The Experts below are selected from a list of 1563 Experts worldwide ranked by ideXlab platform

Joshua A Salomon - One of the best experts on this subject based on the ideXlab platform.

  • modeling ranking time trade off and visual analog scale values for eq 5d health states a review and comparison of methods
    Medical Care, 2009
    Co-Authors: Benjamin M Craig, Jan J V Busschbach, Joshua A Salomon
    Abstract:

    textabstractAbstract BACKGROUND: There is rising interest in eliciting health state valuations using rankings. Due to their relative simplicity, ordinal measurement methods may offer an attractive practical alternative to cardinal methods, such as time trade-off (TTO) and visual analog scale (VAS). In this article, we explore alternative models for estimating cardinal health state values from rank responses in a unique multicountry database. We highlight an estimation challenge pertaining to health states just below perfect health (the "nonoptimal gap") and propose an analytic solution to ameliorate this problem. METHODS: Using a standardized protocol developed by the EuroQol Group, rank, VAS, and TTO responses were collected for 43 health states in 8 countries: Slovenia, Argentina, Denmark, Japan, Netherlands, Spain, United Kingdom, and United States, yielding a sample of 179,431 state responses from 11,483 subjects. States were described using the EQ-5D system, which allows for 3 different possible levels on 5 different dimensions of health. We estimated conditional logit and probit regression models for rank responses. The regressions included 17 health state attribute variables reflecting specific levels on each dimension and counts of different levels across dimensions. This flexible specification accommodates previously published valuation models, such as models applied in the United Kingdom and United States. In addition to fitting standard conditional logit and probit models, which assume Equal Variance across health states (homoscedasticity), we examined a heteroscedastic probit model that assumes no Variance for the 2 points anchoring the scale ("optimal health" and "dead") and relaxes the Equal-Variance Assumption for all other states. Rank-based predictions for the 243 unique states defined by the EQ-5D system were compared with predictions from conventional linear models fitted to TTO and VAS responses. RESULTS: By construction, the TTO and VAS models assume no Variance around the anchoring states of optimal health and dead. Mimicking this Assumption in the probit rank models helps dissolve the nonoptimal gap. For all other states, Variances in TTO and VAS were negatively associated with mean values, which contradict the Assumption of homoscedasticity. Estimated health state values from the heteroscedastic probit model for the ranking data were highly correlated with predictions from both TTO and VAS models for the 243 EQ-5D states. Between VAS and rank-based estimates, Lin's rho, a measure of agreement, was over 0.98 with a mean absolute difference of 0.028. Corresponding measures of agreement between rank and TTO estimates were 0.96 and 0.12, which is similar to the agreement between VAS and TTO. CONCLUSIONS: Rank-based valuation techniques, which offer advantages of flexibility, generalizability, and ease of administration, may be attractive substitutes for TTO and VAS in the measurement of societal values for health outcomes.

Benjamin M Craig - One of the best experts on this subject based on the ideXlab platform.

  • modeling ranking time trade off and visual analog scale values for eq 5d health states a review and comparison of methods
    Medical Care, 2009
    Co-Authors: Benjamin M Craig, Jan J V Busschbach, Joshua A Salomon
    Abstract:

