens, G.W. ( 2006). On the failure of the bootstrap for matching estimators (NBER Technical Working Paper Series No. 325). Cambridge, MA: National Bureau of Economic Research.
2.
Aday, L.A., Begley, C.E., Lairson, D.R., & Balkrishnan, R. (2004). Evaluating the healthcare system: Effectiveness, efficiency, and equity (3rd ed.). Chicago, IL: Health Administration Press.
3.
Aguinis, H. ( 2004). Regression analysis for categorical moderators. New York, NY: Guilford Press.
4.
Angrist, J.D., Imbens, G.W., & Rubin, D.B. ( 1996). Identification of causal effects using instrumental variables . Journal of the American Statistical Association, 91, 444-455.
5.
Armitage, P., Berry, G., & Matthews, J.N.S. (2002). Statistical methods in medical research (4th ed.). Boston, MA: Blackwell Scientific.
6.
Armitage, P., & Colton, T. ( 1998). Encyclopedia of biostatistics. Chichester , England: Wiley.
7.
Arterburn, D.E., Maciejewski, M.L., & Tsevat, J. ( 2005). The impact of morbid obesity on medical expenditures in adults. International Journal of Obesity, 29, 334-339.
8.
Bazzoli, G.J., Chan, B., Shortell, S.M. & D’Aunno, T. ( 2000). The financial performance of hospitals belonging to health networks and systems. Inquiry, 37, 234-252.
9.
Bazzoli, G.J., Miller, R.H., & Burns, L.R. ( 2000). Capitated contracting roles and relationships in healthcare . Journal of Healthcare Management, 45, 170-187.
10.
Bound, J., Jaeger, D.A., & Baker, R.M. ( 1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90, 443-450.
11.
Bowling, A., & Ebrahim, S. ( 2005). Handbook of health research methods: Investigation, measurement and analysis. Washington, DC: National Academies Press.
12.
Campbell, D.T., & Stanley, J.C. ( 1963). Experimental and quasi-experimental designs for research on teaching. Chicago, IL: Rand McNally .
13.
Daniel, W.W. ( 2005). Biostatistics: A foundation for analysis in the health sciences. Hoboken, NJ: Wiley.
14.
Darlington, G.A. ( 1998). Explanatory variables. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (pp. 1443-1444). New York, NY : Wiley.
15.
Donner, A., & Klar, N. ( 2000). Design and analysis of cluster randomization trials in health research. London, UK: Arnold .
16.
Finkelstein, E.A., Trogdon, J.G., Cohen, J.W., & Dietz, W. ( 2009). Annual medical spending attributable to obesity: Payer- and service-specific estimates. Health Affairs (Project Hope), 28, w822-w831.
17.
Fitzmaurice, G.M., Laird, N.M., & Ware, J.H. ( 2004). Applied longitudinal analysis. New York, NY: Wiley.
18.
Flegal, K.M., Graubard, B.I., Williamson, D.F., & Gail, M.H. ( 2005). Excess deaths associated with underweight, overweight, and obesity. Journal of the American Medical Association, 293, 1861-1867.
19.
Flegal, K.M., Williamson, D.F., Pamuk, E.R., & Rosenberg, H.M. (2004). Estimating deaths attributable to obesity in the United States. American Journal of Public Health, 94, 1486-1489.
20.
Gail, M.H. ( 1998). Selection bias. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (p. 4045). New York, NY: Wiley .
Greenland, S. ( 1998). Confounding. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (pp. 901-907). New York, NY: Wiley.
23.
Greenland, S., & Robins, J.M. ( 1986). Identifiability, exchangeability, and epidemiological confounding. International Journal of Epidemiology, 15, 413-419.
24.
Hebert, P.L., McBean, A.M., & Kane, R.L. ( 2005). Explaining trends in hospitalizations for pneumonia and influenza in the elderly. Medical Care Research and Review , 62, 560-582.
25.
Heckman, J. ( 1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5, 475-492.
26.
