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Society for Academic Emergency Medicine call for abstracts
BIOSTATISTICS Gaddis & Gaddis
e m e r g e n c y p h y s i c i a n s . Its shape is s y m m e t r i c a l and bell-shaped, and is defined as the " n o r m a l distribution." M a n y h u m a n a n a t o m i c and physiologic c h a r a c t e r i s t i c s approach the normal distribution.6 Other terminology used synonymously with "normal distribution" includes G a u s s i a n curve, curve of error, and n o r m a l probability curve. A n understanding of the characteristics of the n o r m a l d i s t r i b u t i o n is f u n d a m e n t a l i n the d e v e l o p m e n t of even a basic l~nowledge of biostatistics. This topic will be discussed in greater detail in Part 2 of this series. Other distributions are depicted in Figure 2. 6 Bimodal distributions have two peaks of cluster, or areas w i t h a high frequency level. For example, if the weights of A m e r i c a n adults were plotted, there w o u l d be two definitive points of cluster, one for female weight and one for male weight. Data that are rectangularly distributed show equal frequency of occurrence for all levels of a characteristic. The date of birth (month and day) of a sample of all p a t i e n t s seen i n an e m e r g e n c y d e p a r t m e n t approaches this distribution pattern. Skewed data are those that tail off to either the high or low end of meas u r e m e n t units. The a n n u a l i n c o m e of patients seen i n the ED of an inner-city c o m m u n i t y hospital will be defined by a distribution that is posi t i v e l y skewed, s h o w i n g a high frequency of lower a n n u a l incomes. A negatively skewed distribution has a cluster of data on the high end of the u n i t scale and tails off toward the low end. 6 G i v e n the nature of data collected in medical science research, the norm a l d i s t r i b u t i o n is t h e o n e w i t h which the clinician will be most familiar. Most statistical methods ap-
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plied to interval or ratio data assume t h a t the data are n o r m a l l y distributed. It is w h e n this a s s u m p t i o n is violated that significant controversy exists i n the statistical c o m m u n i t y regarding t h e proper a p p l i c a t i o n of statistical tests.
PARAMETRIC VERSUS NONPARAMETRIC METHODS Statistical methods are defined as being p a r a m e t r i c or n o n p a r a m e t r i c . T h e type of a n a l y s i s m e t h o d t h a t should be selected is d e p e n d e n t on the nature of the data to be analyzed. Parametric methods use data extrapolated from a sample of the population studied to n u m e r i c a l l y describe some characteristic of a population. P a r a m e t r i c m e t h o d s are v a l i d o n l y w h e n t h a t characteristic follows or nearly follows the n o r m a l distribut i o n i n t h e p o p u l a t i o n s t u d i e d . 12 Thus, parametric methods can be applied properly to m o s t i n t e r v a l and r a t i o scale data w h e n t h o s e data come from a sample of a n o r m a l l y distributed population. Parametric methods can be used to derive measures of c e n t r a l t e n d e n c y and variability, w h i c h will be discussed i n Part 2 of the series. N o n p a r a m e t r i c m e t h o d s are app l i e d to n o n - n o r m a l l y d i s t r i b u t e d data and/or data that do not meet the c r i t e r i a for t h e u s e of p a r a m e t r i c m e t h o d s . D a t a t h a t fit the o r d i n a l scale d e f i n i t i o n should be analyzed by n o n p a r a m e t r i c m e t h o d s . Examples i n c l u d e Glasgow C o m a Scale, 8 T r a u m a Score, 9 the Injury Severity Score, lo a n d o t h e r s i m i l a r o r d i n a l scales data.
SUMMARY This has been the introductory ins t a l l m e n t of a series of articles outlining the proper use of biostatistics.
Annals of Emergency Medicine
We have introduced some basic concepts regarding data and its classification, w h i c h will be needed for understanding of topics to be presented s u b s e q u e n t l y (Figure 3). Measures of central tendency, m e a s u r e s of variability, confidence intervals, and the a p p r o p r i a t e use of t h e s e s t a t i s t i c a l concepts will be discussed i n part 2 of this series.
