# Normally Distributed Data. A mean with a normal value Test of Hypothesis Sign Test Paired observations within a single patient group

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1 ANALYSIS OF CONTINUOUS VARIABLES / 31 CHAPTER SIX ANALYSIS OF CONTINUOUS VARIABLES: COMPARING MEANS In the last chater, we addressed the analysis of discrete variables. Much of the statistical analysis in medical research, however, involves the analysis of continuous variables (such as cardiac outut, blood ressure, and heart rate) which can assume an infinite range of values. As with discrete variables, the statistical analysis of continuous variables requires the alication of secialized tests. In general, these tests comare the means of two (or more) data sets to determine whether the data sets differ significantly from one another. There are four situations in biostatistics where we might wish to comare the means of two or more data sets. Each situation requires a different statistical test deending on whether the data is normally or non-normally distributed about the mean (Figure 6-1). 1. When we wish to comare the observed mean of a data set with a standard or normal value, we use the test of hyothesis or the sign test. 2. When we wish to determine whether the mean in a single atient grou has changed as a result of a treatment or intervention, the single-samle, aired t-test or Wilcoxon signedranks test is aroriate. 3. When we are evaluating the means of two different grous of atients, we use the twosamle, unaired t-test or Wilcoxon rank-sum test. 4. When multile comarisons are required to determine how one theray differs from several others, we emloy analysis of variance (ANOVA). The use of multile comarisons is discussed in the next chater. If we wish to comare: Normally Distributed Data Non-Normally Distributed Data A mean with a normal value Test of Hyothesis Sign Test Paired observations within a single atient grou Single-samle, aired t-test Wilcoxon signed-ranks test Means from two different atient grous Two-samle, unaired t-test Wilcoxon rank-sum test Multile atient grous ANOVA Nonarametric ANOVA COMPARING MEANS Figure 6-1: Analysis of Continuous Variables There are three factors which determine whether an observed samle mean is different from another mean or normal value. First, the larger the difference between the means, the more likely the difference has not occurred by chance. Second, the smaller the variability in the data about the mean, the more likely the observed samle mean reresents the true mean of the oulation-at-large. The standard deviation reresents the variability of the data about the mean. The smaller the standard deviation, the smaller the variability of the data about the mean. Third, the larger the samle size, the more accurately the samle mean will reresent the true oulation mean. The standard error of the mean estimates how closely the samle mean aroximates the true oulation mean. As the samle size increases (and aroaches the size of the oulation), the standard error of the mean aroaches zero.

2 32 / A PRACTICAL GUIDE TO BIOSTATISTICS THE t DISTRIBUTION: ANALYSIS OF NORMALLY DISTRIBUTED DATA The t distribution is a robability distribution which is frequently used to evaluate hyotheses regarding the means of continuous variables. It is commonly referred to as "Student's t-test" after William Gosset, a mathematician with the Guinness Brewery, who in 1908 noted that if one samles a normally distributed bell-shaed oulation, the samle observations will also be normally distributed (assuming the samle size is greater than 30). Unfortunately, comany olicy forbade emloyee ublishing and he was forced to use the seudonym "Student." He named the distribution, "t", and defined a measure of the difference between two means known as the "critical ratio" or "t statistic" which followed the t distribution: t= x- µ x-µ = se sd / n where x = the mean of the samle observations, µ = the mean of the oulation µ, and se = the standard error of the samle mean (which is equal to the samle standard deviation divided by the square root of the samle size, n). The t distribution is similar to the standard normal (z) distribution (discussed in Chater Two) in that it is symmetrically distributed about a mean of zero. Unlike the normal distribution, however, which has a standard deviation of 1, the standard deviation of the t distribution varies with an entity known as the degrees of freedom. Since the t-test lays a rominent role in many statistical calculations, degrees of freedom is an imortant statistical concet. Degrees of freedom is related to samle size and indicates the number of observations in a data set that are free to vary. For examle, if we make n observations and calculate their mean, we are free to change only n - 1 of the observations if the mean is to remain the same as once we have done so, we will automatically know the value of the nth observation. The degrees of freedom for a data set are therefore equal to the samle size (n) - 1. Whereas there is only one standard normal (z) distribution, there is a searate t distribution for each ossible degree of freedom from 1 to. As with the normal distribution, critical values of the t-statistic can be obtained from t distribution tables (found in any statistics textbook) based on the desired significance level (-value) and the degrees of freedom. Aroriate use of the t distribution requires that three assumtions are met. The first assumtion is that the observations follow a normal (gaussian or "bell-shaed") distribution; that is, they are evenly distributed about