Heckman, J.J. ( 1979). Sample selection bias as a specification error. Econometrica: Journal of the Econometric Society, 47, 153-161.
27.
Heckman, J.J. ( 1985). Selection bias and self-selection. In J. Eatwell, M. Milgate, & P. Newmann (Eds.), The new Palgrave: A dictionary of economics (pp. 287-296). New York, NY : Stockton.
28.
Heckman, J.J., & Hotz, V.J. ( 1989). Choosing among alternative nonexperimental methods for estimating the impact of social programs: The case of manpower training. Journal of the American Statistical Association, 84, 862-874.
29.
Hernán, M.A., & Robins, J.M. ( 2006). Estimating causal effects from epidemiological data. Journal of Epidemiology & Community Health, 60, 578-586.
30.
Hosmer, D.W., & Lemeshow, S. ( 2000). Applied logistic regression (2nd ed.). New York, NY: Wiley.
Jones, B., & Wang, J. ( 1998). Panel study. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (pp. 3247-3249). New York, NY : Wiley.
33.
Kmenta, J. ( 1986). Elements of econometrics. New York, NY: Macmillan.
34.
Knowler, W.C., Barrett-Connor, E., Fowler, S.E., Hamman, R.F., Lachin, J.M., Walker, E.A., & Nathan, D.M. ( 2002). Diabetes prevention program research group 2002 reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin . New England Journal of Medicine, 346, 393-403.
35.
Kraemer, H.C., Kiernan, M., Essex, M.J., & Kupfer, D.J. ( 2006). Moderators and mediators: Comparing the Baron & Kenny and MacArthur approaches. Master lecture presented at the 27th annual meeting of the Society of Behavioral Medicine, San Fransisco, CA.
36.
Kraemer, H.C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. ( 2001). How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. American Journal of Psychiatry, 158, 848-856.
37.
Kraemer, H.C., Wilson, G.T., Fairburn, C.G., & Agras, W.S. ( 2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877-883.
38.
LaLonde, R.J. ( 1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76, 604-620.
39.
Lawlor, D.A., Hart, C.L., Hole, D.J., & Smith, G.D. ( 2006). Reverse causality and confounding and the associations of overweight and obesity with Mortality. Obesity, 14, 2294-2304.
40.
Machin, D., & Campbell, M.J. (2005). Design of studies for medical research. Hoboken, NJ: Wiley.
41.
Maciejewski, M.L., Diehr, P., Smith, M.A., & Hebert, P. ( 2002). Common methodological terms in health services research and their synonyms [correction of symptoms]. Medical Care , 40, 477-484.
42.
Manson, J.E., Stampfer, M.J., Hennekens, C.H., & Willett, W.C. ( 1987). Body weight and longevity. A reassessment. Journal of the American Medical Association, 257, 353-358.
43.
Marczyk, G.R., DeMatteo, D., & Festinger, D. ( 2005). Essentials of research design and methodology. Hoboken, NJ: Wiley.
44.
Mertens, D.M. ( 2005). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Thousand Oaks, CA: Sage.
45.
Murray, D.M. ( 1998). Design and analysis of group-randomized trials. New York, NY: Oxford University Press.
46.
Newhouse, J.P., & McClellan, M. ( 1998). Econometrics in outcomes research: The use of instrumental variables. Annual Review of Public Health, 19, 17-34.
47.
Pearl, J. ( 2000). Causality: Models, reasoning, and inference. Cambridge, England: Cambridge University Press.
48.
Petrie, A., & Sabin, C. ( 2005). Medical statistics at a glance. Malden, MA: Wiley-Blackwell.
49.
Piantadosi, S. ( 2005). Clinical trials: A methodologic perspective. Hoboken, NJ: Wiley-Interscience.
50.
Pindyck, R.S., & Rubinfeld, D.L. (1991). Econometric models and economic forecasts. New York, NY: McGraw-Hill.
51.
Pocock, S.J. ( 1974). Harmonic analysis applied to seasonal variations in sickness absence. Applied Statistics, 23, 103-120.
