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Statistical tests in gaze

Loading package

library(autoReg)
library(dplyr) # for use of pipe operator `%>%`

Statistical tests for numeric variables

The gaze() function in this autoReg package perform statistical tests for compare means between/among groups. The acs data included in moonBook package is a dataset containing demographic and laboratory data of 857 patients with acute coronary syndrome(ACS).

To make a table comparing baseline characteristics, use gaze() function.

data(acs, package="moonBook")
gaze(sex~.,data=acs)
————————————————————————————————————————————————————————————————————————
  Dependent:sex        levels           Female          Male        p   
       (N)                             (N=287)        (N=570)           
————————————————————————————————————————————————————————————————————————
age               Mean ± SD             68.7 ± 10.7   60.6 ± 11.2  <.001 
cardiogenicShock  No                    275 (95.8%)     530 (93%)   .136 
                  Yes                     12 (4.2%)       40 (7%)        
entry             Femoral               119 (41.5%)   193 (33.9%)   .035 
                  Radial                168 (58.5%)   377 (66.1%)        
Dx                NSTEMI                 50 (17.4%)   103 (18.1%)   .012 
                  STEMI                  84 (29.3%)   220 (38.6%)        
                  Unstable Angina       153 (53.3%)   247 (43.3%)        
EF                Mean ± SD             56.3 ± 10.1    55.6 ± 9.4   .387 
height            Mean ± SD             153.8 ± 6.2   167.9 ± 6.1  <.001 
weight            Mean ± SD              57.2 ± 9.3   68.7 ± 10.3  <.001 
BMI               Mean ± SD              24.2 ± 3.6    24.3 ± 3.2   .611 
obesity           No                    194 (67.6%)   373 (65.4%)   .580 
                  Yes                    93 (32.4%)   197 (34.6%)        
TC                Mean ± SD            188.9 ± 51.1  183.3 ± 45.9   .124 
LDLC              Mean ± SD            117.8 ± 41.2  116.0 ± 41.1   .561 
HDLC              Mean ± SD             39.0 ± 11.5   37.8 ± 10.9   .145 
TG                Mean ± SD            119.9 ± 76.2  127.9 ± 97.3   .195 
DM                No                    173 (60.3%)   380 (66.7%)   .077 
                  Yes                   114 (39.7%)   190 (33.3%)        
HBP               No                     83 (28.9%)   273 (47.9%)  <.001 
                  Yes                   204 (71.1%)   297 (52.1%)        
smoking           Ex-smoker              49 (17.1%)   155 (27.2%)  <.001 
                  Never                 209 (72.8%)   123 (21.6%)        
                  Smoker                 29 (10.1%)   292 (51.2%)        
————————————————————————————————————————————————————————————————————————

You can make a publication-ready table with myft() function which can be used in HTML, pdf, microsoft word and powerpoint file.

gaze(sex~.,data=acs) %>% myft()

name

levels

Female (N=287)

Male (N=570)

p

age

Mean ± SD

68.7 ± 10.7

60.6 ± 11.2

<.001

cardiogenicShock

No

275 (95.8%)

530 (93%)

.136

Yes

12 (4.2%)

40 (7%)

entry

Femoral

119 (41.5%)

193 (33.9%)

.035

Radial

168 (58.5%)

377 (66.1%)

Dx

NSTEMI

50 (17.4%)

103 (18.1%)

.012

STEMI

84 (29.3%)

220 (38.6%)

Unstable Angina

153 (53.3%)

247 (43.3%)

EF

Mean ± SD

56.3 ± 10.1

55.6 ± 9.4

.387

height

Mean ± SD

153.8 ± 6.2

167.9 ± 6.1

<.001

weight

Mean ± SD

57.2 ± 9.3

68.7 ± 10.3

<.001

BMI

Mean ± SD

24.2 ± 3.6

24.3 ± 3.2

.611

obesity

No

194 (67.6%)

373 (65.4%)