the true oulation mean. If the observations are not normally distributed, the t-statistic is not accurate and should not be used. As a general rule, if the median differs markedly from the mean, the t-test should not be used. The second assumtion is that the variances (the standard deviations squared) of the two grous being comared, although unknown, are equal. The third assumtion is that the observations occur indeendently (i.e., an observation in one grou does not influence the occurrence of an observation in the other grou). THE t DISTRIBUTION AND CONFIDENCE INTERVALS In Chater Three, we saw that confidence intervals could be calculated for any mean in order to evaluate how confident we were that our samle mean reresented the true oulation mean. In order to calculate the 95% confidence interval for a mean we used the following equation: 95% confidence interval = mean ± aroximately 2 se Remember that this calculated the aroximate confidence interval. In reality, the exact multilying factor for the standard error of the mean deends on the samle size and degrees of freedom. Using the t distribution, we can calculate the exact 95% confidence interval for a articular mean (x) and standard deviation (sd) as follows (the critical value of t is obtained from a t distribution table based on the desired significance level and degrees of freedom resent): sd 95% confidence interval = x ± t se = x ± t n For examle, suose we studied 87 intensive care unit (ICU) atients and found that the mean ICU LOS (length of stay) was 6.7 days with a standard deviation of 19.1 days. If we wish to determine how

3 ANALYSIS OF CONTINUOUS VARIABLES / 33 closely our observed mean ICU LOS aroximates the true mean LOS for all ICU atients with 95% confidence, we would determine the critical value of t for a significance level of 0.05 (5% chance of a Tye I error) and 86 (87-1) degrees of freedom. The critical value of t for these arameters, as obtained from a t distribution table, is Calculating the confidence interval as above we obtain: 95% confidence interval = 6.7 ± = 6.7 ± 4.07 days 87 Therefore, we can be 95% confident that, based on our study data, the interval from 2.63 to days contains the true mean LOS for all ICU atients. ANALYSIS OF NORMALLY DISTRIBUTED CONTINUOUS VARIABLES The t-test is commonly used in statistical analysis. It is an aroriate method for comaring two grous of continuous data which are both normally distributed. The most commonly used forms of the t- test are the test of hyothesis, the single-samle, aired t-test, and the two-samle, unaired t-test. TEST OF HYPOTHESIS Suose we know from revious exerience that the normal mean LOS for all ICU atients in our hosital is 3.2 days and we wish to comare this to our study mean of 6.7 days to determine whether these two means differ significantly. To do so, we would use a form of the t-test known as the test of hyothesis in which we comare a single observed mean with a standard or normal value. For this comarison, the null and alternate hyotheses would be: Null hyothesis: the normal and study ICU LOS are not different Alternate hyothesis: the normal and study ICU LOS are different Using a significance level of 0.05 and n-1 (86) degrees of freedom we noted above that the critical value of t is The t statistic would then be calculated for the test of hyothesis as follows: t= x- sd / µ = n = = where x = the study ICU LOS of 6.7 days, µ = the normal ICU LOS of 3.2 days, sd = the standard deviation of 19.1 days*, and n = 87 * Remember that one of the inherent assumtions in using the t-test is that the samle and oulation have the same variance (and therefore the same standard deviation) Since 1.7 does not exceed the critical value of 1.99, we cannot reject the null hyothesis and must conclude that the means, although different, do not differ significantly. This is consistent with what we found using the 95% confidence interval in which we found that there was a 95% robability that the true oulation mean (3.2 days in this examle) was between 2.63 and days. If the normal ICU LOS in our hosital was actually 2.4 days (instead of 3.2 days), the critical value of t obtained from the above equation would be ( )/2.05 or Since a t-statistic of 2.10 exceeds the critical value of 1.99, we would accet our alternate hyothesis and state that our study ICU LOS was significantly greater than the normal ICU LOS with at least 95% confidence. We would also know that this is true by realizing that an ICU LOS of 2.4 days lies outside of our 95% confidence interval of 2.63 to days. Thus, t-tests and confidence intervals are just two different methods of determining the same statistical information. The broadness of the above 95% confidence interval demonstrates that there is considerable variability in the data. If the samle variability was less (i.e., the standard deviation was smaller), the 95% confidence interval would be narrower and we would be more likely to find that our two means were significantly different. For examle, had out standard deviation been only 5 days, instead of 19.1 days, t would have been 6.53 rather than 1.7. This is clearly greater than the critical value of 1.99 necessary to accet the alternate hyothesis and state that a significant difference exists with at least 95% confidence. Thus, the greater the variability in the samle data, the greater the difficulty in roving that the samle mean is different from a normal value. This also holds true when one is comaring two grous of atients. The more variable the observations in each grou, the harder it is to rove that they are different.

