The standard first-line treatment for normogonadotropic anovulatory infertile patients [referred to as World Health Organization group 2 (WHO 2)] is ovulation induction using clomiphene citrate (CC) in incremental doses. Twenty to 25% of women show clomiphene-resistant anovulation (CRA), that is, they remain anovulatory even after multiple attempts with increased doses of CC. About 50% of the ovulatory CC patients conceive within six CC-induced cycles. Given the heterogeneous nature of the group, the individual prognosis (i.e., the chance of success) will vary considerably between patients. In the event an individual prognosis of each patient would be available before the start of the treatment, the overall efficiency of ovulation induction could be improved. Prognostic evidence at an individual level should use multiple patient variables, including results from previous treatments (if any). When variables are interdependent, a statistical model can be used to relate individual characteristics with the predicted outcome. Such a model will provide estimates of prognosis for individualized patient profiles, allowing new patients to profit from the experience of the cohort of previous patients used to build the model. This paper discusses the prediction of time to pregnancy following induction of ovulation with CC. This prediction was broken down in two steps, leading to two separate prognostic models. The first model predicts an intermediate outcome, the chance that the patient will be CRA (i.e., no ovulation in response to CC medication); the second model predicts the final outcome (time until pregnancy) in women who do ovulate. The CRA model was based on a prospective cohort study of 201 patients with normogonadotropic oligoamenorrheic infertility, 45 of whom were CRA (22%). It contained four predictor variables all related to the diagnosis of PCOS within the group of WHO 2: Increased free androgen index (FAI; hyperandrogenemia), elevated body mass index (BMI; obesity), greater mean ovarian volume (as an ultrasound feature of polycystic ovaries), and amenorrhea were all predictive for CRA. The second model was based on the non-CRA patients and contained two prognostic variables: increased age and oligomenorrhea were predictive for longer time to pregnancy after first ovulation with CC. Using the example of the prediction of time to pregnancy following induction of ovulation with CC, we present and discuss characteristics of good prognostic evidence for clinical use, focusing on study design, statistical analysis, evaluation, and presentation of results.