krisztina boda and péter kovács
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APPLICATION OF MULTIVARIATE ANALYSES TO FIND PREDICTORS OF MULTIPLE GESTATIONS
FOLLOWING IN VITRO FERTILIZATION
Krisztina Boda and Péter Kovács
Department of Medical Informatics, University of Szeged, Hungary (boda@dmi.uszeged.hu), and
Kaali Institute IVF Center, Budapest, Hungary
22
Introduction
• Multivariate methods are frequently used in medical research. The choice of the appropriate method depends on several criteria, but multicollinearity is a common problem of these methods.
• The aim of this work is to show the application of multivariate methods to find the best predictors of multifetal pregnancy from several, highly correlated independent variables.
33
Background
• Assisted reproductive technology (ART) has lead to a dramatic increase in multiple gestations.
• Multiple gestations, especially high-order multiple gestations are undesired outcome following ART. A multifetal pregnancy is associated with significant maternal, fetal and neonatal morbidity/mortality.
44
Data• Retrospective analysis of 896 fresh in vitro fertilization
(IVF) cycles that resulted in pregnancy from 2002-2003. • Patient characteristics
– age, baseline FSH, etiology of infertility ,
• stimulation parameters– protocol, number of follicles, oocytes, mature oocytes (MII),
fertilization rate, endometrial thickness,
• embryology parameters– number of embryos transferred, quality of best embryo
transferred, embryo score (ESC)
• were evaluated and compared between cycles resulting in singleton and multiple gestations.
55
Frequencies of pregnancies by the number of embryos transferred (What can we do with the „0”-s?)
Pregnancy
Multiple
Singleton Twins Triplets Quadruplets Total 1 22 0 0 0 22 2 141 39 0 0 180 3 385 128 45 1 559 4 87 32 9 3 131
No. of embryos transferred
5 4 0 0 0 4
Total 639 199 54 4 896 % 71.3% 22.2% 6.03% 0.45% 100%
66
Dealing with zeros: pooling cells
The depedent variable is dichotomous (singleton – multiple pregnancies)Number of embryos transferred: omit „1” , pool 4-5
Count
22 0 22
141 39 180
385 174 559
87 44 131
4 0 4
639 257 896
1.00
2.00
3.00
4.00
5.00
No. ofembryostransferred
Total
single multiple
Pregnancy
Total
77
Methods• Two groups:
– singleton vs. multiple gestations• Three groups:
– singleton, twin and higher-order multiple gestations
• Factors that could influence outcome were compared using univariate methods first.
• A multiple logistic regression was used to evaluate the association between cycle outcome and those factors that potentially influence the order of pregnancy; – binary logistic regression to compare two groups, – and multinomial logistic regression to compare three groups.
• Poisson regression• Strong correlation was found between several
independent variables. Multicollinearity diagnostics were performed.
Two groups: singleton vs. multiple pregnancies
99
Variables and p-values of univariate analyses when comparing singleton vs. multiple pregnancies
Code Variable p Code Variable p
NOC Number of cycles 0.665 FERT No. of fertilized oocytes 0.003 AGE Age 0.33 FERTRATE Rate of fertilized oocytes 0.139 IND Indication for IVF 0.092 ET No. of embryos transferred 0.001 Tubal factor MAX MAX embryo blastomere 0.149 Male factor ESC Embryo score <0.001 Unexplained MEANESC Mean embryo score 0.004 Ovulatory ENDV Endometrial thickness 0.316 AMP Ampoules of gonadotropins 0.216 ENDT Type of endometrium 0.453 FSH Baseline FSH (IU/l) 0.009 A LH Baseline LH (IU/l) 0.302 L FOLL No. of follicles > 14 mm 0.005 AL OOCYT No. of oocytes 0.006 CRYOS Cryopreservation 0.059 MII No. of mature oocytes 0.003 AHA Assisted hatching 0.53
1010