REFERENCES 1. Glantz SA: Biostatistics: How to detect, correct, and prevent errors in the medical literature. Circulation 1980;61:1-7. 2. FelsonDT, Cupples LA, Meenan RF: Misuse of statistical methods in arthritis and rheumatism: 1982 versus 1967-68. Arthritis R h e u m 1984;27:1018-1022. 3. Thorn MD, Pulliam CC, Symons MJ, et al: Statistical and research quality of the medical and pharmacy literature. A m J Hosp Pharm 1985;42:1077-1082. 4. Avram MJ, Shanks CA, Dykes MHM, et ah Statistical methods in anesthesia articles: An evaluationof two Americanjournalsduringtwo six-month periods. A n e s t h Analg 1985;64: 607-611. 5. MacArthur RD, Jackson GG: An evaluation of the use of statistical methodologyin the Journal of Infectious Diseases. J Infect Dis 1984; 149:349~354. 6. Hopkins KD, Glass GV: Basic Statistics for the Behavioral Sciences. EnglewoodCliffs, New Jersey, Prentice-HallInc, 1978, 7. Elenbaas RM, ElenbaasJK, Cuddy PG: Evaluating the medical literature. Part II: Statistical analysis. Ann Emerg Med 1983;12:610-620. 8. Teasdale G, Jennett B: Assessment of coma and impaired consciousness:A practical scale. Lancet 1974;2:81-84. 9. Champion HR, 8acco WJ, Carnazzo AJ, et al: Trauma score. Crit Care Med 1981;9:672-676. 10. Baker SP, O'Neill B, HaddonW Jr, et al: The Injury Severity Score: A method for describing patients with multiple injuries and evaluating emergency care. I Trauma 1974;14:187q96. ll. Sokal RR, Rohlf FJ: Biometry, ed 2. New York, WH Freeman and Co, 198!, pp 39-52. 12. Glantz SA: Primer of Biostatistics, ed 2. New York, McGraw-HillBook Co, 1987.
19:1 January 1990
e m e r g e n c y p h y s i c i a n s . Its shape is s y m m e t r i c a l and bell-shaped, and is defined as the " n o r m a l distribution." M a n y h u m a n a n a t o m i c and physiologic c h a r a c t e r i s t i c s approach the normal distribution.6 Other terminology used synonymously with "normal distribution" includes G a u s s i a n curve, curve of error, and n o r m a l probability curve. A n understanding of the characteristics of the n o r m a l d i s t r i b u t i o n is f u n d a m e n t a l i n the d e v e l o p m e n t of even a basic l~nowledge of biostatistics. This topic will be discussed in greater detail in Part 2 of this series. Other distributions are depicted in Figure 2. 6 Bimodal distributions have two peaks of cluster, or areas w i t h a high frequency level. For example, if the weights of A m e r i c a n adults were plotted, there w o u l d be two definitive points of cluster, one for female weight and one for male weight. Data that are rectangularly distributed show equal frequency of occurrence for all levels of a characteristic. The date of birth (month and day) of a sample of all p a t i e n t s seen i n an e m e r g e n c y d e p a r t m e n t approaches this distribution pattern. Skewed data are those that tail off to either the high or low end of meas u r e m e n t units. The a n n u a l i n c o m e of patients seen i n the ED of an inner-city c o m m u n i t y hospital will be defined by a distribution that is posi t i v e l y skewed, s h o w i n g a high frequency of lower a n n u a l incomes. A negatively skewed distribution has a cluster of data on the high end of the u n i t scale and tails off toward the low end. 6 G i v e n the nature of data collected in medical science research, the norm a l d i s t r i b u t i o n is t h e o n e w i t h which the clinician will be most familiar. Most statistical methods ap-
146/89
plied to interval or ratio data assume t h a t the data are n o r m a l l y distributed. It is w h e n this a s s u m p t i o n is violated that significant controversy exists i n the statistical c o m m u n i t y regarding t h e proper a p p l i c a t i o n of statistical tests.
