A simulation study of nonparametric total deviation index as a measure of agreement based on quantile regression.

Total deviation index (TDI) captures a prespecified quantile of the absolute deviation of paired observations from raters, observers, methods, assays, instruments, etc. We compare the performance of TDI using nonparametric quantile regression to the TDI assuming normality (Lin, 2000). This simulation study considers three distributions: normal, Poisson, and uniform at quantile levels of 0.8 and 0.9 for cases with and without contamination. Study endpoints include the bias of TDI estimates (compared with their respective theoretical values), standard error of TDI estimates (compared with their true simulated standard errors), and test size (compared with 0.05), and power. Nonparametric TDI using quantile regression, although it slightly underestimates and delivers slightly less power for data without contamination, works satisfactorily under all simulated cases even for moderate (say, ≥40) sample sizes. The performance of the TDI based on a quantile of 0.8 is in general superior to that of 0.9. The performances of nonparametric and parametric TDI methods are compared with a real data example. Nonparametric TDI can be very useful when the underlying distribution on the difference is not normal, especially when it has a heavy tail.

Minimum clinically important difference in medical studies.

In clinical trials, minimum clinically important difference (MCID) has attracted increasing interest as an important supportive clinical and statistical inference tool. Many estimation methods have been developed based on various intuitions, while little theoretical justification has been established. This article proposes a new estimation framework of the MCID using both diagnostic measurements and patient-reported outcomes (PROs). The framework first formulates the population-based MCID as a large margin classification problem, and then extends to the personalized MCID to allow individualized thresholding value for patients whose clinical profiles may affect their PRO responses. More importantly, the proposed estimation framework is showed to be asymptotically consistent, and a finite-sample upper bound is established for its prediction accuracy compared against the ideal MCID. The advantage of our proposed method is also demonstrated in a variety of simulated experiments as well as two phase-3 clinical trials.

A comparison model for measuring individual agreement.

This article proposes a general comparison model for assessing individual agreement of k  ≥  2 raters evaluating n subjects with m  ≥  2 replicated readings. Users can explore total-rater agreement relative to intrarater agreement where any subset of the k raters can be selected in the numerator and denominator. Users are also allowed to compare intrarater agreement among selected raters. Based on the ratio of mean squared deviations (MSDs), two comparative agreement indices, total-intra ratio (TIR) and intra-intra ratio (IIR), are proposed. The TIR is a noninferiority assessment such that the differences of individual readings from different raters cannot be inferior by a prespecified margin to the differences of the replicated readings within raters. TIR can be used whether a reference exists or not. The method used by the Food and Drug Administration (FDA) for evaluating individual bioequivalence under relative scale becomes the special case of our approach. The IIR is a classical assessment such that the precision of selected raters can be better than; equal to; or worse than that of other raters. The estimation and statistical inference of TIR and IIR are obtained through GEE methodology.

Statistical Scoring Procedures Applicable to Laboratory Performance Evaluation.

Two statistical scoring procedures based on p-values have been developed to evaluate the overall performance of analytical laboratories performing environmental measurements. The overall score of bias and standing are used to determine how consistently a laboratory is able to measure the true (unknown) value correctly over time. The overall score of precision and standing are used to determine how well a laboratory is able to reproduce its measurements in the long run. Criteria are established for qualitatively labeling measurements as Acceptable, Warning, and Not Acceptable, and for identifying areas where laboratories should re-evaluate their measurement procedures. These statistical scoring procedures are applied to two real environmental data sets.

A unified approach for assessing agreement for continuous and categorical data.

This paper proposes several Concordance Correlation Coefficient (CCC) indices to measure the agreement among k raters, with each rater having multiple (m) readings from each of the n subjects for continuous and categorical data. In addition, for normal data, this paper also proposes the coverage probability (CP) and total deviation index (TDI). Those indices are used to measure intra, inter and total agreement among all raters. Intra-rater indices are used to measure the agreement among the multiple readings from the same rater. Inter-rater indices are used to measure the agreement among different raters based on the average of multiple readings. Total-rater indices are used to measure the agreement among different raters based on individual readings. In addition to the agreement, the paper also assess intra, inter, and total precision and accuracy. Through a two-way mixed model, all CCC, precision and accuracy, TDI, and CP indices are expressed as functions of variance components, and GEE method is used to obtain the estimates and perform inferences for all the functions of variance components. Each of previous proposed approaches for assessing agreement becomes one of the special case of the proposed approach. For continuous data, when m approaches infinity, the proposed estimates reduce to the agreement indices proposed by Barnhart et al. (2005). When m = 1, the proposed estimate reduces to the ICC proposed by Carrasco and Jover (2003). When m = 1, the proposed estimate also reduces to the OCCC proposed by Lin (1989), King and Chinchilli (2001a) and Barnhart et al. (2002). When m = 1 and k = 2, the proposed estimate reduces to the original CCC proposed by Lin (1989). For categorical data, when k = 2 and m = 1, the proposed estimate and its associated inference reduce to the kappa for binary data and weighted kappa with squared weight for ordinal data.