    textabstractAbstract BACKGROUND: There is rising interest in eliciting health state valuations using rankings. Due to their relative simplicity, ordinal measurement methods may offer an attractive practical alternative to cardinal methods, such as time trade-off (TTO) and visual analog scale (VAS). In this article, we explore alternative models for estimating cardinal health state values from rank responses in a unique multicountry database. We highlight an estimation challenge pertaining to health states just below perfect health (the "nonoptimal gap") and propose an analytic solution to ameliorate this problem. METHODS: Using a standardized protocol developed by the EuroQol Group, rank, VAS, and TTO responses were collected for 43 health states in 8 countries: Slovenia, Argentina, Denmark, Japan, Netherlands, Spain, United Kingdom, and United States, yielding a sample of 179,431 state responses from 11,483 subjects. States were described using the EQ-5D system, which allows for 3 different possible levels on 5 different dimensions of health. We estimated conditional logit and probit regression models for rank responses. The regressions included 17 health state attribute variables reflecting specific levels on each dimension and counts of different levels across dimensions. This flexible specification accommodates previously published valuation models, such as models applied in the United Kingdom and United States. In addition to fitting standard conditional logit and probit models, which assume Equal Variance across health states (homoscedasticity), we examined a heteroscedastic probit model that assumes no Variance for the 2 points anchoring the scale ("optimal health" and "dead") and relaxes the Equal-Variance Assumption for all other states. Rank-based predictions for the 243 unique states defined by the EQ-5D system were compared with predictions from conventional linear models fitted to TTO and VAS responses. RESULTS: By construction, the TTO and VAS models assume no Variance around the anchoring states of optimal health and dead. Mimicking this Assumption in the probit rank models helps dissolve the nonoptimal gap. For all other states, Variances in TTO and VAS were negatively associated with mean values, which contradict the Assumption of homoscedasticity. Estimated health state values from the heteroscedastic probit model for the ranking data were highly correlated with predictions from both TTO and VAS models for the 243 EQ-5D states. Between VAS and rank-based estimates, Lin's rho, a measure of agreement, was over 0.98 with a mean absolute difference of 0.028. Corresponding measures of agreement between rank and TTO estimates were 0.96 and 0.12, which is similar to the agreement between VAS and TTO. CONCLUSIONS: Rank-based valuation techniques, which offer advantages of flexibility, generalizability, and ease of administration, may be attractive substitutes for TTO and VAS in the measurement of societal values for health outcomes.

Mario Medvedovic - One of the best experts on this subject based on the ideXlab platform.

  • intensity based hierarchical bayes method improves testing for differentially expressed genes in microarray experiments
    BMC Bioinformatics, 2006
    Co-Authors: Maureen A Sartor, Craig R Tomlinson, Scott C Wesselkamper, Siva Sivaganesan, George D Leikauf, Mario Medvedovic
    Abstract:

    The small sample sizes often used for microarray experiments result in poor estimates of Variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the Variance of gene expression measurements into the general empirical Bayes framework. We present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available at http://eh3.uc.edu/ibmt . We use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of Variances relative to the degree of identity (i.e. t-tests vs. Equal Variance Assumption), and the relationship between Variance and signal intensity. When this Variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

Jan J V Busschbach - One of the best experts on this subject based on the ideXlab platform.

  • modeling ranking time trade off and visual analog scale values for eq 5d health states a review and comparison of methods
    Medical Care, 2009
    Co-Authors: Benjamin M Craig, Jan J V Busschbach, Joshua A Salomon
    Abstract:

    textabstractAbstract BACKGROUND: There is rising interest in eliciting health state valuations using rankings. Due to their relative simplicity, ordinal measurement methods may offer an attractive practical alternative to cardinal methods, such as time trade-off (TTO) and visual analog scale (VAS). In this article, we explore alternative models for estimating cardinal health state values from rank responses in a unique multicountry database. We highlight an estimation challenge pertaining to health states just below perfect health (the "nonoptimal gap") and propose an analytic solution to ameliorate this problem. METHODS: Using a standardized protocol developed by the EuroQol Group, rank, VAS, and TTO responses were collected for 43 health states in 8 countries: Slovenia, Argentina, Denmark, Japan, Netherlands, Spain, United Kingdom, and United States, yielding a sample of 179,431 state responses from 11,483 subjects. States were described using the EQ-5D system, which allows for 3 different possible levels on 5 different dimensions of health. We estimated conditional logit and probit regression models for rank responses. The regressions included 17 health state attribute variables reflecting specific levels on each dimension and counts of different levels across dimensions. This flexible specification accommodates previously published valuation models, such as models applied in the United Kingdom and United States. In addition to fitting standard conditional logit and probit models, which assume Equal Variance across health states (homoscedasticity), we examined a heteroscedastic probit model that assumes no Variance for the 2 points anchoring the scale ("optimal health" and "dead") and relaxes the Equal-Variance Assumption for all other states. Rank-based predictions for the 243 unique states defined by the EQ-5D system were compared with predictions from conventional linear models fitted to TTO and VAS responses. RESULTS: By construction, the TTO and VAS models assume no Variance around the anchoring states of optimal health and dead. Mimicking this Assumption in the probit rank models helps dissolve the nonoptimal gap. For all other states, Variances in TTO and VAS were negatively associated with mean values, which contradict the Assumption of homoscedasticity. Estimated health state values from the heteroscedastic probit model for the ranking data were highly correlated with predictions from both TTO and VAS models for the 243 EQ-5D states. Between VAS and rank-based estimates, Lin's rho, a measure of agreement, was over 0.98 with a mean absolute difference of 0.028. Corresponding measures of agreement between rank and TTO estimates were 0.96 and 0.12, which is similar to the agreement between VAS and TTO. CONCLUSIONS: Rank-based valuation techniques, which offer advantages of flexibility, generalizability, and ease of administration, may be attractive substitutes for TTO and VAS in the measurement of societal values for health outcomes.

