Describing data requires no adjustment for multiple comparisons: A reply from savitz and olshan

American Journal of Epidemiology

Volumn 147, Issue 9, 1998, Pages 813-814

  Savitz, David A ^a   Olshan, Andrew F ^a  

^a IL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPARATIVE STUDY; EPIDEMIOLOGY; HUMAN; METHODOLOGY; NOTE; STANDARD; STATISTICAL ANALYSIS; STATISTICS;

DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGY; HUMANS; RESEARCH DESIGN;

EID: 0032068055     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/oxfordjournals.aje.a009532     Document Type: Note

References (10)

1. Savitz DA, Olshan AF. Multiple comparisons and related issues in the interpretation of epidemiologic data. Am J Epidemiol 1995;142:904-8
2. Thompson J. Invited commentary: Re: "Multiple comparisons and related issues in the interpretation of epidemiologic data." Am J Epidemiol 1997; 147:801-6
3. Greenland S. Summarization, smoothing, and inference in epidemiologic analysis. Scand J Soc Med 1993;21: 227-32
4. Poole C. Confidence intervals exclude nothing. Am J Public Health 1987;77:492-3
5. Greenland S. Randomization, statistics, and causal inference. Epidemiology 1990;l:421-9
6. Rothman KJ. Modern epidemiology. Boston, MA: Little, Brown and Company, 1986
7. Goodman SN. A comment on replication, p values and evidence. StatMed 1992; 11:875-9
8. Goodman SN. Multiple comparisons, explained. Am J Epidemiol 1997; 147:807-12
9. Greenland S, Robins JM. Empirical-Bayes adjustments for multiple comparisons are sometimes useful. Epidemiology 1991;2:244-51
10. Greenland S, Poole C. Empirical-Bayes and semi-Bayes approaches to occupational and environmental hazard surveillance. Arch Environ Health 1994;49:9-16