Variables and p-values of univariate analyses when comparing singleton vs. multiple pregnancies
Code Variable p Code Variable p
NOC Number of cycles 0.665 FERT No. of fertilized oocytes 0.003 AGE Age 0.33 FERTRATE Rate of fertilized oocytes 0.139 IND Indication for IVF 0.092 ET No. of embryos transferred 0.001 Tubal factor MAX MAX embryo blastomere 0.149 Male factor ESC Embry oscore <0.001 Unexplained MEANESC Mean embryo score 0.004 Ovulatory ENDV Endometrial thickness 0.316 AMP Ampoules of gonadotropins 0.216 ENDT Type of endometrium 0.453 FSH Baseline FSH (IU/l) 0.009 A LH Baseline LH (IU/l) 0.302 L FOLL No. of follicles > 14 mm 0.005 AL OOCYT No. of oocytes 0.006 CRYOS Cryopreservation 0.059 MII No. of mature oocytes 0.003 AHA Assisted hatching 0.53
Candidate variables for binary logistic regression
Red: p<0.05Blue: p is „small”
1111
Pairwise correlations
Marked correlations are significant at p < .05000N=861 (Casewise deletion of missing data)
VariableAGEAMP#FSHLHFOLL#OOCYTMII#FERT#FERTRATEETMAX#ESCMEANESCEND#VCRYOSAHAAGEAMP#FSHLHFOLL#OOCYTMII#FERT#FERTRATEETMAX#ESCMEANESCEND#VCRYOSAHA
1.000.170.16-0.04-0.21-0.11-0.07-0.05 0.090.22-0.010.07 -0.05-0.00-0.070.370.171.000.22-0.10-0.22-0.17-0.14-0.11 0.060.07-0.010.01 -0.020.01-0.140.100.160.221.000.15-0.23-0.18-0.17-0.15 0.060.00-0.09-0.09-0.12-0.07-0.080.11-0.04-0.100.151.000.120.050.040.07 -0.00-0.050.010.00 0.03-0.000.060.05-0.21-0.22-0.230.121.000.700.620.56 -0.190.110.230.28 0.260.040.30-0.05-0.11-0.17-0.180.050.701.000.910.81 -0.190.200.230.33 0.280.020.42-0.04-0.07-0.14-0.170.040.620.911.000.88 0.190.220.260.36 0.310.020.45-0.04-0.05-0.11-0.150.070.560.810.881.00 0.120.240.310.42 0.350.020.51-0.020.090.060.06-0.00-0.19-0.190.190.12 1.000.070.060.08 0.070.020.050.020.220.070.00-0.050.110.200.220.24 0.071.000.100.45 -0.02-0.04-0.020.23-0.01-0.01-0.090.010.230.230.260.31 0.060.101.000.81 0.860.070.190.020.070.01-0.090.000.280.330.360.42 0.080.450.811.00 0.850.030.230.10-0.05-0.02-0.120.030.260.280.310.35 0.07-0.020.860.85 1.000.060.27-0.03-0.000.01-0.07-0.000.040.020.020.02 0.02-0.040.070.03 0.061.000.00-0.01-0.07-0.14-0.080.060.300.420.450.51 0.05-0.020.190.23 0.270.001.00-0.090.370.100.110.05-0.05-0.04-0.04-0.02 0.020.230.020.10 -0.03-0.01-0.091.00
1212
Structure of variables based on correlations by cluster analysis
Tree Diagram f or 16 V ar iables
Single Linkage
1-Pears on r
0,0 0,2 0,4 0,6 0,8 1,0
Linkage Dis tanc e
END#V
LH
FSH
A MP#
FERTRA TE
ESC
MEA NESC
MA X#
ET
CRY OS
FERT#
MII#
OOCY T
FOLL#
A HA
A GE
1313
The phenomenon of multicollinearity
Univariate logistic regressions Variable Code Coeff St.Err. Wald df p No. of oocytes OOCYT 0.052 0.019 7.742 1 0.005
No. of mature oocytes MII 0.066 0.022 8.687 1 0.003 Multivariate model (variables together) Variable Code Coeff St.Err. Wald df p No. of oocytes OOCYT 0.011 0.045 0.063 1 0.802 No. of mature oocytes MII 0.053 0.054 0.991 1 0.320
•When the independent variables are correlated, there are problems in estimating regression coefficients. •The greater the multicollinearity, the greater the standard errors.•Slight changes in model structure result in considerable changes in the magnitude or sign of parameter estimates.
1414
Identification of problematic multicollinearity I.Collinearity statistics
• Tolerance. A statistic used to determine how much the independent variables are linearly related to one another. The proportion of a variable's variance not accounted for by other independent variables in the equation.
• Variance inflation factor (VIF). The reciprocal of the tolerance. As the variance inflation factor increases, so does the variance of the regression coefficient, making it an unstable estimate. Large (>4) VIF values are an indicator of multicollinearity.
