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.

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.

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%.

```
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
```

```
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.

```
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.

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.

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%) |

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.

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)
```

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.

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).

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:

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:

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:

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:

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

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.

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%) |

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

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.

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 |