Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^

Modeling a health promotion campaign process

In this work we will present a model that describes how the number of healthy and unhealthy subjects that belong to a cohort, changes through time when there are occurrences of health promotion campaigns aiming to change the undesirable behavior. This model also includes immigration and emigration components for each group and a component taking into account when a subject that used to perform a healthy behavior changes to perform the unhealthy behavior. We will express the model in terms of a bivariate probability generating function and in addition we will simulate the model. ^ An illustrative example on how to apply the model to the promotion of condom use among adolescents will be created and we will use it to compare the results obtained from the simulations and the results obtained by the probability generating function. ^

Outcomes and cost effectiveness of treating type 2 diabetes with a nurse case manager following treatment algorithms versus primary care physicians

Background. Diabetes places a significant burden on the health care system. Reduction in blood glucose levels (HbA1c) reduces the risk of complications; however, little is known about the impact of disease management programs on medical costs for patients with diabetes. In 2001, economic costs associated with diabetes totaled $100 billion, and indirect costs totaled $54 billion. ^ Objective. To compare outcomes of nurse case management by treatment algorithms with conventional primary care for glycemic control and cardiovascular risk factors in type 2 diabetic patients in a low-income Mexican American community-based setting, and to compare the cost effectiveness of the two programs. Patient compliance was also assessed. ^ Research design and methods. An observational group-comparison to evaluate a treatment intervention for type 2 diabetes management was implemented at three out-patient health facilities in San Antonio, Texas. All eligible type 2 diabetic patients attending the clinics during 1994–1996 became part of the study. Data were obtained from the study database, medical records, hospital accounting, and pharmacy cost lists, and entered into a computerized database. Three groups were compared: a Community Clinic Nurse Case Manager (CC-TA) following treatment algorithms, a University Clinic Nurse Case Manager (UC-TA) following treatment algorithms, and Primary Care Physicians (PCP) following conventional care practices at a Family Practice Clinic. The algorithms provided a disease management model specifically for hyperglycemia, dyslipidemia, hypertension, and microalbuminuria that progressively moved the patient toward ideal goals through adjustments in medication, self-monitoring of blood glucose, meal planning, and reinforcement of diet and exercise. Cost effectiveness of hemoglobin AI, final endpoints was compared. ^ Results. There were 358 patients analyzed: 106 patients in CC-TA, 170 patients in UC-TA, and 82 patients in PCP groups. Change in hemoglobin A1c (HbA1c) was the primary outcome measured. HbA1c results were presented at baseline, 6 and 12 months for CC-TA (10.4%, 7.1%, 7.3%), UC-TA (10.5%, 7.1%, 7.2%), and PCP (10.0%, 8.5%, 8.7%). Mean patient compliance was 81%. Levels of cost effectiveness were significantly different between clinics. ^ Conclusion. Nurse case management with treatment algorithms significantly improved glycemic control in patients with type 2 diabetes, and was more cost effective. ^

Amyotrophic lateral sclerosis and Parkinsonism-dementia complex of Guam: Descriptive epidemiology, secular trends, and birth cohort effects on incidence, 1950--1989