4 34 / A PRACTICAL GUIDE TO BIOSTATISTICS SINGLE-SAMPLE, PAIRED T-TEST If we wish to evaluate an intervention or treatment and have aired observations (such as re- and ost-intervention) on a single grou of atients, we can use a single-samle, aired t-test to determine whether the aired observations are significantly different from one another. In the test of hyothesis, we comared a single mean with a standard value. In calculating the aired t-test, because we are interested in the differences between airs of data, the mean of the differences between the airs relaces the mean of the observations. Otherwise, the calculation of the t-statistic remains the same. Consider the following data on arterial oxygen tension (PaO 2 ) measurements in 10 atients before and after the addition of ositive end-exiratory ressure (PEEP). Our null and alternate hyotheses would be as follows: Null hyothesis: there is no change in PaO 2 with the addition of PEEP. Alternate hyothesis: there is a change in PaO 2 with the addition of PEEP. Patient Pre-PEEP PaO 2 (torr) Post-PEEP PaO 2 (torr) Difference mean sd Using a significance level of 0.05 and n-1 (9) degrees of freedom, the critical value of t, obtained from a t distribution table, is The t statistic for the single-samle, aired t-test is then calculated as follows: mean of the differences t = 16.3 standard error of the mean of the differences = = 10.9 Since 10.9 is greater than the critical value of 2.26 required to identify a difference with a significance level of 0.05 (95% confidence of not committing a Tye I error), we would reject the null hyothesis of no difference and state that the addition of PEEP significantly increases PaO 2. Since our calculated critical value of 10.9 markedly exceeds 2.26, the actual significance level (or -value) is really much smaller than In fact, the actual significance level associated with a critical value of 10.9 and 9 degrees of freedom is <

5 TWO SAMPLE, UNPAIRED T-TEST ANALYSIS OF CONTINUOUS VARIABLES / 35 One of the most common uses of the t-test is to comare observations from two searate grous of atients to determine whether the two grous are significantly different with resect to a articular variable of interest. To make such a comarison, we use a two-samle, unaired t-test. As with the other forms of the t-test, it is assumed that the data are normally distributed. Consider the following data on ICU charges for 10 age and rocedure matched atients from the Preoerative Evaluation study. We wish to determine whether erforming a reoerative evaluation on the atient (lacement of a ulmonary artery catheter the night before surgery) results in higher ICU charges than no reoerative evaluation (ulmonary artery catheter lacement in the oerating room rior to surgery). Note that the mean and median of each grou is similar, and that the variances are also similar. The first two assumtions of the t-test are therefore met and the use of a t-test to analyze this data is aroriate. Study Patients Control Patients \$5,199 \$21,929 \$25,451 \$8,065 \$9,774 \$5,832 \$3,995 \$28,213 \$14,226 \$16,039 \$30,415 \$1,995 \$3,919 \$20,877 \$23,936 \$25,374 \$20,029 \$2,544 \$15,533 \$15,257 mean \$14,948 \$14,613 median \$13,380 \$15,648 sd \$9,580 \$9,550 variance \$91,776,400 \$91,202,500 Null hyothesis: reoerative evaluation does not affect ICU charges Alternate hyothesis: reoerative evaluation either increases or decreases ICU charges. The degrees of freedom in a two-samle t-test are calculated as (n 1 + n 2-2) since we are free to vary only n - 1 of the observations in each of the two grous. Assuming a significance level of 0.05 and 18 degrees of freedom ( ), the critical value of t is 2.1. The t statistic for a two-samle, unaired t-test is given by the following modification of the t-test equation: x1 x2 t = s 1/n 1/n r 1+ 2 where s r = the ooled standard deviation for both grous which is calculated as follows: 2 ( n 1)( sd ) ( n 1)( sd ) s r = n + n 2 Thus, we calculate the t statistic for the above data as: 1 2 ( 9)( ) ( 9)( 9550) s r = = 9565 ( ) 335 t = = = Since t does not exceed the critical value of 2.1 necessary to detect a significant difference with 95% confidence, we must accet the null hyothesis and state that erforming a reoerative evaluation rior to the atient s oeration does not result in a significant difference in ICU charges. Note that in the examle above, our alternate hyothesis determined that we needed to erform a two-tailed test. We do

