Menopause status

Var1	Freq
1	164
2	455
3	446
NA	0

Age

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  30.00   45.00   50.00   50.26   55.00   75.00

[1] 1065

[1] 7.532381

MENOSTATUS	mean	median	sd	n
1	42.37805	42	4.495779	164
2	47.06154	47	4.523136	455
3	56.42601	56	5.993453	446

Height

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  129.0   160.0   165.0   164.6   170.0   196.0

[1] 7.276345

[1] 1065

MENOSTATUS	mean	median	sd	n
1	164.6951	165	7.809368	164
2	164.7582	165	7.505858	455
3	164.4439	165	6.835482	446

Weight

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  36.00   59.00   64.00   65.88   71.00  112.00

[1] 10.8061

[1] 1065

MENOSTATUS	mean	median	sd	n
1	65.63415	64	10.24068	164
2	66.59121	65	10.89254	455
3	65.24888	63	10.89968	446

Body fat

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   5.00   20.00   24.00   24.45   29.00   40.00

[1] 6.176382

[1] 1065

MENOSTATUS	mean	median	sd	n
1	23.38554	23	5.958866	83
2	24.54545	24	6.077791	198
3	24.80303	24	6.342716	198

Years post menopause

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.     NAs 
  1.000   2.000   5.000   6.622  10.000  99.000     623

[1] 7.24716

[1] 1065

Years of resistance training

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    4.00   10.00   12.56   20.00   44.00

[1] 10.51795

[1] 485

MENOSTATUS	mean	median	sd	n
1	11.45946	10	7.957402	74
2	10.63590	9	9.062540	195
3	14.67130	10	12.049059	216

Race

#Race/ethnicity
#American indian
table(data$AA)


  0 
485

kable(prop.table(table(data$AA)))

Var1	Freq
0	1

x=xtabs(~AA+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	74	195	216

kable(prop.table(x,2))

	1	2	3
0	1	1	1

#Asian
table(data$ASIAN)


  0   1 
465  20

kable(prop.table(table(data$ASIAN)))

Var1	Freq
0	0.9587629
1	0.0412371

x=xtabs(~ASIAN+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	70	191	204
1	4	4	12

kable(prop.table(x,2))

	1	2	3
0	0.9459459	0.9794872	0.9444444
1	0.0540541	0.0205128	0.0555556

#BLACK
table(data$BLACK)


  0   1 
482   3

kable(prop.table(table(data$BLACK)))

Var1	Freq
0	0.9938144
1	0.0061856

x=xtabs(~BLACK+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	74	193	215
1	0	2	1

kable(prop.table(x,2))

	1	2	3
0	1	0.9897436	0.9953704
1	0	0.0102564	0.0046296

#HISP
table(data$HISP, useNA = "always")


   0    1 <NA> 
 459   26    0

kable(prop.table(table(data$HISP)))

Var1	Freq
0	0.9463918
1	0.0536082

x=xtabs(~HISP+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	67	182	210
1	7	13	6

kable(prop.table(x,2))

	1	2	3
0	0.9054054	0.9333333	0.9722222
1	0.0945946	0.0666667	0.0277778

#MIXED
table(data$MIXED)


  0   1 
475  10

kable(prop.table(table(data$MIXED)))

Var1	Freq
0	0.9793814
1	0.0206186

x=xtabs(~MIXED+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	71	191	213
1	3	4	3

kable(prop.table(x,2))

	1	2	3
0	0.9594595	0.9794872	0.9861111
1	0.0405405	0.0205128	0.0138889

#SUM THIS FINDS PEOPLE WHO CHOSE MULTIPLE RACES
table(data$SUM)


  0   1   2   3 
  4 469   8   4

kable(prop.table(table(data$SUM)))

Var1	Freq
0	0.0082474
1	0.9670103
2	0.0164948
3	0.0082474

x=xtabs(~SUM+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	0	1	3
1	73	187	209
2	0	4	4
3	1	3	0

kable(prop.table(x,2))

	1	2	3
0	0.0000000	0.0051282	0.0138889
1	0.9864865	0.9589744	0.9675926
2	0.0000000	0.0205128	0.0185185
3	0.0135135	0.0153846	0.0000000

#nhpi
table(data$NHPI)


  0   1 
482   3

kable(prop.table(table(data$NHPI)))

Var1	Freq
0	0.9938144
1	0.0061856

x=xtabs(~NHPI+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	74	193	215
1	0	2	1

kable(prop.table(x,2))

