芳香族化合物大鼠经口LD50预测模型的研究

目的通过芳香族化合物分子结构与大鼠经口LD50间的定量构效关系研究,建立LD50预测系统.方法采用逐步回归统计分析方法,在对91种芳香族化合物进行电化学、热力学及3D结构学等参数与大鼠经口LD50间的构效关系研究基础上,建立LD50预测模型,并探讨预测准确性.结果该预测模型对高毒性化合物的预测准确度为100%、中毒性为80%、微毒性为61.5%,低毒性化合物的预测准确度较差仅为24.6%,总预测准确度为63.2%.大鼠经口LD50预测值和实测值之间呈正相关系数(r＝0.65,P＜0.05).结论计算机毒性预测系统是化合物安全性评价工作中的有效的辅助方法.     

A comparison of stated preference methods for estimating monetary values

This paper presents a comparison of willingness to pay (WTP) estimates generated from a dichotomous choice (DC) contingent valuation experiment and a choice experiment (CE). The study was conducted with subjects undergoing assisted reproductive techniques in the Grampian area of Scotland. Subjects first completed a DC WTP questionnaire. They were sent a CE questionnaire two months later. Welfare estimates for the DC WTP study and CE were not significantly different. Important research questions are raised concerning the validity of the various approaches to estimating WTP.     

A predictive model for Dengue Hemorrhagic Fever epidemics

A statistical model for predicting monthly Dengue Hemorrhagic Fever (DHF) cases from the city of Makassar is developed and tested. The model uses past and present DHF cases, climate and meteorological observations as inputs. These inputs are selected using a stepwise regression method to predict future DHF cases. The model is tested independently and its skill assessed using two skill measures. Using the selected variables as inputs, the model is capable of predicting a moderately-severe epidemic at lead times of up to six months. The most important input variable in the prediction is the present number of DHF cases followed by the relative humidity three to four months previously. A prediction 1-6 months in advance is sufficient to initiate various activities to combat DHF epidemic. The model is suitable for warning and easily becomes an operational tool due to its simplicity in data requirement and computational effort.     

A weighted generalized score statistic for comparison of predictive values of diagnostic tests

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re‐formulations that are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re‐formulation we presented, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic that incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, always reduces to the score statistic in the independent samples situation, and preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe that the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the WGS test statistic in a general GEE setting. Copyright © 2012 John Wiley & Sons, Ltd.     

新型生化灭鼠剂对大白鼠半数致死剂量的研究

目的观察新型生化杀鼠剂对大白鼠的半数致死剂量,为指导用药提供科学依据。方法采用不同剂量、不同时间(d)灌胃的方法测试灭鼠剂的LD50。结果对试验鼠总体LD50为0.204 1,95%可信限为0.150 6~0.276 5。对雄性试验鼠的LD50为0.181 5,95%可信限为0.117 8~0.279 6。对雌性试验鼠的LD50为0.230 2,95%可信限为0.147 0~0.360 4。结论该杀鼠剂可以有效杀灭试验鼠。     

A financial planning model for estimating hospital debt capacity.

A computer-based financial planning model was formulated to measure the impact of a major capital improvement project on the fiscal health of Stanford University Hospital. The model had to be responsive to many variables and easy to use, so as to allow for the testing of numerous alternatives. Special efforts were made to identify the key variables that needed to be presented in the model and to include all known links between capital investment, debt, and hospital operating expenses. Growth in the number of patient days of care was singled out as a major source of uncertainty that would have profound effects on the hospital''s finances. Therefore this variable was subjected to special scrutiny in terms of efforts to gauge expected demographic trends and market forces. In addition, alternative base runs of the model were made under three distinct patient-demand assumptions. Use of the model enabled planners at the Stanford University Hospital (a) to determine that a proposed modernization plan was financially feasible under a reasonable (that is, not unduly optimistic) set of assumptions and (b) to examine the major sources of risk. Other than patient demand, these sources were found to be gross revenues per patient, operating costs, and future limitations on government reimbursement programs. When the likely financial consequences of these risks were estimated, both separately and in combination, it was determined that even if two or more assumptions took a somewhat more negative turn than was expected, the hospital would be able to offset adverse consequences by a relatively minor reduction in operating costs.     