21 jRTolerance
21
1
jRVIF
Rj2: the coefficient of determination for the regression of the jth independent variable on all other independent variables.
1515
Tolerance VIF Age .875 1.143 Ampoules of gonadotropins .891 1.123 Baseline FSH (IU/l) .859 1.165 Baseline LH (IU/l) .926 1.080 No. of follicles > 14 mm .460 2.173 No. of oocytes .035 28.524 No. of mature oocytes .032 31.347 No. of fertilized oocytes .203 4.928 Rate of fertilized oocytes .194 5.145 No. of embryos transferred .199 5.029 MAX embryo blastomere .238 4.198 Embryo score .057 17.560 Mean embryo score .067 14.884 Cryopreservation .697 1.434
Identification of problematic multicollinearity I.Collinearity statistics
1616
Identification of problematic multicollinearity II.Factor analysis
• Extraction method:– principal components analysis
• Rotation method:– varimax with Kaiser normalization
• Number of factors – eigenvalues >1
• Results: – Number of factors=6 – Total variance explained=69.62
1717
Rotated Component Matrixa
.944 .128 .110
.912 .103 -.221
.904 .190 .105
.710 .137 -.415
.572 .168 -.169 .130
.187 .943
.136 .933
.274 .883 .236
.724 .156
.662 -.226 .158
.255 .627 -.266 -.235
.569 .311
.840
-.197 .338 .338 .137
.128 -.151 .816
-.166 .193 .303 .613 -.141
.946
No. of mature oocytes
No. of oocytes
No. of fertilized oocytes
No. of follicles > 14 mm
Cryopreservation
Mean embryo score
Max. embryo blastomere
Embryo score
Assisted hatching
Number of cycles
No. of embryos transferred
Age
Rate of fertilized oocytes
Ampoules of gonadotropins
Baseline LH (IU/l)
Baseline FSH (IU/l)
Endometrial thickness
1 2 3 4 5 6
Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 5 iterations.a.
Rotated component matrix (coefficients <0.1 are not shown)
1818
Rotated Component Matrixa
.944 .128 .110
.912 .103 -.221
.904 .190 .105
.710 .137 -.415
.572 .168 -.169 .130
.187 .943
.136 .933
.274 .883 .236
.724 .156
.662 -.226 .158
.255 .627 -.266 -.235
.569 .311
.840
-.197 .338 .338 .137
.128 -.151 .816
-.166 .193 .303 .613 -.141
.946
No. of mature oocytes
No. of oocytes
No. of fertilized oocytes
No. of follicles > 14 mm
Cryopreservation
Mean embryo score
Max. embryo blastomere
Embryo score
Assisted hatching
Number of cycles
No. of embryos transferred
Age
Rate of fertilized oocytes
Ampoules of gonadotropins
Baseline LH (IU/l)
Baseline FSH (IU/l)
Endometrial thickness
1 2 3 4 5 6
Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 5 iterations.a.
Parameters with the strongest association with a factor were mostly included into multivariate model
1919
Binary logistic regression
• Dependent variable: – pregnancy (singleton vs. multiple pregnancies)
• Independent variables:– No. of mature oocytes– Mean embryo score– No. of embryos transferred (categorical)
- 2 vs. 3 - 2 vs. ≥4
– Age– Rate of fertilized oocytes– Baseline FSH (IU/l)– Endometrial thickness
2020
OR (odds ratio) 95% CI p-value
Mean embryo score 1.029 1.008 - 1.051 0.007
Number of embryos transferred 0.017
2 vs. 3 1.743 1.141 - 2.663 .010
2 vs. 4 2.020 1.187 - 3.435 .009
Baseline FSH .935 0.879 - 0.995 .034
Results of stepwise binary logistic regression (main effects)
The significance of model terms in logistic regression was assessed by the likelihood ratio test. Mean embryo score and the number of embryos transferred were positively, while baseline FSH level was negatively associated with multiple gestations.