.580

Yes

93 (32.4%)

197 (34.6%)

TC

Mean ± SD

188.9 ± 51.1

183.3 ± 45.9

.124

LDLC

Mean ± SD

117.8 ± 41.2

116.0 ± 41.1

.561

HDLC

Mean ± SD

39.0 ± 11.5

37.8 ± 10.9

.145

TG

Mean ± SD

119.9 ± 76.2

127.9 ± 97.3

.195

DM

No

173 (60.3%)

380 (66.7%)

.077

Yes

114 (39.7%)

190 (33.3%)

HBP

No

83 (28.9%)

273 (47.9%)

<.001

Yes

204 (71.1%)

297 (52.1%)

smoking

Ex-smoker

49 (17.1%)

155 (27.2%)

<.001

Never

209 (72.8%)

123 (21.6%)

Smoker

29 (10.1%)

292 (51.2%)

You can select the statistical method comparing means between/among groups with argument method. Possible values in methods are:

Default value is 1.

1. Comparison of two groups

Ejection fraction(EF) refers to how well your left ventricle (or right ventricle) pumps blood with each heart beat. The normal values are approximately 56-78%.

(1) Parametric method

gaze(sex~EF,data=acs)  # default: method=1 
——————————————————————————————————————————————————————————————
 Dependent:sex   levels        Female          Male       p   
      (N)                     (N=287)        (N=570)          
——————————————————————————————————————————————————————————————
EF             Mean ± SD       56.3 ± 10.1    55.6 ± 9.4  .387 
——————————————————————————————————————————————————————————————

If you want to compare EF means between males and females in acs data with parametric method, you have to compare the variances of two samples. If the variances of two groups are equal, the pooled variance is used to estimate the variance. Otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.

var.test(EF~sex,data=acs)  # F Test to Compare Two Variances

    F test to compare two variances

data:  EF by sex
F = 1.144, num df = 239, denom df = 482, p-value = 0.2214
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
 0.9221264 1.4309581
sample estimates:
ratio of variances 
          1.143983

The result of var.test is not significant. So we cannot reject the null hypothesis :\(H_0 : true\ ratio\ of\ variance\ is\ equal\ to\ 0\). With this result, we perform t-test using pooled variance.

t.test(EF~sex,data=acs,var.equal=TRUE)

    Two Sample t-test

data:  EF by sex
t = 0.86514, df = 721, p-value = 0.3872
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
 -0.8346856  2.1498875
sample estimates:
mean in group Female   mean in group Male 
            56.27375             55.61615

The result of t.test is not significant(\(p=.387\)). The p value in the table is the result of this test. Alternatively, if the result of var.test() is significant, we perform t.test with the Welch approximation to the degrees of freedom.

t.test(EF~sex,data=acs) # default value: var.equal=FALSE

    Welch Two Sample t-test

data:  EF by sex
t = 0.8458, df = 449.65, p-value = 0.3981
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
 -0.8703566  2.1855585
sample estimates:
mean in group Female   mean in group Male 
            56.27375             55.61615

(2) Non-parametric method

gaze(sex~EF,data=acs, method=2)  # method=2 forces analysis as continuous non-normal 
—————————————————————————————————————————————————————————————————————————————
 Dependent:sex     levels           Female                Male           p   
      (N)                           (N=287)              (N=570)             
—————————————————————————————————————————————————————————————————————————————
EF             Median (IQR)    59.2 (51.4 to 63.1)  57.3 (50.0 to 61.8)  .053 
—————————————————————————————————————————————————————————————————————————————

When you choose method=2, the Wilcoxon rank sum test(also known as Mann-Whitney test) is performed.

wilcox.test(EF~sex,data=acs)

    Wilcoxon rank sum test with continuity correction

data:  EF by sex
W = 63078, p-value = 0.05295
alternative hypothesis: true location shift is not equal to 0