This study describes the patterns of occurrence of amyotrophic lateral sclerosis (ALS) and parkinsonism-dementia complex (PDC) of Guam during 1950-1989. Both ALS and PDC occur with high frequency among the indigenous Chamorro population, first recognized in the early 1950's. Reports in the early 1980's indicated that both ALS and PDC were disappearing, due to a purported reduction in exposure to harmful environmental factors as a result of the dramatic changes in lifestyle that took place after World War II. However, this study provides compelling evidence that ALS and PDC have not disappeared on Guam and that rates for both are higher during 1980-1989 than previously reported.^ The patterns of occurrence for both ALS and PDC overlap in most respects: (1) incidence and mortality are decreasing; (2) median age at onset is increasing; (3) males are at increased risk for developing disease; (4) risk is higher for those residing in the south compared to the non-south; and (5) age-specific incidence is decreasing over time except in the oldest age groups.^ Age-specific incidence of ALS and PDC, separately and together, is generally higher for cohorts born before 1920 than for those born after 1920. A significant birth cohort effect on the incidence of PDC for the 1906-1915 birth cohort was found, but not for ALS and for ALS and PDC together. Whether or not a cohort effect, period effect, or both are associated with incidence of ALS and PDC cannot be determined from the data currently available and will require additional follow-up of individuals born after 1920.^ The epidemiological data amassed over this 40-year period provide evidence that supports an environmental exposure model for disease occurrence as opposed to a simple genetic or infectious disease model. Whether neurodegenerative disease in this population occurs as a consequence of a single exposure or is explained by a multifactorial model such as a genetic predisposition with some environmental interaction is yet to be determined. However, descriptive studies such as this can provide clues concerning timing and location of potential adverse exposures but cannot determine etiology, underscoring the urgent need for analytic studies of ALS and PDC to further investigate existing etiologic hypotheses and to test new hypotheses. ^

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

Analysis of recurrent failure times: A time-dependent Yule process approach

Analysis of recurrent events has been widely discussed in medical, health services, insurance, and engineering areas in recent years. This research proposes to use a nonhomogeneous Yule process with the proportional intensity assumption to model the hazard function on recurrent events data and the associated risk factors. This method assumes that repeated events occur for each individual, with given covariates, according to a nonhomogeneous Yule process with intensity function λx(t) = λ 0(t) · exp( x′β). One of the advantages of using a non-homogeneous Yule process for recurrent events is that it assumes that the recurrent rate is proportional to the number of events that occur up to time t. Maximum likelihood estimation is used to provide estimates of the parameters in the model, and a generalized scoring iterative procedure is applied in numerical computation. ^ Model comparisons between the proposed method and other existing recurrent models are addressed by simulation. One example concerning recurrent myocardial infarction events compared between two distinct populations, Mexican-American and Non-Hispanic Whites in the Corpus Christi Heart Project is examined. ^

Descriptive epidemiology of cancer in three regions of Kazakhstan

The current study investigated data quality and estimated cancer incidence and mortality rates using data provided by Pavlodar, Semipalatinsk and Ust-Kamenogorsk Regional Cancer Registries of Kazakhstan during the period of 1996–1998. Assessment of data quality was performed using standard quality indicators including internal database checks, proportion of cases verified from death certificates only, mortality:incidence ratio, data patterns, proportion of cases with unknown primary site, proportion of cases with unknown age. Crude and age-adjusted incidence and mortality rates and 95% confidence intervals were calculated, by gender, for all cancers combined and for 28 specific cancer sites for each year of the study period. The five most frequent cancers were identified and described for every population. The results of the study provide the first simultaneous assessment of data quality and standardized incidence and mortality rates for Kazakh cancer registries. ^

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^

Drinking water quality perception along the U.S.-Mexico border

Drinking water-related exposures within populations living in the United States-Mexico border region, particularly among Hispanics, is an area that is largely unknown. Specifically, perceptions that may affect water source selection is an issue that has not been fully addressed. This study evaluates drinking water quality perceptions in a mostly Hispanic community living along the United States-Mexico border, a community also facing water scarcity issues. Using a survey that was administered during two seasons (winter and summer), data were collected from a total of 608 participants, of which 303 were living in the United States and 305 in Mexico. A (random) convenience sampling technique was used to select households and those interviewed were over 18 years of age. Statistically significant differences were observed involving country of residence (p=0.002). Specifically, those living in Mexico reported a higher use of bottled water than those living in the United States. Perception factors, especially taste, were cited as main reasons for not selecting unfiltered tap water as a primary drinking water source. Understanding what influences drinking water source preference can aid in the development of risk communication strategies regarding water quality. ^

DNA nucleotide excision repair gene single nucleotide polymorphisms and hereditary nonpolyposis colorectal cancer

