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