	1	2	3
0	1	0.9897436	0.9953704
1	0	0.0102564	0.0046296

#NAFR
table(data$NAFR)


  0   1 
484   1

kable(prop.table(table(data$NAFR)))

Var1	Freq
0	0.9979381
1	0.0020619

x=xtabs(~NAFR+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	74	195	215
1	0	0	1

kable(prop.table(x,2))

	1	2	3
0	1	1	0.9953704
1	0	0	0.0046296

#WHITE
table(data$WHITE)


  0   1 
 64 421

kable(prop.table(table(data$WHITE)))

Var1	Freq
0	0.1319588
1	0.8680412

x=xtabs(~WHITE+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	15	22	27
1	59	173	189

kable(prop.table(x,2))

	1	2	3
0	0.2027027	0.1128205	0.125
1	0.7972973	0.8871795	0.875

#OTHER
table(data$OTHER, useNA = "always")


   0    1 <NA> 
 472   13    0

kable(prop.table(table(data$OTHER)))

Var1	Freq
0	0.9731959
1	0.0268041

x=xtabs(~OTHER+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	71	189	212
1	3	6	4

kable(prop.table(x,2))

	1	2	3
0	0.9594595	0.9692308	0.9814815
1	0.0405405	0.0307692	0.0185185

#DNK REFUSED
table(data$DNK, useNA = "always")


   0 <NA> 
 485    0

kable(prop.table(table(data$DNK)))

Var1	Freq
0	1

x=xtabs(~DNK+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	74	195	216

kable(prop.table(x,2))

	1	2	3
0	1	1	1

#No answer
table(data$NOANS)


  0   1 
481   4

kable(prop.table(table(data$NOANS)))

Var1	Freq
0	0.9917526
1	0.0082474

x=xtabs(~NOANS+MENOSTATUS, data=data)
kable(x)

	1	2	3
0	74	194	213
1	0	1	3

kable(prop.table(x,2))

	1	2	3
0	1	0.9948718	0.9861111
1	0	0.0051282	0.0138889

Education

Var1	Freq
College graduate	195
DNK/Prefer not to answer	4
GED/High School Graduate	18
Graduate degree	177
Less than High School Grad	6
Some college or technical school	85
NA	0

Var1	Freq
College graduate	0.4020619
DNK/Prefer not to answer	0.0082474
GED/High School Graduate	0.0371134
Graduate degree	0.3649485
Less than High School Grad	0.0123711
Some college or technical school	0.1752577
NA	0.0000000

	1	2	3
College graduate	28	82	85
DNK/Prefer not to answer	1	1	2
GED/High School Graduate	4	8	6
Graduate degree	33	65	79
Less than High School Grad	1	1	4
Some college or technical school	7	38	40

	1	2	3
College graduate	0.3783784	0.4205128	0.3935185
DNK/Prefer not to answer	0.0135135	0.0051282	0.0092593
GED/High School Graduate	0.0540541	0.0410256	0.0277778
Graduate degree	0.4459459	0.3333333	0.3657407
Less than High School Grad	0.0135135	0.0051282	0.0185185
Some college or technical school	0.0945946	0.1948718	0.1851852

Employment

Var1	Freq
Disabled	1
DNK/Prefer not to answer	1
Homemaker	49
Other	10
Retired	38
Unemployed	3
Working full time (>= 35 hours per week)	256
Working part-time (< 35 hours per week)	127
NA	0

Var1	Freq
Disabled	0.0020619
DNK/Prefer not to answer	0.0020619
Homemaker	0.1010309
Other	0.0206186
Retired	0.0783505
Unemployed	0.0061856
Working full time (>= 35 hours per week)	0.5278351
Working part-time (< 35 hours per week)	0.2618557

	1	2	3
Disabled	0	1	0
DNK/Prefer not to answer	0	1	0
Homemaker	8	22	19
Other	2	5	3
Retired	0	1	37
Unemployed	1	2	0
Working full time (>= 35 hours per week)	50	111	95
Working part-time (< 35 hours per week)	13	52	62

	1	2	3
Disabled	0.0000000	0.0051282	0.0000000
DNK/Prefer not to answer	0.0000000	0.0051282	0.0000000
Homemaker	0.1081081	0.1128205	0.0879630
Other	0.0270270	0.0256410	0.0138889
Retired	0.0000000	0.0051282	0.1712963
Unemployed	0.0135135	0.0102564	0.0000000
Working full time (>= 35 hours per week)	0.6756757	0.5692308	0.4398148
Working part-time (< 35 hours per week)	0.1756757	0.2666667	0.2870370