Hereditary nonpolyposis colorectal cancer (HNPCC) is an autosomal dominant disease caused by germline mutations in DNA mismatch repair(MMR) genes. The nucleotide excision repair(NER) pathway plays a very important role in cancer development. We systematically studied interactions between NER and MMR genes to identify NER gene single nucleotide polymorphism (SNP) risk factors that modify the effect of MMR mutations on risk for cancer in HNPCC. We analyzed data from polymorphisms in 10 NER genes that had been genotyped in HNPCC patients that carry MSH2 and MLH1 gene mutations. The influence of the NER gene SNPs on time to onset of colorectal cancer (CRC) was assessed using survival analysis and a semiparametric proportional hazard model. We found the median age of onset for CRC among MMR mutation carriers with the ERCC1 mutation was 3.9 years earlier than patients with wildtype ERCC1(median 47.7 vs 51.6, log-rank test p=0.035). The influence of Rad23B A249V SNP on age of onset of HNPCC is age dependent (likelihood ratio test p=0.0056). Interestingly, using the likelihood ratio test, we also found evidence of genetic interactions between the MMR gene mutations and SNPs in ERCC1 gene(C8092A) and XPG/ERCC5 gene(D1104H) with p-values of 0.004 and 0.042, respectively. An assessment using tree structured survival analysis (TSSA) showed distinct gene interactions in MLH1 mutation carriers and MSH2 mutation carriers. ERCC1 SNP genotypes greatly modified the age onset of HNPCC in MSH2 mutation carriers, while no effect was detected in MLH1 mutation carriers. Given the NER genes in this study play different roles in NER pathway, they may have distinct influences on the development of HNPCC. The findings of this study are very important for elucidation of the molecular mechanism of colon cancer development and for understanding why some mutation carriers of the MSH2 and MLH1 gene develop CRC early and others never develop CRC. Overall, the findings also have important implications for the development of early detection strategies and prevention as well as understanding the mechanism of colorectal carcinogenesis in HNPCC. ^

Association between glycemic index and glycemic load and the risk of incident coronary heart disease among whites and African Americans with and without type 2 diabetes: The Atherosclerosis Risk in Communities study

Several studies have examined the association between high glycemic index (GI) and glycemic load (GL) diets and the risk for coronary heart disease (CHD). However, most of these studies were conducted primarily on white populations. The primary aim of this study was to examine whether high GI and GL diets are associated with increased risk for developing CHD in whites and African Americans, non-diabetics and diabetics, and within stratifications of body mass index (BMI) and hypertension (HTN). Baseline and 17-year follow-up data from ARIC (Atherosclerosis Risk in Communities) study was used. The study population (13,051) consisted of 74% whites, 26% African Americans, 89% non-diabetics, 11% diabetics, 43% male, 57% female aged 44 to 66 years at baseline. Data from the ARIC food frequency questionnaire at baseline were analyzed to provide GI and GL indices for each subject. Increases of 25 and 30 units for GI and GL respectively were used to describe relationships on incident CHD risk. Adjusted hazard ratios for propensity score with 95% confidence intervals (CI) were used to assess associations. During 17 years of follow-up (1987 to 2004), 1,683 cases of CHD was recorded. Glycemic index was associated with 2.12 fold (95% CI: 1.05, 4.30) increased incident CHD risk for all African Americans and GL was associated with 1.14 fold (95% CI: 1.04, 1.25) increased CHD risk for all whites. In addition, GL was also an important CHD risk factor for white non-diabetics (HR=1.59; 95% CI: 1.33, 1.90). Furthermore, within stratum of BMI 23.0 to 29.9 in non-diabetics, GI was associated with an increased hazard ratio of 11.99 (95% CI: 2.31, 62.18) for CHD in African Americans, and GL was associated with 1.23 fold (1.08, 1.39) increased CHD risk in whites. Body mass index modified the effect of GI and GL on CHD risk in all whites and white non-diabetics. For HTN, both systolic blood pressure and diastolic blood pressure modified the effect on GI and GL on CHD risk in all whites and African Americans, white and African American non-diabetics, and white diabetics. Further studies should examine other factors that could influence the effects of GI and GL on CHD risk, including dietary factors, physical activity, and diet-gene interactions. ^

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Persisting abdominal symptoms after travelers' diarrhea: Is there a genetic predisposition?