(3) Performs test for normality

gaze(sex~EF,data=acs, method=3) 
—————————————————————————————————————————————————————————————————————————————
 Dependent:sex     levels           Female                Male           p   
      (N)                           (N=287)              (N=570)             
—————————————————————————————————————————————————————————————————————————————
EF             Median (IQR)    59.2 (51.4 to 63.1)  57.3 (50.0 to 61.8)  .053 
—————————————————————————————————————————————————————————————————————————————

When method=3, perform the Shapiro-Wilk test or the Anderson-Daring test for normality(nortest::ad.test) to decide between normal or non-normal. If the number of cases are below 5000, Shapiro-Wilk test performed. If above 5000, Anderson-Daring test for normality performed.

nrow(acs)
[1] 857
out=lm(age~sex,data=acs)
shapiro.test(resid(out))

    Shapiro-Wilk normality test

data:  resid(out)
W = 0.99343, p-value = 0.000808

The result of shapiro.test() is significant. So we perform Wilcoxon rank sum test.

2. Comparison of three or more groups

The ‘Dx’ column of acs data is diagnosis. It has three groups : Unstable Angina, NSTEMI and STEMI. You can make a table summarizing baseline characteristics among three groups. The parametric method comparing means of three or more groups is ANOVA, whereas non-parametric method is Kruskal-Wallis rank sum test.

gaze(Dx~.,data=acs) %>% myft()

name

levels

NSTEMI (N=153)

STEMI (N=304)

Unstable Angina (N=400)

p

age

Mean ± SD

64.3 ± 12.3

62.1 ± 12.1

63.8 ± 11.0

.073

sex

Female

50 (32.7%)

84 (27.6%)

153 (38.2%)

.012

Male

103 (67.3%)

220 (72.4%)

247 (61.8%)

cardiogenicShock

No

149 (97.4%)

256 (84.2%)

400 (100%)

<.001

Yes

4 (2.6%)

48 (15.8%)

0 (0%)

entry

Femoral

58 (37.9%)

133 (43.8%)

121 (30.2%)

.001

Radial

95 (62.1%)

171 (56.2%)

279 (69.8%)

EF

Mean ± SD

55.0 ± 9.3

52.4 ± 9.5

59.2 ± 8.7

<.001

height

Mean ± SD

163.3 ± 8.2

165.1 ± 8.2

161.7 ± 9.7

<.001

weight

Mean ± SD

64.3 ± 10.2

65.7 ± 11.6

64.5 ± 11.6

.361

BMI

Mean ± SD

24.1 ± 3.2

24.0 ± 3.3

24.6 ± 3.4

.064

obesity

No

106 (69.3%)

209 (68.8%)

252 (63%)

.186

Yes

47 (30.7%)

95 (31.2%)

148 (37%)

TC

Mean ± SD

193.7 ± 53.6

183.2 ± 43.4

183.5 ± 48.3

.057

LDLC

Mean ± SD

126.1 ± 44.7

116.7 ± 39.5

112.9 ± 40.4

.004

HDLC

Mean ± SD

38.9 ± 11.9

38.5 ± 11.0

37.8 ± 10.9

.501

TG

Mean ± SD

130.1 ± 88.5

106.5 ± 72.0

137.4 ± 101.6

<.001

DM

No

96 (62.7%)

208 (68.4%)

249 (62.2%)

.209

Yes

57 (37.3%)

96 (31.6%)

151 (37.8%)

HBP

No

62 (40.5%)

150 (49.3%)

144 (36%)

.002

Yes

91 (59.5%)

154 (50.7%)

256 (64%)

smoking

Ex-smoker

42 (27.5%)

66 (21.7%)

96 (24%)

<.001

Never

50 (32.7%)

97 (31.9%)

185 (46.2%)

Smoker

61 (39.9%)

141 (46.4%)

119 (29.8%)

(1) Parametric method

Now we focus on comparing means of age among three groups.