Income

Var1	Freq
$150K-$199,999K	69
$200K-249,000	57
$250K or more	120
$50K-$99K	67
0-49,999	23
100-149,999	67
DNK/Prefer not to answer	82
NA	0

Var1	Freq
$150K-$199,999K	0.1422680
$200K-249,000	0.1175258
$250K or more	0.2474227
$50K-$99K	0.1381443
0-49,999	0.0474227
100-149,999	0.1381443
DNK/Prefer not to answer	0.1690722
NA	0.0000000

	1	2	3
$150K-$199,999K	13	30	26
$200K-249,000	11	26	20
$250K or more	11	60	49
$50K-$99K	11	20	36
0-49,999	9	6	8
100-149,999	9	25	33
DNK/Prefer not to answer	10	28	44

	1	2	3
$150K-$199,999K	0.1756757	0.1538462	0.1203704
$200K-249,000	0.1486486	0.1333333	0.0925926
$250K or more	0.1486486	0.3076923	0.2268519
$50K-$99K	0.1486486	0.1025641	0.1666667
0-49,999	0.1216216	0.0307692	0.0370370
100-149,999	0.1216216	0.1282051	0.1527778
DNK/Prefer not to answer	0.1351351	0.1435897	0.2037037

Distribution comparisons for Inclusion vs. Status and Inclusion by Race

# Create the 2 x 3 table
tab <- matrix(
  c(496, 164,
    1823, 455,
    1830, 446),
  nrow = 2,
  byrow = FALSE
)
tab

     [,1] [,2] [,3]
[1,]  496 1823 1830
[2,]  164  455  446

rownames(tab) <- c("Not dropped", "Dropped")
colnames(tab) <- c("Pre", "Peri", "Post")

tab

            Pre Peri Post
Not dropped 496 1823 1830
Dropped     164  455  446

# Chi-square test comparing inclusion by status
x=chisq.test(tab)
x


    Pearson's Chi-squared test

data:  tab
X-squared = 9.1939, df = 2, p-value = 0.01008

ci_cramersv(x)


    Two-sided 95% chi-squared confidence interval for the population
    Cramer's V

Sample estimate: 0.04199171 
Confidence interval:
      2.5%      97.5% 
0.02210297 0.07011688

tab <- matrix(
  c(
    1,  19,
    41, 122,
    4,  19,
    67, 227,
    21, 194,
    8, 11,
    6,  25,
    922,  3720,
    27, 78, 
    10, 16
  ),
  nrow = 10,
  byrow = TRUE
)

rownames(tab) <- c(
  "AIndian", "Asian", "Black", "Hispanic", "Mixed",
  "HawaiianI", "NAfrican", "White", "Other", "DNK"
)

colnames(tab) <- c("Dropped", "Not_Dropped")
tab

          Dropped Not_Dropped
AIndian         1          19
Asian          41         122
Black           4          19
Hispanic       67         227
Mixed          21         194
HawaiianI       8          11
NAfrican        6          25
White         922        3720
Other          27          78
DNK            10          16

# Chi-square test comparing race vs. inclusion
x=chisq.test(tab)
x


    Pearson's Chi-squared test

data:  tab
X-squared = 34.676, df = 9, p-value = 0.00006796

ci_cramersv(x)


    Two-sided 95% chi-squared confidence interval for the population
    Cramer's V

Sample estimate: 0.0791293 
Confidence interval:
      2.5%      97.5% 
0.05694361 0.10523708

Age comparison


    Welch Modified Two-Sample t-Test

data:  Summarized x and y
t = 4.0283, df = 1640.9, p-value = 0.00005875
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.5336158 1.5463842
sample estimates:
mean of x mean of y 
    51.30     50.26

[1] 0.1391427

Resistance training comparison


    Welch Modified Two-Sample t-Test

data:  Summarized x and y
t = -0.31462, df = 620.84, p-value = 0.7532
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.1586909  0.8386909
sample estimates:
mean of x mean of y 
    12.40     12.56

[1] -0.01472294

FFMS

Laura Connell Pyott

2026-10-04

Menopause status

Age

Height

Weight

Body fat

Years post menopause

Years of resistance training

Race

Education

Employment

Income

Distribution comparisons for Inclusion vs. Status and Inclusion by Race

Age comparison

Resistance training comparison