52.
Roberts, C., Troop, N., Connan, F., Treasure, J., & Campbell I.C. ( 2007). The effects of stress on body weight: Biological and psychological predictors of change in BMI. Obesity, 15, 3045-3055.
53.
Robins, J.M., Hernán, M. Á., & Brumback, B. ( 2000). Marginal structural models and causal inference in epidemiology . Epidemiology, 11, 550-560.
54.
Robins, J.M., Mark, S.D., & Newey, W.K. ( 1992). Estimating exposure effects by modelling the expectation of exposure conditional on confounders. Biometrics, 48, 479-495.
55.
Rosenbaum, P.R. ( 1989). Optimal matching for observational studies. Journal of the American Statistical Association, 84, 1024-1032.
56.
Rosenbaum, P.R., & Rubin, D.B. ( 1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
57.
Rosenbaum, P.R., & Rubin, D.B. ( 1984). Reducing bias in observational studies using sub-classification on the propensity score. Journal of the American Statistical Association, 79, 516-524.
58.
Rosenbaum, P.R., & Rubin, D.B. ( 1985). The bias due to incomplete matching. Biometrics, 41, 103-116.
59.
Rothman, K.J., & Greenland S. ( 1998). Modern epidemiology. Philadelphia , PA: Lippincott Raven.
60.
Rotnitzky A., & Robins J. ( 2003). Inverse probability weighted estimation in survival analysis . In Encyclopedia of biostatistics. Retrieved from http://biosun1.harvard.edu/~robins/publications/IPW-survival-encyclopedia-submitted-corrected.pdf
61.
Rubin, D.B. ( 1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology , 66, 688-701.
62.
Rubin, D.B. ( 1979). Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74, 318-328.
63.
Selvin, S. ( 2004). Statistical analysis of epidemiologic data. New York, NY: Oxford University Press.
64.
Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.
65.
Slone, D., Shapiro, S., Miettinen, O.S., Finkle, W.D., & Stolley, P.D. ( 1979). Drug evaluation after marketing. Annals of Internal Medicine, 90, 257-261.
Trochim, W.M.K. ( 2001). Research methods knowledge base (2nd ed.). Cincinnati, OH: Atomic Dog .
68.
Tumbarello, M., Tacconelli, E., de Gaetano, K., Ardit, F., Pirronti, T., Claudia, R., & Ortona, L. ( 1998). Bacterial pneumonia in HIV-infected patients: Analysis of risk factors and prognostic indicators. Journal of Acquired Immune Deficiency Syndromes and Human Retroviology, 18, 39-45.
69.
van Belle, G., Fisher, L.D., Heagerty, P.J., & Lumley, T.S. ( 2004). Biostatistics: A methodology for the health sciences . Hoboken, NJ: Wiley-Interscience .
70.
Vogt, W.P. ( 2005). Dictionary of statistics & methodology: A nontechnical guide for the social sciences. Thousand Oaks, CA: Sage.
71.
Voils, C.I., Steffens, D.C., Flint, E.P., & Bosworth, H.B. (2005). Social support and locus of control as predictors of adherence to antidepressant medication in an elderly population. American Journal of Geriatric Psychiatry, 13, 157-165.
72.
Winship, C., & Mare, R.D. ( 1992). Models for sample selection bias. Annual Review of Sociology, 18, 327-350.
73.
Wooldridge, J.M. ( 2002). Econometric analysis of cross section and panel data . Cambridge: MIT Press.
74.
Wooldridge, J.M. ( 2003). Introductory econometrics: A modern approach. Mason, OH: South-Western, Thomson Learning.
75.
Yin, W., Basu, A., Zhang, J.X., Rabbani, A., Meltzer, D.O., & Alexander, G.C. (2008). The effect of the Medicare Part D prescription benefit on drug utilization and expenditures. Annals of Internal Medicine, 148, 169-172.
76.
Young, T.K. ( 2005). Population health: Concepts and methods. New York, NY: Oxford University Press.