2121
Estimated probability of multiple pregnancy at a mean FSH level 7.65
345.1067.0)4(703.0)3(556.0029.01
log1
log
FSHembryosifembryosifMEANESCp
p
The model-equationThe model-equation
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 10 20 30 40 50 60
Mean embryo score
pro
ba
bili
ty
2
3
4,5
number of embryos transferred
2222
Examination of interactions
Example: the interaction with age is not significant
-2 Log-likelihood, Likelihood Ratio Test Statistic (G), Degrees of Freedom (df), and p-value for Interactions of Interest when added to the main effects model
Model -2 Log-Likelihood
2 df 2diff dfdiff p-value
Main Effects Model (MEANESC, ET, FSH)
968.992 20.714 4
MEANESC, ET, FSH, MEANESC*AGE
966.978 22.728 5 2.014 1 0.156
MEANESC, ET, FSH, ET*AGE 967.572 22.133 6 1.419 2 0.492 MEANESC, ET, FSH, FSH*AGE
967.522 22.184 5 1.47 1 0.225
Specific interactions between parameters of interest were also investigated.
Poisson regression
2424
Poisson regression model
• Dependent variable: – Number of pregnancy
• Independent variables:– No. of mature oocytes– Mean embryo score– No. of embryos transferred (categorical)
- 2 vs. 3 - 2 vs. ≥4
– Age– Rate of fertilized oocytes– Baseline FSH (IU/l)– Endometrial thickness
2525
Poisson regression results by PROC GENMODproc genmod data=KOVACSP.diakhoz;model tsz= fsh et23 et24 meanesc /dist=poi link=log obstats dscale;; ods output ObStats=temp; run; Criteria For Assessing Goodness Of Fit
Criterion DF Value Value/DF
Deviance 810 190.1216 0.2347 Scaled Deviance 810 810.0000 1.0000 Pearson Chi-Square 810 219.2273 0.2707 Scaled Pearson X2 810 934.0028 1.1531 Log Likelihood -3246.1994
Analysis Of Parameter Estimates
Standard Wald 95% Confidence Chi- Parameter DF Estimate Error Limits Square Pr > ChiSq
Intercept 1 0.1497 0.0693 0.0139 0.2855 4.67 0.0307 fsh 1 -0.0095 0.0056 -0.0205 0.0015 2.89 0.0891 et23 1 0.1492 0.0389 0.0730 0.2255 14.70 0.0001 et24 1 0.1883 0.0496 0.0910 0.2856 14.40 0.0001 meanesc 1 0.0063 0.0020 0.0024 0.0102 9.99 0.0016 Scale 0 0.4845 0.0000 0.4845 0.4845
NOTE: The scale parameter was estimated by the square root of DEVIANCE/DOF.
2626
Estimated number of pregnancies at a mean FSH level 7.65
1497.00095.0)4(1883.0
)3(1492.00063.0log
FSHembryosif
embryosifMEANESCThe model-equationThe model-equation
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 10 20 30 40 50 60
Mean embryo score
nu
mb
er
2
3
4,5
number of embryos transferred
Three groups: singleton, twin and multiple pregnancies
2828
Pooling pregnancies into three groups: singletons, twins and multiple pregnancies
Count
121 36 0 157
336 104 44 484
54 18 10 82
511 158 54 723
20 3 0 23
49 24 2 75
37 14 2 53
106 41 4 151
2
3
4,5
Number of embryostransferred
Total
2
3
4,5
Number of embryostransferred
Total
Age<=35
>35
single twin muliple
Pregnancy
Total
Frequencies of pregnancies by the number of embryos transferred and by age
multiple
2929
Variables and p-values of univariate analyses when comparing singleton, twins and multiple pregnancies
Code Variable p Code Variable p
AGE Age 0.141 FERT No. of fertilized oocytes 0.001 AGE Age-group (cut-point 35 years) 0.036 FERTRATE Rate of fertilized oocytes 0.784 IND Indication for IVF 0.074 ET No. of embryos transferred <0.0001 Tubal factor MAX MAX embryo blastomere 0.208 Male factor ESC Embryo score 0.000 Unexplained MEANESC Mean embryo score 0.003 Ovulatory ENDV Endometrial thickness 0.299 AMP Ampoules of gonadotropins 0.105 ENDT Type of endometrium 0.668 FSH Baseline FSH (IU/l) 0.134 A LH Baseline LH (IU/l) 0.783 L FOLL No. of follicles > 14 mm 0.045 AL OOCYT No. of oocytes 0.006 CRYOS Cryopreservation <0.0001 MII No. of mature oocytes 0.001 AHA Assissted hatching 0.520
3030
Variables and p-values of univariate analises when comparing singleton, twins and multiple pregnancies
Code Variable p Code Variable p