gaze(Dx~age,data=acs)  # default : method=1
———————————————————————————————————————————————————————————————————————————————————————
 Dependent:Dx   levels        NSTEMI          STEMI          Unstable Angina       p   
     (N)                     (N=153)         (N=304)             (N=400)               
———————————————————————————————————————————————————————————————————————————————————————
age           Mean ± SD       64.3 ± 12.3    62.1 ± 12.1              63.8 ± 11.0  .073 
———————————————————————————————————————————————————————————————————————————————————————

We can perform ANOVA as follows

out=lm(age~Dx,data=acs)
anova(out)
Analysis of Variance Table

Response: age
           Df Sum Sq Mean Sq F value  Pr(>F)  
Dx          2    715  357.62   2.624 0.07309 .
Residuals 854 116389  136.29                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

On analysis of variance table you can get the p value 0.073.

(2) Non-parametric method

gaze(Dx~age,data=acs, method=2) %>% myft()

name

levels

NSTEMI (N=153)

STEMI (N=304)

Unstable Angina (N=400)

p

age

Median (IQR)

65.0 (55.0 to 75.0)

62.0 (53.0 to 71.0)

65.0 (56.0 to 72.0)

.109

The above p value in the table is the result of Kruskal-Wallis rank sum test.

kruskal.test(age~Dx,data=acs)

    Kruskal-Wallis rank sum test

data:  age by Dx
Kruskal-Wallis chi-squared = 4.424, df = 2, p-value = 0.1095
      if(sum(result)<=5000) out4=shapiro.test(resid(out3))
      else out4=nortest::ad.test(resid(out3))
      out5=kruskal.test(as.numeric(x),factor(y))
      p=c(out4$p.value,anova(out3)$Pr[1],out5$p.value)

(3) Performs test for normality

gaze(Dx~age,data=acs, method=3) %>% myft()

name

levels

NSTEMI (N=153)

STEMI (N=304)

Unstable Angina (N=400)

p

age

Median (IQR)

65.0 (55.0 to 75.0)

62.0 (53.0 to 71.0)

65.0 (56.0 to 72.0)

.109

When method=3, gaze() performs normality test.

out=lm(age~Dx,data=acs)
shapiro.test(resid(out))

    Shapiro-Wilk normality test

data:  resid(out)
W = 0.99102, p-value = 4.413e-05

Since the result for normality test is significant(\(p<0.001\)), then we perform Kruskal-Wallis test.

Statistical tests for categorical variables

The statistical methods for categorical variables in gaze() are as follows:

You can choose by setting catMethod argument(default value is 2).

(1) Default method : chi-squared test with continuity correction

The default method for categorical variables is chi-squared test with Yates’s correction for continuity(https://en.wikipedia.org/wiki/Yates%27s_correction_for_continuity).

gaze(sex~Dx,data=acs) # default : catMethod=2
————————————————————————————————————————————————————————————————————
 Dependent:sex      levels           Female          Male       p   
      (N)                           (N=287)        (N=570)          
————————————————————————————————————————————————————————————————————
Dx             NSTEMI                 50 (17.4%)   103 (18.1%)  .012 
               STEMI                  84 (29.3%)   220 (38.6%)       
               Unstable Angina       153 (53.3%)   247 (43.3%)       
————————————————————————————————————————————————————————————————————

You can get same result with the following R code:

result=table(acs$Dx,acs$sex)
chisq.test(result)  # default: correct = TRUE

    Pearson's Chi-squared test

data:  result
X-squared = 8.7983, df = 2, p-value = 0.01229

(2) Chi-squared test without continuity correction

If you want to perform chi-squared test without continuity correction, just set catMethod=1. This is the default method in SPSS.

gaze(sex~Dx,data=acs, catMethod=1) # Perform chisq.test without continuity correction
————————————————————————————————————————————————————————————————————
 Dependent:sex      levels           Female          Male       p   
      (N)                           (N=287)        (N=570)          
————————————————————————————————————————————————————————————————————
Dx             NSTEMI                 50 (17.4%)   103 (18.1%)  .012 
               STEMI                  84 (29.3%)   220 (38.6%)       
               Unstable Angina       153 (53.3%)   247 (43.3%)       
————————————————————————————————————————————————————————————————————

