In computerized adaptive tests, some newly developed items are often added for pretesting purposes. In this pretesting, item characteristics are estimated which is called calibration. It is promising to allocate calibration items to examinees based on their abilities and methods from optimal experimental design have been used for that. However, the abilities of the examinees have usually been assumed to be known for this allocation. In practice, the abilities are estimates based on a limited number of operational items. We develop the theory for handling the uncertainty in abilities in a proper way and show how optimal calibration design can be derived in this situation. The method has been implemented in an R package. We see that the derived optimal calibration designs are more robust if this uncertainty in abilities is acknowledged.

When large achievement tests are conducted regularly, items need to be calibrated before being used as operational items in a test. Methods have been developed to optimally assign pretest items to examinees based on their abilities. Most of these methods, however, are intended for situations where examinees arrive sequentially to be assigned to calibration items. In several calibration tests, examinees take the test simultaneously or in parallel. In this article, we develop an optimal calibration design tailored for such parallel test setups. Our objective is both to investigate the efficiency gain of the method as well as to demonstrate that this method can be implemented in real calibration scenarios. For the latter, we have employed this method to calibrate items for the Swedish national tests in Mathematics. In this case study, like in many real test situations, items are of mixed format and the optimal design method needs to handle that. The method we propose works for mixed-format tests and accounts for varying expected response times. Our investigations show that the proposed method considerably enhances calibration efficiency.

Objectives The Swedish prescribed drug register contains dose instructions as written by the physician. A challenge is to convert the text into a number of doses per day which can be used to calculate duration of treatment. The objective of this study is to compare algorithms for named entity recognition to extract dosage per day. Methods Two sequence models, Hidden Markov Model (HMM) and Conditional Random Fields (CRF), were used to predict label sequences. The HMM and CRF were compared using different measures of prediction: precision, recall, F-score and accuracy. We also evaluated how prediction was effected by including more labels and features; for CRF models we used 12 labels for both models with 2 and 11 feature types respectively, for HMM models we used 12, 15 and 18 labels respectively. Using the predicted labels, a rule-based algorithm was used to predict dosage per day. Prediction of dosage per day was evaluated using accuracy. Results Label prediction: As expected, increasing the number of labels/features increased the F-score. The CRF model with 11 feature types had a F-score of 0.989 compared to 0.972 using two feature types. The HMM model with 15 and 18 labels both achieved a F-score of 0.986 compared to 0.966 using 12 labels. In terms of precision and recall the performance of the CRF and HMM varied. Dosage prediction: The CRF model with 11 feature types achieved 97.2% accuracy. The HMM with 15 labels achieved a higher accuracy than with 18 labels (95.7% versus 95.5%). Conclusions The CRF had the highest accuracy in label and dosage per day prediction. The HMM model also had comparably high accuracy but was generally lower than the CRF. We recommend CRF over HMM for named entity recognition on prescription text; it is time efficient and predicts dosage per day with high accuracy.

This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four-step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose-finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs.We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose–response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design.

The problem of allocating experimental units to treatment groups when variance heterogeneity over treatment groups is present is considered. A(A)- and D-A-optimal allocations are derived for estimation of linear combinations of treatment means. Explicit expressions for the design weights are provided for the A(A)-optimal design. The minimax strategy is introduced as an approach to handle unknown variances. Efficiencies of minimax allocations are evaluated.

This paper demonstrates how to plan a contingent valuation experiment to assess the value of ecologically produced clothes. First, an appropriate statistical model (the trinomial spike model) that describes the probability that a randomly selected individual will accept any positive bid, and if so, will accept the bid A, is defined. Secondly, an optimization criterion that is a function of the variances of the parameter estimators is chosen. However, the variances of the parameter estimators in this model depend on the true parameter values. Pilot study data are therefore used to obtain estimates of the parameter values and a locally optimal design is found. Because this design is only optimal given that the estimated parameter values are correct, a design that minimizes the maximum of the criterion function over a plausable parameter region (i.e. a minimax design) is then found.

This paper concerns design of contingent valuation experiments when interest is in knowing whether respondents have positive willingness to pay and if so, if they are willing to pay a certain amount for a specified good. A trinomial spike model is used to model the response. Locally D- and c-optimal designs are derived and it is shown that any locally optimal design can be deduced from the locally optimal design for the case when one of the model parameters is standardized. It is demonstrated how information about the parameters, e.g. from pilot studies, can be used to construct minimax and maximin efficient designs, for which the best guaranteed value of the criterion function or efficiency function is sought under the assumption that the parameter values are within certain regions. The proposed methodology is illustrated in an application where the value of environmentally friendly produced clothes is evaluated.

As the costs of clinical studies increase, the demand for more efficient designs also increases. Therefore, there is a growing interest in introducing designs that optimize precision in clinical studies. Unfortunately, optimal designs generally require knowledge of unknown parameters. We consider the maximin approach to handle this problem. A maximin efficient design maximizes the efficiency when compared to a standard design, as the parameters vary in a specified subset of the parameter space. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering an Emax-sigmoid model describing a dose-response relationship and compare inferential precision with that obtained when using a uniform design. In a first approach to construct a maximin efficient design we specify a number of possible scenarios, each of which describing a possible shape of the dose-response relation. The design obtained is shown to be at least 15 percent more efficient than the uniform design. It is then shown that the obtained design is maximin efficient also for a much larger parameter set defined by parameter values between those specified by the initial scenarios.

c-optimal designs for estimating the model parameters of the quadratic logistic regression model are considered. The designs are constructed via the canonical design space. It is shown that the number of design points varies between 1 and 4 depending on the parameter being estimated. Furthermore, formulae for finding the design points along with the corresponding design weights are derived.