AGE Age 0.141 FERT No. of fertilized oocytes 0.001 AGE Age-group (cut-point 35 years) 0.036 FERTRATE Rate of fertilized oocytes 0.784 IND Indication for IVF 0.074 ET No. of embryos transferred <0.0001 Tubal factor MAX MAX embryo blastomere 0.208 Male factor ESC Embryo score 0.000 Unexplained MEANESC Mean embryo score 0.003 Ovulatory ENDV Endometrial thickness 0.299 AMP Ampoules of gonadotropins 0.105 ENDT Type of endometrium 0.668 FSH Baseline FSH (IU/l) 0.134 A LH Baseline LH (IU/l) 0.783 L FOLL No. of follicles > 14 mm 0.045 AL OOCYT No. of oocytes 0.006 CRYOS Cryopreservation <0.0001 MII No. of mature oocytes 0.001 AHA Assissted hatching 0.520
Candidate variables for multinomial logistic regression
Red: p<0.05Blue: p is „small”
3131
Multinomial logistic regression
• Dependent variable: pregnancy– Reference category: singleton pregnancy
• Independent variables (based on univariate results and factor analysis):– embryo score– Baseline FSH – Cryopreservation– Age (group)– (number of embryos transferred was suppressed :
only >2 could be taken into account)
3232
Results
Likelihood Ratio Tests
Effect -2 Log Likelihood Chi-Square df Sig.
of Reduced Model
Intercept 1181.617(a) .000 0 .
ESC 1197.148 15.531 2 .000
FSH 1188.177 6.560 2 .038
CRYOS 1188.188 6.570 2 .037
AGE 1189.922 8.305 2 .016
Embryo score, FSH, age less than 35 years and the availability of surplus embryos for cryopreservation
were linked to high-order multiple gestations.
3333
Examination of interactions
• The interactions between age and the other variables in the model and all two-way interactions were examined and tested by the likelihood ratio test.
• None of these interactions was significant.
3434
OR 95% CI p-value
Single vs. twin
Embryo score 1.010 1.002 - 1.017 0.011
FSH 0.917 0.855 - .983 0.014
Cryopreservation 0.924 0.604 - 1.416 0.718
Age < 35 0.787 0.513 - 1.206 0.272
Single vs. multiple
Embryo score 1.021 1.009 - 1.033 0.001
FSH 0.999 0.891 - 1.121 0.990
Cryopreservation 2.147 1.176 – 3.921 0.013
Age < 35 3.649 1.098 - 12.127 0.035
Results of multinomial logistic regression
Baseline FSH was lower in patients whose cycle resulted in twins
Embryo score was significantly associated with higher-order multiple gestations as well
The risk of a high-order multiple gestation was increased 3.649 times among women under the age of 35 years .
When surplus embryos were available for cryopreservation, the risk of high-order multiple gestation was increased
3535
Conclusions
The several multivariate methods revealed similar results.
The application of multicollinearity diagnostics and factor analysis was helpful in the choice of independent variables in the multivariate models:
In the final models original and „relatively” uncorrelated variables were used.
3636
Conclusions
By limiting the number of high quality embryos transferred, especially among young women who have several good quality embryos, one could reduce the number of multifetal gestations and the perinatal outcome could be improved.
3737
ReferencesArticles:• Elster N. and the Institute for Science, Law, and Technology
Working Group on Reproductive Technology: Less is more: the risks of multiple births. Fertility and Sterility 2000;74, 617-622.
• The ESHRE Capri Workshop Group: Multiple gestation pregnancy. Human Reproduction 2000;15,1856-1864.
• Van Steen K, Curran D, Kramer J, Molenberghs G, Vreckem A, Bottomley A, Sylvester R. Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection. Statistics in Medicine 2002: 21, 3865-3884.
Books: • Hosmer DW and Lemeshow S. Applied Logistic Regression. Wiley:
New York, 2000.• Agresti A. An Introduction to Categorical Data Analysis. Wiley: New
York, 1996.
Thank you for your attention!
3939
Drawbacks
The number of embryos transferred was decided by the clinician, and was based on his own experience – this subjective element may cause bias in the model and in the parameter estimation.
However, here randomisation could not be used because of ethical reasons.
The data set contained no information about unsuccessful in vitro fertilizations, that did not result in pregnancy.