6 36 / A PRACTICAL GUIDE TO BIOSTATISTICS not know a riori whether reoerative evaluation will either increase or decrease ICU charges and thus need to investigate both ossibilities. If we were only interested in identifying whether reoerative evaluation increased ICU charges, our alternate hyothesis would reflect that and be stated as: Alternate hyothesis: reoerative evaluation increases ICU charges We would therefore only need to erform a one-tailed test as we would only be interested in identifying differences in one direction. Remember that a two-tailed test requires more statistical evidence to detect a significant difference than does a one-tailed test because the two-tailed test must look in both directions (i.e., are charges increased? and are charges decreased?). The critical value of t reflects this difference in that the critical value necessary to detect a difference for a one-tailed test is smaller than that for a twotailed test. Also remember that if we only erform a one-tailed test (i.e., reoerative evaluation increases ICU charges), but reoerative evaluation actually decreases ICU charges, we will commit a Tye II error because our alternate hyothesis will not account for this ossibility. We will thus be forced to accet the null hyothesis and conclude that there is no difference when, in fact, there really is. In common ractice, therefore, most researchers erform the statistically more strenous two-tailed test in order to be sure of detecting any difference that may be resent and avoiding Tye II errors. TRANSFORMATION OF NON-NORMALLY DISTRIBUTED DATA As we have noted, one of the imortant assumtions in the use of t-tests is that the data is normally distributed. This is not always the case, however. Data may inherently not be normally distributed about the mean, or significant outliers may be resent which skew the data in one direction or another. If this is true, the use of t-tests is inaroriate. In the situation in which the observations in a data set do not follow a normal distribution there are two otions: we can either "transform" the data to a scale which is normally distributed, or we can use non-arametric methods of analysis which do not assume a normal distribution. There are several methods for transforming non-normally distributed data to a normal distribution. Logarithmic transformations involve calculating the logarithm or using the square root of each data oint to create a transformed data set. This will frequently minimize the effect of outlying data oints and convert the data to a more normal distribution. Statistical tests utilizing the t distribution can then be used on the transformed, and now normally distributed, data to evaluate the observations. Rank transformations involve ordering the observations from smallest to largest and assigning a rank to each observation from 1 to n beginning with the smallest observation. Rank-ordering of data is the basis of the non-arametric statistical methods. One otential roblem with analyzing data through transformation is that the results of the statistical analysis actually refer to the transformed data and not the original data. In most situations, this oint is not imortant. It may, however, make interretation of the results difficult as the units of transformed data will be exressed in terms of the logarithm or square root that was used in the transformation. Further, the transformed data may lead to conclusions which are not suorted by the original, non-transformed data and vice versa. Because of these otential roblems with transformation of data, the more commonly used alternative in analyzing non-normally distributed data is to use non-arametric methods of analysis.