On tests for equality of predictive values for t diagnostic procedures

This paper concerns comparisons of the efficiency of several diagnostic tests, as characterized by the measures of sensitivity (xi), specificity (eta) and predictive value (rho). We show that hypotheses concerning the equality of predictive values relate only to hypotheses concerning xi and eta and that we can test these by approximate chi 2 statistics. Data for the cases of t = 2 or 3 diagnostic tests illustrate the method.     

A dynamic model for estimating changes in health status and costs

We develop an innovative method to assess total treatment costs over a finite period of time while incorporating the dynamics of change in the health status of patients. Costs are incurred through medical care use while patients sojourn in health states. Because complete ascertainment of costs and observation of events are not always feasible, some patient utilization will be incomplete and events will also be censored. A Markov model is used to estimate the transition probabilities between health states and the impact of patient variables on transition intensities. A mixed-effects model is used for sojourn costs with transition times as random effects and patient variables as fixed effects. The models are combined to estimate net present values (NPVs) of expenditures over a finite time interval as a function of patient characteristics. The method is applied to a data set of 624 incident cases of cancer. Physical functioning after cancer diagnosis was assessed periodically through structured interviews. The outcomes of interest are normal physical function, impaired physical function, or the terminal state, dead. Charges were obtained from Medicare claim files for 2 years following cancer diagnosis. For demonstration purposes, we estimate NPVs for charges incurred over 2 years by cancer site and cancer stage. Our method, a joint regression model, provides a flexible approach to assessing the influence of patient characteristics on both cost and health outcomes while accommodating heteroscedasticity, skewness and censoring in the data.     

Confidence intervals for predictive values with an emphasis to case-control studies

The accuracy of a binary-scale diagnostic test can be represented by sensitivity (Se), specificity (Sp) and positive and negative predictive values (PPV and NPV). Although Se and Sp measure the intrinsic accuracy of a diagnostic test that does not depend on the prevalence rate, they do not provide information on the diagnostic accuracy of a particular patient. To obtain this information we need to use PPV and NPV. Since PPV and NPV are functions of both the accuracy of the test and the prevalence of the disease, constructing their confidence intervals for a particular patient is not straightforward. In this paper, a novel method for the estimation of PPV and NPV, as well as their confidence intervals, is developed. For both predictive values, standard, adjusted and their logit transformed-based confidence intervals are compared using coverage probabilities and interval lengths in a simulation study. These methods are then applied to two case-control studies: a diagnostic test assessing the ability of the e4 allele of the apolipoprotein E gene (ApoE.e4) on distinguishing patients with late-onset Alzheimer's disease (AD) and a prognostic test assessing the predictive ability of a 70-gene signature on breast cancer metastasis.     

Positive predictive values for self-reported fractures in an adult Japanese population

Objectives  

Self-reporting provides useful information for assessing the risk factors of osteoporotic fractures in large cohort studies. However, to data, no studies in Japan have confirmed the accuracy of this approach in this context. The aim of the study reported here was to determine the positive predictive value (PPV) for the self-reporting of fractures.     