4040
Identification of problematic multicollinearity II. Collinearity Diagnostics
Dimension
Eigenvalue Condition
Index (Const) Age
FSH
FOLL OOCY MII FERT ET MAX ESC
MEANESC CRYO
1 10.363 1.000 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 2 .799 3.601 .00 .00 .01 .00 .00 .00 .00 .00 .00 .00 .00 .54 3 .302 5.855 .00 .00 .02 .02 .02 .01 .02 .00 .01 .00 .00 .22 4 .250 6.440 .00 .00 .12 .00 .00 .00 .00 .00 .01 .01 .01 .12 5 8.776E-02 10.867 .00 .00 .08 .50 .00 .02 .09 .00 .00 .00 .00 .03 6 7.367E-02 11.860 .00 .01 .43 .03 .00 .00 .00 .06 .01 .01 .01 .03 7 4.598E-02 15.013 .02 .06 .32 .12 .00 .01 .01 .02 .03 .05 .01 .00 8 3.168E-02 18.086 .00 .00 .00 .17 .26 .06 .71 .00 .00 .00 .00 .02 9 1.991E-02 22.817 .00 .04 .00 .01 .02 .00 .00 .01 .90 .02 .12 .01 10 1.450E-02 26.735 .00 .00 .00 .07 .70 .89 .16 .00 .01 .00 .00 .01 11 9.120E-03 33.708 .18 .80 .00 .06 .00 .00 .00 .11 .03 .07 .10 .00 12 2.742E-03 61.482 .79 .07 .01 .02 .00 .00 .00 .80 .00 .84 .75 .00
Condition index: square root of the ratio of the largest to the smallest eigenvalueCondition index: square root of the ratio of the largest to the smallest eigenvalue
4141
Result of stepwise binary logistic regression (main effects)
B S.E. Wald df Sig. OR 95% CI
MEANESC 0.029 0.011 7.376 1 0.007 1.029 1.008 1.051 ET 8.124 2 0.017 2 vs. 3 0.556 0.216 6.615 1 0.010 1.743 1.141 2.663 2 vs. 4,5 0.703 0.271 6.730 1 0.009 2.020 1.188 3.435 FSH -0.067 0.032 4.473 1 0.034 0.935 0.879 0.995 Constant -1.345 0.382 12.415 1 0.000 0.261
345.1067.0)4(703.0)3(556.0029.01
log1
log
FSHembryosifembryosifMEANESCp
p
The model-equationThe model-equation
)345.1067.0)4(703.0)3(556.0029.0(11
.)Pr( FSHembryosifembryosifMEANESCe
gestmultiplep
4242
Result of stepwise binary logistic regression (main effects)
The significance of model terms in logistic regression was assessed by the likelihood ratio test. . Testing Global Null Hypothesis: BETA=0Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSqTest Chi-Square DF Pr > ChiSq
Likelihood Ratio 20.7141 4 0.0004Likelihood Ratio 20.7141 4 0.0004 Score 19.9532 4 0.0005Score 19.9532 4 0.0005 Wald 19.4124 4 0.0007Wald 19.4124 4 0.0007
Hosmer and Lemeshow Goodness-of-Fit TestHosmer and Lemeshow Goodness-of-Fit Test
Chi-Square DF Pr > ChiSqChi-Square DF Pr > ChiSq
3.1985 8 0.92133.1985 8 0.9213
4343
Results of multinomial logistic regression
B Std. Error Wald df Sig. OR 95% CI multiples Intercept ESC 0.020 0.006 11.780 1 0.001 1.021 1.009 1.033 FSH -.001 0.058 0.000 1 0.990 0.999 0.891 1.121 [cryos=.00] .764 0.307 6.189 1 0.013 0.466 0.255 0.850 [cryos=1.00] 0 . . 0 . . . . [AGEGR=.00] 1.294 0.613 4.462 1 0.035 3.649 1.098 12.127 [AGEGR=1.00] 0 . . 0 . . . .
twins Intercept -.852 0.465 3.351 1 0.067 ESC .009 0.004 6.514 1 0.011 1.010 1.002 1.017 FSH -.087 0.035 6.032 1 0.014 0.917 0.855 0.983 [cryos=.00] .079 0.217 0.131 1 0.718 1.082 0.706 1.656 [cryos=1.00] 0 . . 0 . . . . [AGEGR=.00] -.240 0.218 1.209 1 0.272 0.787 0.513 1.206 [AGEGR=1.00] 0 . . 0 . . . .
The reference category is: single
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