7 NON-PARAMETRIC METHODS OF ANALYSIS ANALYSIS OF CONTINUOUS VARIABLES / 37 In the resence of non-normally distributed data, the alternative to transformation is to use nonarametric methods. These tests, unlike those which use the t distribution, do not assume that the data are normally distributed. They are statistically stronger than t-tests in the resence of skewed data, but are weaker than t-tests in the resence of normally distributed data. The most commonly used nonarametric methods are the sign test, the Wilcoxon signed-ranks test, and the Wilcoxon rank-sum test (Mann-Whitney U test). THE SIGN TEST If we wish to comare a single non-normally distributed mean with a known standard or normal value, the sign test is a useful method (note that if the data were normally distributed, we would use the test of hyothesis in this situation). The sign test is based on the fact that if the observations were normally distributed and were not different from the standard or normal value, their median would closely aroximate the standard or normal value (the median of the oulation-at-large). Given this, there would be a robability of 0.50 that any observation was either greater than or less than the standard value. If the observed values differ from the standard value significantly (and therefore do not reresent the oulation-at-large), the robability will be skewed to one side or the other of To erform the sign test, we must first comare each of our n observations with the standard or normal value (the oulation median) and sum the number of comarisons (N) in which the observation exceeds the normal value. The robability of such an occurrence is given by: N n N ( 0.5) ( 0.5) robability = ( n-n )! For examle, what is the robability of 10 ICU atients all having an ICU LOS greater than the normal LOS of 2.7 days (i.e., N=10)? The robability of this event is obtained by: ( 0.5) ( 0.5) robability = ( )! ( )( 1) = = ( 1) The actual robability of such an occurrence is therefore 1 in As with Fisher's exact test, all of the more extreme cases must also be calculated, when aroriate, and each of the resultant robabilities summed to obtain the total robability of occurrence. Note that this is an examle of a onetailed test; we are asking whether a mean is greater than the known value. If we wanted to test whether the ICU LOS for the same 10 atients was either less than or greater than the normal value, we would want to erform a two-tailed test. For the sign test, the two-tailed robability is given by doubling the one tailed robability. The sign test can also be used to evaluate aired sets of data. Given any two values, there is a robability of 0.50 that the difference between the values is ositive. Similarly, there is a robability of 0.50 that the difference is negative. Consider the following data from the Preoerative Evaluation study comaring ICU LOS for reoerative evaluation (study) atients and for age and rocedure matched atients (control atients) who did not undergo reoerative evaluation. The data means and medians are not similar and are therefore not normally distributed being skewed by atient air 6. To aroriately analyze this data, a non-arametric method such as the sign test must be used. Our null and alternate hyotheses would be as follows: Null hyothesis: the ICU LOS for study and control atients is the same. Alternate hyothesis: the ICU LOS for study atients and control atients are different.

8 38 / A PRACTICAL GUIDE TO BIOSTATISTICS Patient Pairs Study Patient ICU LOS Control Patient ICU LOS Difference Given 10 airs of age and rocedure-matched atients, 8 sets of aired data had a ositive difference while 2 sets had a negative difference. Using the sign test, and calculating each of the more extreme ossible outcomes, the total robability of this occurring is: P total ( 0.5) ( 0.5) ( 0.5) ( 0.5) ( 0.5) ( 0.5) = + + 2! 