You can get same result with the following R code:

result=table(acs$Dx,acs$sex)
chisq.test(result, correct=FALSE)  # without continuity correction

    Pearson's Chi-squared test

data:  result
X-squared = 8.7983, df = 2, p-value = 0.01229

(3) Fisher’s exact test

If you want to perform Fisher’s exact test, set the catMethod=3.

gaze(sex~Dx,data=acs, catMethod=3) # Perform Fisher's exact test
————————————————————————————————————————————————————————————————————
 Dependent:sex      levels           Female          Male       p   
      (N)                           (N=287)        (N=570)          
————————————————————————————————————————————————————————————————————
Dx             NSTEMI                 50 (17.4%)   103 (18.1%)  .012 
               STEMI                  84 (29.3%)   220 (38.6%)       
               Unstable Angina       153 (53.3%)   247 (43.3%)       
————————————————————————————————————————————————————————————————————

You can get same result with the following R code:

result=table(acs$Dx,acs$sex)
fisher.test(result)  

    Fisher's Exact Test for Count Data

data:  result
p-value = 0.01191
alternative hypothesis: two.sided

(4) Test for trend in proportions

If you want to perform test for trend in proportions, set the catMethod=4. You can perform this test only when the grouping variable has only two group(male and female for example).

gaze(sex~Dx,data=acs, catMethod=4) # Perform test for trend in proportions
————————————————————————————————————————————————————————————————————
 Dependent:sex      levels           Female          Male       p   
      (N)                           (N=287)        (N=570)          
————————————————————————————————————————————————————————————————————
Dx             NSTEMI                 50 (17.4%)   103 (18.1%)  .050 
               STEMI                  84 (29.3%)   220 (38.6%)       
               Unstable Angina       153 (53.3%)   247 (43.3%)       
————————————————————————————————————————————————————————————————————

You can get same result with the following R code:

result=table(acs$Dx,acs$sex)
result
                 
                  Female Male
  NSTEMI              50  103
  STEMI               84  220
  Unstable Angina    153  247
prop.trend.test(result[,2],rowSums(result)) 

    Chi-squared Test for Trend in Proportions

data:  result[, 2] out of rowSums(result) ,
 using scores: 1 2 3
X-squared = 3.8332, df = 1, p-value = 0.05025

Make a combining table with two or more grouping variables

You can make a combining table with two or more grouping variables.

gaze(sex+Dx~.,data=acs) %>% myft()

sex (N)

Female (N=287)

Male (N=570)

name

levels

NSTEMI (N=50)

STEMI (N=84)

Unstable Angina (N=153)

p

NSTEMI (N=103)

STEMI (N=220)

Unstable Angina (N=247)

p

age

Mean ± SD

70.9 ± 11.4

69.1 ± 10.4

67.7 ± 10.7

.177

61.1 ± 11.6

59.4 ± 11.7

61.4 ± 10.6

.133

cardiogenicShock

No

49 (98%)

73 (86.9%)

153 (100%)

<.001

100 (97.1%)

183 (83.2%)

247 (100%)

<.001

Yes

1 (2%)

11 (13.1%)

0 (0%)

3 (2.9%)

37 (16.8%)

0 (0%)

entry

Femoral

22 (44%)

45 (53.6%)

52 (34%)

.013

36 (35%)

88 (40%)

69 (27.9%)

.022

Radial

28 (56%)

39 (46.4%)

101 (66%)

67 (65%)

132 (60%)

178 (72.1%)