A predictive model for vapor concentration in a nose-only inhalation chamber

A unique nose-only inhalation chamber was designed and constructed to deliver uniform concentrations of gas, vapor, and aerosol contaminants to mice. This research investigated the fluid dynamics of a vaporous contaminant in the vertical flow chamber. The vapor was introduced by allowing the liquid phase of the contaminant to evaporate freely into the chamber interior. A contaminant mass transfer model was developed to predict concentrations generated by the system. The mathematical model of the system used clean airflow, liquid surface area, thickness of the stagnant air layer covering the liquid, system pressure, contaminant diffusion coefficient, and contaminant vapor pressure to compute the vapor concentration delivered to exposure ports. The equation was verified by placing various containers of methyl isobutyl ketone in the chamber and determining with a photospectrometer the resulting equilibrium concentrations. Vapor pressure, diffusion coefficient, and system pressure were held constant while airflow, surface area, and stagnant air layer thickness were varied systematically within the chamber. The resulting empirical data points were compared to the curves predicted by the theoretical model. Empirical concentrations fell within 0 to 48% of the theoretical values, showing that the equation can be used to choose values for airflow, surface area, and stagnant air layer thickness that will result in chamber concentrations in close proximity to the target concentration. If an exact concentration is essential, parameters may be individually adjusted to converge on the target concentration.     

A model for estimating the impact of changes in children's vaccines.

OBJECTIVES. To assist in strategic planning for the improvement of vaccines and vaccine programs, an economic model was developed and tested that estimates the potential impact of vaccine innovations on health outcomes and costs associated with vaccination and illness. METHODS. A multistep, iterative process of data extraction/integration was used to develop the model and the scenarios. Parameter replication, sensitivity analysis, and expert review were used to validate the model. RESULTS. The greatest impact on the improvement of health is expected to result from the production of less reactogenic vaccines that require fewer inoculations for immunity. The greatest economic impact is predicted from improvements that decrease the number of inoculations required. CONCLUSIONS. Scenario analysis may be useful for integrating health outcomes and economic data into decision making. For childhood infections, this analysis indicates that large cost savings can be achieved in the future if we can improve vaccine efficacy so that the number of required inoculations is reduced. Such an improvement represents a large potential "payback" for the United States and might benefit other countries.     

某口服液对高尿酸血症大鼠模型的影响

目的 探讨某口服液对高尿酸血症大鼠模型的作用。方法 取SD大鼠90只,按血尿酸水平随机分成6组,每组15只,分别为正常对照组、模型对照组、阳性对照组[别嘌醇27.0 mg/(kg·bw)] 、受试口服液低、中、高3个剂量组[1.67、3.33、10.00 ml/(kg·bw)],除正常对照组外,其余各组动物采用腺嘌呤+氧嗪酸钾诱导高尿酸血症,同时给予各受试液连续灌胃30 d,测定血清中尿酸、肌酐、尿素氮水平。取肾进行病理切片。结果 与模型对照组比较,受试口服液低、中、高剂量组实验末期血清尿酸和肌酐水平降低,差异有统计学意义(P<0.01);肾脏病理结果显示,与模型对照组比较,受试口服液中、高剂量组肾小管病变程度缓解。结论 受试口服液对大鼠高尿酸血症具有明显的改善作用。     

A hierarchical model for estimating significance levels of non-parametric linkage statistics for large pedigrees

The significance level of a non-parametric linkage (NPL) statistic is often found by simulation since the distribution of the test statistic is complex and unknown. Ideally, simulation occurs by randomly assigning founder genotypes and then simulating meiotic events for the descendants in the pedigree, commonly referred to as 'gene dropping'. The missing data pattern in the original pedigree, including lack of phase information (due to unordered genotypes), is then imposed on the simulated pedigree. However, this approach is usually computationally infeasible for larger pedigrees which require Markov chain Monte Carlo (MCMC) techniques to calculate the statistic, as an additional MCMC run is required to estimate the statistic for each gene drop. In this work, we propose a novel method to estimate the significance level of the NPL statistic in large pedigrees. This is accomplished by constructing a hierarchical model, which allows estimation of the NPL statistic variability via separate estimation of the Markov chain and gene dropping variability. The significance level is estimated by fitting a parametric model to the statistic and using the method of moments to obtain parameter estimates. In a simulation study we found our hierarchical model estimates to be very close to the gold standard empirical estimates, and offer substantial improvements over the existing conservative method used by the software SimWalk2. The estimation procedure significantly reduces the computational time relative to the ideal empirical estimate, allowing for an accurate estimate of the significance level in a more manageable amount of time.