1! Ptotal = = Since we did not know a riori whether the two grous differed and in which direction, we need to erform a two-tailed test. Thus, the robability that the two grous have the same mean ICU LOS is (0.0025)(2) = or 1 in 200. We therefore reject the null hyothesis and accet our alternate hyothesis stating that the grous differ significantly with a robability of WILCOXON SIGNED RANKS TEST When we are evaluating a single grou of atients and wish to evaluate the difference between two aired samles which are non-normally distributed, the Wilcoxon signed-ranks test is most commonly used. This is the non-arametric equivalent of the single samle, aired t-test. The Wilcoxon-signed ranks test evaluates the hyothesis that the medians of the two sets of observations are the same. It is almost as owerful as the t-test when the observations are normally distributed and is more owerful than the t-test when the observations are not. Consider the following data on right ventricular end-diastolic volume index (RVEDVI) before and after atients received a 500 ml normal saline infusion. If the data were normally distributed, we would use the single-samle, aired t-test. Uon insection of the raw data, however, it is obvious that the data are not normally distributed and contain several outliers (articularly atients 3 and 7) which skew the data. To confirm this, we can comare the mean and median of each samle. Since the mean and median are not similar, the data are non-normally distributed and a t-test is inaroriate. We would therefore use the Wilcoxon signed-ranks test to comare the means of the re- and ost-infusion data. 0! 0

9 ANALYSIS OF CONTINUOUS VARIABLES / 39 RVEDVI RVEDVI Signed Patient Pre-bolus Post-bolus Difference Rank Rank mean median sd To erform the Wilcoxon signed-ranks test, we first calculate the difference between each air of observations. These differences are then ordered from smallest to largest (ignoring the sign) and ranked from 1 to n beginning with the smallest observation. The sign of the difference (either ositive or negative) is then assigned to that difference s rank. We then use the signed ranks of the observations to calculate the t statistic. The mean and median of the ranks are both 4.5. The ranked data are now normally distributed and use of the t-test is aroriate whereas it would not have been on the original, nonnormally distributed data. Assuming we wish to detect a difference with 95% confidence (significance level of 0.05) and have 9 degrees of freedom (10-1 airs of data), the critical value of t is The t statistic is then calculated on the ranks as follows: mean of ranks 4.5 t = = = 3.2 standard error of the mean of ranks Since our calculated t exceeds the critical value of t (2.26) for a significance level of 0.05, we would reject the null hyothesis and accet the alternate hyothesis, concluding that a 500 ml normal saline infusion does result in a significant increase in RVEDVI with a significance level of < WILCOXON RANK-SUM TEST (MANN-WHITNEY U TEST) If we are comaring the means of two indeendent grous, and the data are non-normally distributed, the Wilcoxon rank-sum test (also known as the Mann-Whitney U test) is aroriate. This is the nonarametric equivalent of the two-samle, unaired t-test. The Wilcoxon rank-sum test analyzes the equality of the samle medians rather than the means (as is done in the two-samle, unaired t-test). It is similar to the Wilcoxon signed-ranks test for aired data in that the raw observations are ranked from smallest to largest and the mean ranks are then comared using the t statistic for two indeendent samles (two-samle, unaired t-test). The difference in the ranking for this test is that the observations of both grous are considered as one grou for the urose of assigning the ranks. The theory of the test is that if the two samles are similar, their medians will also be similar and the mean ranks will be equal. If one mean rank is larger, then that samle must have larger observations (and therefore a larger median) than the other. The Wilcoxon rank-sum test then quantitates how different the two mean ranks are using the t-statistic. Consider the following data on total hosital charges and ICU LOS (length of stay) for age and rocedure matched atients from the Preoerative Evaluation study. The data are non-normally distributed containing several outliers (atient airs 4 and 6) which skew the data (note the differences between the mean and median for each grou). To use the Wilcoxon rank-sum test, the data from both grous are combined and ranked from smallest to largest. Thus, all 20 of the atient charges have been considered as a grou and ranked from 1 to 20 (see below). Similarly, all of the LOS measurements have been considered as a grou and ranked from 1 to 20. Note that for the LOS measurements, some of the observations have the same value. In this situation, the ranking is erformed by using the mean rank for each of the ties. For examle,