EF

Mean ± SD

54.8 ± 9.1

52.3 ± 10.9

59.4 ± 8.8

<.001

55.1 ± 9.4

52.4 ± 8.9

59.1 ± 8.7

<.001

height

Mean ± SD

154.2 ± 5.1

155.7 ± 5.4

152.6 ± 6.7

.002

167.5 ± 5.7

168.7 ± 6.0

167.3 ± 6.4

.055

weight

Mean ± SD

57.2 ± 10.3

57.4 ± 9.0

57.1 ± 9.1

.978

67.5 ± 8.4

68.8 ± 10.9

69.0 ± 10.6

.479

BMI

Mean ± SD

24.1 ± 4.3

23.6 ± 3.2

24.5 ± 3.5

.215

24.1 ± 2.6

24.1 ± 3.4

24.6 ± 3.4

.205

obesity

No

35 (70%)

60 (71.4%)

99 (64.7%)

.528

71 (68.9%)

149 (67.7%)

153 (61.9%)

.301

Yes

15 (30%)

24 (28.6%)

54 (35.3%)

32 (31.1%)

71 (32.3%)

94 (38.1%)

TC

Mean ± SD

196.3 ± 52.7

180.7 ± 45.7

191.1 ± 53.1

.192

192.6 ± 54.3

184.1 ± 42.6

178.7 ± 44.6

.036

LDLC

Mean ± SD

127.7 ± 39.5

111.0 ± 40.0

118.3 ± 41.8

.088

125.4 ± 47.1

118.9 ± 39.1

109.5 ± 39.2

.002

HDLC

Mean ± SD

40.1 ± 13.8

39.5 ± 11.2

38.5 ± 10.8

.627

38.4 ± 10.9

38.1 ± 10.9

37.4 ± 10.9

.655

TG

Mean ± SD

112.5 ± 51.1

112.3 ± 87.2

126.3 ± 76.0

.316

138.0 ± 100.2

104.3 ± 65.5

144.3 ± 114.2

<.001

DM

No

25 (50%)

54 (64.3%)

94 (61.4%)

.240

71 (68.9%)

154 (70%)

155 (62.8%)

.219

Yes

25 (50%)

30 (35.7%)

59 (38.6%)

32 (31.1%)

66 (30%)

92 (37.2%)

HBP

No

19 (38%)

28 (33.3%)

36 (23.5%)

.084

43 (41.7%)

122 (55.5%)

108 (43.7%)

.016

Yes

31 (62%)

56 (66.7%)

117 (76.5%)

60 (58.3%)

98 (44.5%)

139 (56.3%)

smoking

Ex-smoker

8 (16%)

13 (15.5%)

28 (18.3%)

.184

34 (33%)

53 (24.1%)

68 (27.5%)

.002

Never

37 (74%)

57 (67.9%)

115 (75.2%)

13 (12.6%)

40 (18.2%)

70 (28.3%)

Smoker

5 (10%)

14 (16.7%)

10 (6.5%)

56 (54.4%)

127 (57.7%)

109 (44.1%)

You can select whether or not show total column.

gaze(sex+Dx~.,data=acs,show.total=TRUE) %>% myft()

sex (N)

Female (N=287)

Male (N=570)

name

levels

NSTEMI (N=50)

STEMI (N=84)

Unstable Angina (N=153)

total (N=287)

p

NSTEMI (N=103)

STEMI (N=220)

Unstable Angina (N=247)

total (N=570)

p

age

Mean ± SD

70.9 ± 11.4

69.1 ± 10.4

67.7 ± 10.7

68.7 ± 10.7

.177

61.1 ± 11.6

59.4 ± 11.7

61.4 ± 10.6

60.6 ± 11.2

.133

cardiogenicShock

No

49 (98%)

73 (86.9%)

153 (100%)

275 (95.8%)

<.001

100 (97.1%)

183 (83.2%)

247 (100%)

530 (93%)

<.001

Yes

1 (2%)

11 (13.1%)

0 (0%)

12 (4.2%)

3 (2.9%)

37 (16.8%)

0 (0%)

40 (7%)

entry

Femoral

22 (44%)

45 (53.6%)

52 (34%)