Seasonal variation of red clover (Trifolium pratense L., Fabaceae) isoflavones and estrogenic activity.

Red clover (Trifolium pratense L., Fabaceae) dietary supplements are currently used to treat menopausal symptoms because of their high content of the mildly estrogenic isoflavones daidzein, genistein, formononetin, and biochanin A. These compounds are estrogenic in vitro and in vivo, but little information exists on the best time to harvest red clover fields to maximize content of the isoflavones and thus make an optimal product. Samples of cultivated red clover above-ground parts and flower heads were collected in parallel over one growing season in northeastern Illinois. Generally, autohydrolytic extracts of above-ground parts contained more isoflavones and had more estrogenic activity in Ishikawa endometrial cells as compared with extracts of flower heads. Daidzein and genistein contents peaked around June to July, while formononetin and biochanin A contents peaked in early September. Flower head and total above-ground parts extracts exhibited differential estrogenic activity in an Ishikawa (endometrial) cell-based alkaline phosphatase induction assay, whereas nondifferential activity was observed for most extracts tested in an MCF-7 (breast) cell proliferation assay when tested at the same final concentrations. Ishikawa assay results could be mapped onto the extracts' content of individual isoflavones, but MCF-7 results did not show such a pattern. These results suggest that significant metabolism of isoflavones may occur in MCF-7 cells but not in Ishikawa cells; therefore, caution is advised in the choice of bioassay used for the biological standardization of botanical dietary supplements.

Optimal designs in stability studies.

New optimality criteria for stability studies are proposed, and the related optimal designs are investigated. For each optimality criterion, optimal designs are identified within a class of competing designs. The property of the optimal designs for detecting slope differences is discussed.

Comparison of the in vitro estrogenic activities of compounds from hops (Humulus lupulus) and red clover (Trifolium pratense).

Because the prevailing form of hormone replacement therapy is associated with the development of cancer in breast and endometrial tissues, alternatives are needed for the management of menopausal symptoms. Formulations of Trifolium pratense L. (red clover) are being used to alleviate menopause-associated hot flashes but have shown mixed results in clinical trials. The strobiles of Humulus lupulusL. (hops) have been reported to contain the prenylflavanone, 8-prenylnaringenin (8-PN), as the most estrogenic constituent, and this was confirmed using an estrogen receptor ligand screening assay utilizing ultrafiltration mass spectrometry. Extracts of hops and red clover and their individual constituents including 8-PN, 6-prenylnaringenin (6-PN), isoxanthohumol (IX), and xanthohumol (XN) from hops and daidzein, formononetin, biochanin A, and genistein from red clover were compared using a variety of in vitro estrogenic assays. The IC50 values for the estrogen receptor alpha and beta binding assays were 15 and 27 microg/mL, respectively, for hops and 18.0 and 2.0 microg/mL, respectively, for the red clover extract. Both of the extracts, genistein, and 8-PN activated the estrogen response element (ERE) in Ishikawa cells while the extracts, biochanin A, genistein, and 8-PN, significantly induced ERE-luciferase expression in MCF-7 cells. Hop and red clover extracts as well as 8-PN up-regulated progesterone receptor (PR) mRNA in the Ishikawa cell line. In the MCF-7 cell line, PR mRNA was significantly up-regulated by the extracts, biochanin A, genistein, 8-PN, and IX. The two extracts had EC50 values of 1.1 and 1.9 microg/mL, respectively, in the alkaline phosphatase induction assay. On the basis of these data, hops and red clover could be attractive for the development as herbal dietary supplements to alleviate menopause-associated symptoms.

Optimal and efficient crossover designs under different assumptions about the carryover effects.

In certain studies it is desirable or necessary that a subject, such as a patient in a medical trial, receive a treatment in each period. This facilitates a within-subject comparison of the treatments. Designs for studies of this type are called crossover designs or repeated measurements designs. If there are s subjects in p periods, the design should specify which of the t treatments is assigned to subject j in period i, i = 1,... ,p,j = 1,..., s. Equivalently we may think of a design as assigning each subject to one of the t(p) possible treatment sequences. The choice of a design will clearly depend on the values of p, s, and t, to which we will refer as the design parameters. But for any set of design parameters, we will typically still have many design choices. To distinguish between different designs for the same design parameters, we will compare the designs under criteria that are related to the objective of the study. Often the objective is a comparison of the treatments, and we would choose a design that, in some sense, provides good estimates of the treatment differences. For these criteria, a design that is optimal under one statistical model may not be optimal under another. It is therefore also of interest to identify designs that are efficient (relative to an optimal design) for more than one model. The main difference in the models that we will consider is in how the possible first-order carryover effects are modeled. This is a controversial issue, and it is by no means our intent to resolve this here. But a design that is efficient under a variety of plausible models is preferable to one that performs well under one model but poorly under another. Our main focus will be on two models. One of these models has been considered extensively in the literature, while the other is relatively new. For selected design parameters, we will compare selected designs under these models.