Abstract
Prediction models are used in reproductive medicine to calculate the probability of pregnancy without treatment, as well as the probability of pregnancy after ovulation induction, intrauterine insemination or in vitro fertilization. The performance of such prediction models is often evaluated with a receiver operating characteristic (ROC) curve. The area under the ROC curve, also known as cstatistic, is then used as a measure of model performance. The value of this cstatistic is low for most prediction models in reproductive medicine. Here, we demonstrate that low values of the cstatistic are to be expected in these prediction models, but we also show that this does not imply that these models are of limited use in clinical practice. The calibration of the model (the correspondence between modelbased probabilities and observed pregnancy rates) as well as the availability of a clinically useful distribution of probabilities and the ability to correctly identify the appropriate form of management are more meaningful concepts for model evaluation.
Language  English 

Pages  17741778 
Number of pages  5 
Journal  Human Reproduction 
Volume  24 
Issue number  8 
DOIs 

Publication status  Published  1 Jan 2009 
Keywords
 Fertility
 IUI
 IVF
 Prediction model
 Spontaneous pregnancy
ASJC Scopus subject areas
 Reproductive Medicine
 Obstetrics and Gynaecology
Cite this
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Evaluating prediction models in reproductive medicine. / Coppus, S. F P J; Van Der Veen, F.; Opmeer, B. C.; Mol, B. W J; Bossuyt, P. M M.
In: Human Reproduction, Vol. 24, No. 8, 01.01.2009, p. 17741778.Research output: Contribution to journal › Comment/debate
TY  JOUR
T1  Evaluating prediction models in reproductive medicine
AU  Coppus, S. F P J
AU  Van Der Veen, F.
AU  Opmeer, B. C.
AU  Mol, B. W J
AU  Bossuyt, P. M M
PY  2009/1/1
Y1  2009/1/1
N2  Prediction models are used in reproductive medicine to calculate the probability of pregnancy without treatment, as well as the probability of pregnancy after ovulation induction, intrauterine insemination or in vitro fertilization. The performance of such prediction models is often evaluated with a receiver operating characteristic (ROC) curve. The area under the ROC curve, also known as cstatistic, is then used as a measure of model performance. The value of this cstatistic is low for most prediction models in reproductive medicine. Here, we demonstrate that low values of the cstatistic are to be expected in these prediction models, but we also show that this does not imply that these models are of limited use in clinical practice. The calibration of the model (the correspondence between modelbased probabilities and observed pregnancy rates) as well as the availability of a clinically useful distribution of probabilities and the ability to correctly identify the appropriate form of management are more meaningful concepts for model evaluation.
AB  Prediction models are used in reproductive medicine to calculate the probability of pregnancy without treatment, as well as the probability of pregnancy after ovulation induction, intrauterine insemination or in vitro fertilization. The performance of such prediction models is often evaluated with a receiver operating characteristic (ROC) curve. The area under the ROC curve, also known as cstatistic, is then used as a measure of model performance. The value of this cstatistic is low for most prediction models in reproductive medicine. Here, we demonstrate that low values of the cstatistic are to be expected in these prediction models, but we also show that this does not imply that these models are of limited use in clinical practice. The calibration of the model (the correspondence between modelbased probabilities and observed pregnancy rates) as well as the availability of a clinically useful distribution of probabilities and the ability to correctly identify the appropriate form of management are more meaningful concepts for model evaluation.
KW  Fertility
KW  IUI
KW  IVF
KW  Prediction model
KW  Spontaneous pregnancy
UR  http://www.scopus.com/inward/record.url?scp=67651087306&partnerID=8YFLogxK
U2  10.1093/humrep/dep109
DO  10.1093/humrep/dep109
M3  Comment/debate
VL  24
SP  1774
EP  1778
JO  Human Reproduction
T2  Human Reproduction
JF  Human Reproduction
SN  02681161
IS  8
ER 