119 (41.5%)

.013

36 (35%)

88 (40%)

69 (27.9%)

193 (33.9%)

.022

Radial

28 (56%)

39 (46.4%)

101 (66%)

168 (58.5%)

67 (65%)

132 (60%)

178 (72.1%)

377 (66.1%)

EF

Mean ± SD

54.8 ± 9.1

52.3 ± 10.9

59.4 ± 8.8

56.3 ± 10.1

<.001

55.1 ± 9.4

52.4 ± 8.9

59.1 ± 8.7

55.6 ± 9.4

<.001

height

Mean ± SD

154.2 ± 5.1

155.7 ± 5.4

152.6 ± 6.7

153.8 ± 6.2

.002

167.5 ± 5.7

168.7 ± 6.0

167.3 ± 6.4

167.9 ± 6.1

.055

weight

Mean ± SD

57.2 ± 10.3

57.4 ± 9.0

57.1 ± 9.1

57.2 ± 9.3

.978

67.5 ± 8.4

68.8 ± 10.9

69.0 ± 10.6

68.7 ± 10.3

.479

BMI

Mean ± SD

24.1 ± 4.3

23.6 ± 3.2

24.5 ± 3.5

24.2 ± 3.6

.215

24.1 ± 2.6

24.1 ± 3.4

24.6 ± 3.4

24.3 ± 3.2

.205

obesity

No

35 (70%)

60 (71.4%)

99 (64.7%)

194 (67.6%)

.528

71 (68.9%)

149 (67.7%)

153 (61.9%)

373 (65.4%)

.301

Yes

15 (30%)

24 (28.6%)

54 (35.3%)

93 (32.4%)

32 (31.1%)

71 (32.3%)

94 (38.1%)

197 (34.6%)

TC

Mean ± SD

196.3 ± 52.7

180.7 ± 45.7

191.1 ± 53.1

188.9 ± 51.1

.192

192.6 ± 54.3

184.1 ± 42.6

178.7 ± 44.6

183.3 ± 45.9

.036

LDLC

Mean ± SD

127.7 ± 39.5

111.0 ± 40.0

118.3 ± 41.8

117.8 ± 41.2

.088

125.4 ± 47.1

118.9 ± 39.1

109.5 ± 39.2

116.0 ± 41.1

.002

HDLC

Mean ± SD

40.1 ± 13.8

39.5 ± 11.2

38.5 ± 10.8

39.0 ± 11.5

.627

38.4 ± 10.9

38.1 ± 10.9

37.4 ± 10.9

37.8 ± 10.9

.655

TG

Mean ± SD

112.5 ± 51.1

112.3 ± 87.2

126.3 ± 76.0

119.9 ± 76.2

.316

138.0 ± 100.2

104.3 ± 65.5

144.3 ± 114.2

127.9 ± 97.3

<.001

DM

No

25 (50%)

54 (64.3%)

94 (61.4%)

173 (60.3%)

.240

71 (68.9%)

154 (70%)

155 (62.8%)

380 (66.7%)

.219

Yes

25 (50%)

30 (35.7%)

59 (38.6%)

114 (39.7%)

32 (31.1%)

66 (30%)

92 (37.2%)

190 (33.3%)

HBP

No

19 (38%)

28 (33.3%)

36 (23.5%)

83 (28.9%)

.084

43 (41.7%)

122 (55.5%)

108 (43.7%)

273 (47.9%)

.016

Yes

31 (62%)

56 (66.7%)

117 (76.5%)

204 (71.1%)

60 (58.3%)

98 (44.5%)

139 (56.3%)

297 (52.1%)

smoking

Ex-smoker

8 (16%)

13 (15.5%)

28 (18.3%)

49 (17.1%)

.184

34 (33%)

53 (24.1%)

68 (27.5%)

155 (27.2%)

.002

Never

37 (74%)

57 (67.9%)

115 (75.2%)

209 (72.8%)

13 (12.6%)