10 40 / A PRACTICAL GUIDE TO BIOSTATISTICS four atients had a LOS of 1 day. Normally, these atients would receive the ranks 1, 2, 3, and 4. Since we must treat each observation equally, each of the four atients with LOS of 1 day is assigned the average rank of 2.5 [( )/4=2.5]. Similarly, there are four atients with a LOS of 2 days. These atients would normally receive the ranks 5, 6, 7, and 8. We assign each of these atients the average rank of 6.5 [( )/4=6.5]. Patient Study Pt Rank Control Pt Rank Study Pt Rank Control Rank Pairs Charges Charges LOS Pt LOS 1 \$28,004 8 \$19, \$44, \$39, \$23,727 5 \$22, \$330, \$26, \$11,172 1 \$18, \$838, \$36, \$28,334 9 \$101, \$53, \$70, \$34, \$26, \$57, \$49, mean \$145, \$41, median \$39,607 \$31, sd \$261, \$31, We will first comare the atient charges using the Wilcoxon rank-sum test. To calculate the t statistic, we must first calculate the ooled standard deviation of the ranks which is calculated as in the two-samle unaired t-test, but using the standard deviations of the ranks: ( )( 6.1) ( )( ) s r = = 5.95 The degrees of freedom in this case are calculated by (n + n 1 2-2). For a significance level of 0.05 and 18 degrees of freedom ( ), the critical value of t is 2.1. The t statistic for the Wilcoxon rank-sum test is calculated as follows: rank1 rank t = = = 0.83 s 1/n + 1/n r 1 2 ( )( ) Based on this, we cannot reject our null hyothesis of no difference as the calculated value of t does not exceed the critical value of 2.1. The mean hosital charges are therefore not significantly different between the grous desite the difference of over \$100,000 between the means. The reason that this is true is related to the large variability that exists in the charges as noted by the large standard deviations. The large variability in the data makes it difficult to rove that a difference exists between the grous. We now can comare the LOS measurements, again using the Wilcoxon rank-sum test. Using the same significance level (0.05) and degrees of freedom (18), the critical value of t for this comarison is still 2.1. The t statistic is calculated as follows: ( )( 5.0) ( )( ) s r = t = ( 5.05)( 0.447) = 2.26 = 5.05 As t exceeds the critical value of 2.1, we reject the null hyothesis of no difference and state that the mean ICU LOS of reoerative evaluation atients is significantly greater than that of control atients with a significance level of < Interestingly, if we use the two samle, unaired t-test to analyze this data, the null hyothesis is not rejected and the significant difference is missed (a Tye II error). This is because of the non-normal distribution of the data and the inequality of the samle variances, both of which violate the basic

11 ANALYSIS OF CONTINUOUS VARIABLES / 41 assumtions of the t-test, making it inaccurate and leading to a false conclusion. This emhasizes the imortance of erforming the aroriate statistical test if valid conclusions are to be made. The Wilcoxon rank-sum test is esecially useful in evaluating data from ordered categories (such as tumor staging classifications) which have non-numerical scores. For examle, how would we comare two treatments whose outcome is documented as either "worse", "unchanged", or "imroved? One otion is to comress the data into two categories (such as "imroved" and "not imroved") so that we can use a chi-square test or Fisher s exact test to comare the grous. By doing so, however, we sacrifice data (reducing our observations to two categories instead of three categories) and gain less information from the study. If we aly ranks to the outcome categories, however, and evaluate these ranks with the Wilcoxon rank-sum test, no data is sacrificed and we use the full otential of the data collected. SUGGESTED READING 1. Dawson-Saunders B, Tra RG. Basic and clinical biostatistics (2nd Ed). Norwalk, CT: Aleton and Lange, Wassertheil-Smoller S. Biostatistics and eidemiology: a rimer for health rofessionals. New York: Sringer-Verlag, Cambell MJ, Machin D. Medical statistics: a commonsense aroach. Chichester: John Wiley and Sons Ltd, Moses LE, Emerson JD, Hosseini H. Analyzing data from ordered categories. NEJM 1984; 311: O'Brien PC, Shamo MA. Statistical considerations for erforming multile tests in a single exeriment. 4. erforming multile statistical tests on the same data. Mayo Clin Proc 1988; 63: O'Brien PC, Shamo MA. 5. One samle of aired observations (aired t-test). Mayo Clin Proc 1981; 56: O'Brien PC, Shamo MA. 6. Comaring two samles (the two-samle t-test). Mayo Clin Proc 1981; 56: Godfrey K. Comaring the means of several grous. NEJM 1985; 313: Conover WJ, Iman RL. Rank transformations as a bridge between arametric and nonarametric statistics. Am Stat 1981;35:

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