Statistical tests in gaze

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:

1 forces analysis as normal-distributed
2 forces analysis as continuous non-normal
3 performs a Shapiro-Wilk test or nortest::ad.test to decide between normal or non-normal

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:

0 : Perform chi-squared test first. If warning present, perform Fisher’s exact test
1 : Perform chi-squared test without continuity correction
2 : Perform chi-squared test with continuity correction (default value)
3 : perform Fisher’s exact test
4 : perform test for trend in proportions

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

Statistical tests in gaze

Loading package

Statistical tests for numeric variables

1. Comparison of two groups

(1) Parametric method

(2) Non-parametric method

(3) Performs test for normality

2. Comparison of three or more groups

(1) Parametric method

(2) Non-parametric method

(3) Performs test for normality

Statistical tests for categorical variables

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

(2) Chi-squared test without continuity correction

(3) Fisher’s exact test

(4) Test for trend in proportions

Make a combining table with two or more grouping variables

Missing data analysis