40 (18.2%)

70 (28.3%)

123 (21.6%)

Smoker

5 (10%)

14 (16.7%)

10 (6.5%)

29 (10.1%)

56 (54.4%)

127 (57.7%)

109 (44.1%)

292 (51.2%)

Missing data analysis

You can use gaze() for missing data analysis. Set the missing argument TRUE.

gaze(EF~.,data=acs, missing=TRUE) %>% myft()

Dependent:EF

levels

Not missing (N=723)

Missing (N=134)

p

age

Mean ± SD

63.1 ± 11.9

64.3 ± 10.6

.303

sex

Female

240 (33.2%)

47 (35.1%)

.746

Male

483 (66.8%)

87 (64.9%)

cardiogenicShock

No

686 (94.9%)

119 (88.8%)

.012

Yes

37 (5.1%)

15 (11.2%)

entry

Femoral

262 (36.2%)

50 (37.3%)

.889

Radial

461 (63.8%)

84 (62.7%)

Dx

NSTEMI

139 (19.2%)

14 (10.4%)

<.001

STEMI

272 (37.6%)

32 (23.9%)

Unstable Angina

312 (43.2%)

88 (65.7%)

height

Mean ± SD

163.2 ± 9.1

163.1 ± 9.3

.908

weight

Mean ± SD

64.7 ± 11.4

66.3 ± 10.7

.251

BMI

Mean ± SD

24.2 ± 3.4

24.9 ± 3.1

.093

obesity

No

465 (64.3%)

102 (76.1%)

.011

Yes

258 (35.7%)

32 (23.9%)

TC

Mean ± SD

186.1 ± 47.5

179.9 ± 49.0

.183

LDLC

Mean ± SD

117.5 ± 40.5

111.1 ± 44.3

.110

HDLC

Mean ± SD

38.5 ± 11.0

36.9 ± 11.6

.135

TG

Mean ± SD

123.7 ± 87.2

134.1 ± 108.9

.309

DM

No

462 (63.9%)

91 (67.9%)

.428

Yes

261 (36.1%)

43 (32.1%)

HBP

No

303 (41.9%)

53 (39.6%)

.680

Yes

420 (58.1%)

81 (60.4%)

smoking

Ex-smoker

172 (23.8%)

32 (23.9%)

.033

Never

268 (37.1%)

64 (47.8%)

Smoker

283 (39.1%)

38 (28.4%)

If there is no missing data, show the table summarizing missing numbers.

gaze(sex~.,data=acs,missing=TRUE) %>% myft()
There is no missing data in column 'sex'

name

levels

N

stats

n

age

Mean ± SD

857

63.3 ± 11.7

857

cardiogenicShock

No

857

805 (93.9%)

805

Yes

52 (6.1%)

52

entry

Femoral

857

312 (36.4%)

312

Radial

545 (63.6%)

545

Dx

NSTEMI

857

153 (17.9%)

153

STEMI

304 (35.5%)

304

Unstable Angina

400 (46.7%)

400

EF

Mean ± SD

723

55.8 ± 9.6

723

height

Mean ± SD

764

163.2 ± 9.1

764

weight

Mean ± SD

766

64.8 ± 11.4

766

BMI

Mean ± SD

764

24.3 ± 3.3

764

obesity

No

857

567 (66.2%)

567

Yes

290 (33.8%)

290

TC

Mean ± SD

834

185.2 ± 47.8

834

LDLC

Mean ± SD

833

116.6 ± 41.1

833

HDLC

Mean ± SD

834

38.2 ± 11.1

834

TG

Mean ± SD

842

125.2 ± 90.9

842

DM

No

857

553 (64.5%)

553

Yes

304 (35.5%)

304

HBP

No

857

356 (41.5%)

356

Yes

501 (58.5%)

501

smoking

Ex-smoker

857

204 (23.8%)

204

Never

332 (38.7%)

332

Smoker

321 (37.5%)

321

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.