PARAMETRIC VERSUS NONPARAMETRIC METHODS Statistical methods are defined as being p a r a m e t r i c or n o n p a r a m e t r i c . T h e type of a n a l y s i s m e t h o d t h a t should be selected is d e p e n d e n t on the nature of the data to be analyzed. Parametric methods use data extrapolated from a sample of the population studied to n u m e r i c a l l y describe some characteristic of a population. P a r a m e t r i c m e t h o d s are v a l i d o n l y w h e n t h a t characteristic follows or nearly follows the n o r m a l distribut i o n i n t h e p o p u l a t i o n s t u d i e d . 12 Thus, parametric methods can be applied properly to m o s t i n t e r v a l and r a t i o scale data w h e n t h o s e data come from a sample of a n o r m a l l y distributed population. Parametric methods can be used to derive measures of c e n t r a l t e n d e n c y and variability, w h i c h will be discussed i n Part 2 of the series. N o n p a r a m e t r i c m e t h o d s are app l i e d to n o n - n o r m a l l y d i s t r i b u t e d data and/or data that do not meet the c r i t e r i a for t h e u s e of p a r a m e t r i c m e t h o d s . D a t a t h a t fit the o r d i n a l scale d e f i n i t i o n should be analyzed by n o n p a r a m e t r i c m e t h o d s . Examples i n c l u d e Glasgow C o m a Scale, 8 T r a u m a Score, 9 the Injury Severity Score, lo a n d o t h e r s i m i l a r o r d i n a l scales data.
SUMMARY This has been the introductory ins t a l l m e n t of a series of articles outlining the proper use of biostatistics.
Annals of Emergency Medicine
We have introduced some basic concepts regarding data and its classification, w h i c h will be needed for understanding of topics to be presented s u b s e q u e n t l y (Figure 3). Measures of central tendency, m e a s u r e s of variability, confidence intervals, and the a p p r o p r i a t e use of t h e s e s t a t i s t i c a l concepts will be discussed i n part 2 of this series.
REFERENCES 1. Glantz SA: Biostatistics: How to detect, correct, and prevent errors in the medical literature. Circulation 1980;61:1-7. 2. FelsonDT, Cupples LA, Meenan RF: Misuse of statistical methods in arthritis and rheumatism: 1982 versus 1967-68. Arthritis R h e u m 1984;27:1018-1022. 3. Thorn MD, Pulliam CC, Symons MJ, et al: Statistical and research quality of the medical and pharmacy literature. A m J Hosp Pharm 1985;42:1077-1082. 4. Avram MJ, Shanks CA, Dykes MHM, et ah Statistical methods in anesthesia articles: An evaluationof two Americanjournalsduringtwo six-month periods. A n e s t h Analg 1985;64: 607-611. 5. MacArthur RD, Jackson GG: An evaluation of the use of statistical methodologyin the Journal of Infectious Diseases. J Infect Dis 1984; 149:349~354. 6. Hopkins KD, Glass GV: Basic Statistics for the Behavioral Sciences. EnglewoodCliffs, New Jersey, Prentice-HallInc, 1978, 7. Elenbaas RM, ElenbaasJK, Cuddy PG: Evaluating the medical literature. Part II: Statistical analysis. Ann Emerg Med 1983;12:610-620. 8. Teasdale G, Jennett B: Assessment of coma and impaired consciousness:A practical scale. Lancet 1974;2:81-84. 9. Champion HR, 8acco WJ, Carnazzo AJ, et al: Trauma score. Crit Care Med 1981;9:672-676. 10. Baker SP, O'Neill B, HaddonW Jr, et al: The Injury Severity Score: A method for describing patients with multiple injuries and evaluating emergency care. I Trauma 1974;14:187q96. ll. Sokal RR, Rohlf FJ: Biometry, ed 2. New York, WH Freeman and Co, 198!, pp 39-52. 12. Glantz SA: Primer of Biostatistics, ed 2. New York, McGraw-HillBook Co, 1987.
19:1 January 1990