Salomon J.a. - One of the best experts on this subject based on the ideXlab platform.

  • Modeling Ranking, Time Trade-Off and Visual Analogue Scale Values for EQ-5D Health States
    'Ovid Technologies (Wolters Kluwer Health)', 2009
    Co-Authors: Craig B.m., Busschbach, J.j. Van, Salomon J.a.
    Abstract:

    Abstract BACKGROUND: There is rising interest in eliciting health state valuations using rankings. Due to their relative simplicity, ordinal measurement methods may offer an attractive practical alternative to cardinal methods, such as time trade-off (TTO) and visual analog scale (VAS). In this article, we explore alternative models for estimating cardinal health state values from rank responses in a unique multicountry database. We highlight an estimation challenge pertaining to health states just below perfect health (the "nonoptimal gap") and propose an analytic solution to ameliorate this problem. METHODS: Using a standardized protocol developed by the EuroQol Group, rank, VAS, and TTO responses were collected for 43 health states in 8 countries: Slovenia, Argentina, Denmark, Japan, Netherlands, Spain, United Kingdom, and United States, yielding a sample of 179,431 state responses from 11,483 subjects. States were described using the EQ-5D system, which allows for 3 different possible levels on 5 different dimensions of health. We estimated conditional logit and probit regression models for rank responses. The regressions included 17 health state attribute variables reflecting specific levels on each dimension and counts of different levels across dimensions. This flexible specification accommodates previously published valuation models, such as models applied in the United Kingdom and United States. In addition to fitting standard conditional logit and probit models, which assume Equal Variance across health states (homoscedasticity), we examined a heteroscedastic probit model that assumes no Variance for the 2 points anchoring the scale ("optimal health" and "dead") and relaxes the Equal-Variance Assumption for all other states. Rank-based predictions for the 243 unique states defined by the EQ-5D system were compared with predictions from conventional linear models fitted to TTO and VAS responses. RESULTS: By construction, the TTO and VAS models assume no Variance around the anchoring states of optimal health and dead. Mimicking this Assumption in the probit rank models helps dissolve the nonoptimal gap. For all other states, Variances in TTO and VAS were negatively associated with mean values, which contradict the Assumption of homoscedasticity. Estimated health state values from the heteroscedastic probit model for the ranking data were highly correlated with predictions from both TTO and VAS models for the 243 EQ-5D states. Between VAS and rank-based estimates, Lin's rho, a measure of agreement, was over 0.98 with a mean absolute difference of 0.028. Corresponding measures of agreement between rank and TTO estimates were 0.96 and 0.12, which is similar to the agreement between VAS and TTO. CONCLUSIONS: Rank-based valuation techniques, which offer advantages of flexibility, generalizability, and ease of administration, may be attractive substitutes for TTO and VAS in the measurement of societal values for health outcomes