Functional gastrointestinal disorders (FGIDs) are defined as ailments of the mid or lower gastrointestinal tract which are not attributable to any discernable anatomic or biochemical defects.1 FGIDs include functional bowel disorders, also known as persisting abdominal symptoms (PAS). Irritable bowel syndrome (IBS) is one of the most common illnesses classified under PAS.2,3 This is the first prospective study that looks at the etiology and pathogenesis of post-infectious PAS in the context of environmental exposure and genetic susceptibility in a cohort of US travelers to Mexico. Our objective was to identify infectious, genetic and environmental factors that predispose to post infectious PAS. ^ Methods. This is a secondary data analysis of a prospective study on a cohort of 704 healthy North American tourists to Cuernavaca, Morelos and Guadalajara, Jalisco in Mexico. The subjects at risk for Travelers' diarrhea were assessed for chronic abdominal symptoms on enrollment and six months after the return to the US. ^ Outcomes. PAS was defined as disturbances of mid and lower gastrointestinal system without any known pathological or radiological abnormalities, or infectious, or metabolic causes. It refers to functional bowel disease, category C of functional gastrointestinal diseases as defined by the Rome II criterion. PAS was sub classified into Irritable bowel syndrome (IBS) and functional abdominal disease (FAD). ^ IBS is defined as recurrent abdominal pain or discomfort present at least 25% and associated with improvement with defecation, change in frequency and form of stool. FAD encompasses other abdominal symptoms of chronic nature that do not meet the criteria for IBS. It includes functional diarrhea, functional constipation, functional bloating: and unspecified bowel symptoms. ^ Results. Among the 704 travelers studied, there were 202 cases of PAS. The PAS cases included 175 cases of FAD and 27 cases of IBS. PAS was more frequent among subjects who developed traveler's diarrhea in Mexico compared to travelers who remained healthy during the short term visit to Mexico (52 vs. 38; OR = 1.8; CI, 1.3–2.5, P < 0.001). A statistically significant difference was noted in the mean age of subjects with PAS compared to healthy controls (28 vs. 34 yrs; OR = 0.97, CI, 0.95–0.98; P < 0.001). Travelers who experienced multiple episodes, a later onset of diarrhea in Mexico and passed greater numbers of unformed stools were more likely to be identified in PAS group at six months. Participants who developed TD caused by enterotoxigenic E.coli in Mexico showed a 2.6 times higher risk of developing FAD (P = 0.003). Infection with Providencia ssp. also demonstrated a greater risk to developing PAS. Subjects who sought treatment for diarrhea while in Mexico also displayed a significantly lower frequency of IBS at six months follow up (OR = 0.30; CI, 0.10–0.80; P = 0.02). ^ Forty six SNPs belonging to 14 genes were studied. Seven SNPs were associated with PAS at 6 months. These included four SNPs from the Caspase Recruitment Domain-Containing Protein 15 gene (CARD15), two SNPs from Surfactant Pulmonary-Associated Protein D gene (SFTPD) and one from Decay-Accelerating Factor For Complement gene (CD55). A genetic risk score (GRS) was composed based on the 7 SNPs that showed significant association with PAS. A 20% greater risk for PAS was noted for every unit increase in GRS. The risk increased by 30% for IBS. The mean GRS was high for IBS (2.2) and PAS (1.1) compared to healthy controls (0.51). These data suggests a role for these genetic polymorphisms in defining the susceptibility to PAS. ^ Conclusions. The study allows us to identify individuals at risk for developing post infectious IBS (PI-IBS) and persisting abdominal symptoms after an episode of TD. The observations in this study will be of use in developing measures to prevent and treat post-infectious irritable bowel syndrome among travelers including pre-travel counseling, the use of vaccines, antibiotic prophylaxis or the initiation of early antimicrobial therapy. This study also provides insights into the pathogenesis of post infectious PAS and IBS. (Abstract shortened by UMI.)^