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Matteo Bassetti,1,2 Mari Ariyasu,3 Bruce Binkowitz,4 Tsutae Den Nagata,3 Roger M Echols,5 Yuko Matsunaga,4 Kiichiro Toyoizumi,4 Yohei Doi6 1Infectious Diseases Clinic, Department of Medicine, University of Udine and Azienda Sanitaria Universitaria Integrata, Udine, Italy; 2Department of Health Sciences, University of Genoa, Genoa, Italy; 3Shionogi & Co., Ltd., Osaka, Japan; 4Shionogi Inc., Florham Park, NJ, USA; 5Infectious Disease Drug Development Consulting, LLC, Easton, CT, USA; 6Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USACorrespondence: Matteo BassettiClinica di Malattie Infettive, Azienda Sanitaria Universitaria Integrata di Udine, Presidio Ospedaliero Universitario Santa Maria della Misericordia, Piazzale Santa Maria della Misericordia 15, Udine 33010, ItalyTel +390432559353Email matteo.bassetti70@gmail.comAbstract: Carbapenem-resistant (CR) Gram-negative infections, including those caused by Enterobacteriaceae and the non-fermenters, represent the greatest unmet need for new effective treatments. The clinical development of new antibiotics for the treatment of CR infections is challenging and should focus on the individual pathogens irrespective of the infection site. However, the drug approval pathway is generally infection-site specific and rarely includes such drug-resistant pathogens. To overcome this limitation, a streamlined clinical development program may include a pathogen-focused clinical study, such as the CREDIBLE-CR study, to meet the expectations of some health authorities (ie, the European Medicines Agency [EMA]) and the medical community. Cefiderocol is a novel siderophore cephalosporin designed to target CR pathogens, including CR strains of Enterobacteriaceae (CRE), Pseudomonas aeruginosa, Acinetobacter baumannii, and also Stenotrophomonas maltophilia, which is intrinsically CR. The CREDIBLE-CR study was planned to evaluate cefiderocol in patients with CR Gram-negative infections regardless of species or infection-site source. Rapid diagnostic testing and/or selective media were provided to facilitate detection of CR pathogens to rapidly enroll patients with nosocomial pneumonia, bloodstream infection/sepsis, or complicated urinary tract infection. Patients were randomized 2:1 to receive cefiderocol or best available therapy. There were no pre-specified statistical hypotheses for this study, as the sample size was driven by enrollment feasibility and not based on statistical power calculations. The objective of the CREDIBLE-CR study was to provide descriptive evidence of the efficacy and safety of cefiderocol for the target population of patients with CR infections, including the non-fermenters. The CREDIBLE-CR study is currently the largest pathogen-focused, randomized, open-label, prospective, Phase 3 clinical study to investigate a new antibiotic in patients with CR Gram-negative infections. Here we describe the design of this pathogen-focused study and steps taken to aid patient enrollment into the study within an evolving regulatory environment.ClinicalTrials.gov registration: NCT02714595.EUDRA-CT registration: 2015-004703-23.Keywords: best available therapy, cefiderocol, carbapenem resistance, pathogen-focused study design, rapid diagnostics, streamlined/limited clinical development

Eight patients with chronic open-angle glaucoma who had an intraocular pressure greater than 24 mm Hg at some time during the day while taking timolol 0.5% twice daily were given the fixed ratio combination of timolol 0.5% with pilocarpine 2% (TP2) twice daily. By comparing full 24-hour diurnal curves on timolol with those on TP2 it was possible to show that all patients except one (at a single timepoint) had an IOP less than 22 mm Hg when changed to TP2. The mean IOPs, the area under the diurnal pressure curve, and the diurnal variation were all significantly lower on TP2 twice daily than on timolol 0.5% twice daily. [ABSTRACT FROM PUBLISHER]

Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the analysis of real-world data has been hitherto under debate and research on visualizing the patient journey is still inconclusive. So far, to classify diseases based on medical histories and patient demographic background and to predict the patient prognosis for each disease, the correlation structure of real-world data has been estimated by machine learning. Therefore, we applied association analysis to real-world data to consider a combination of disease events as the patient journey for depression diagnoses. However, association analysis makes it difficult to interpret multiple outcome measures simultaneously and comprehensively. To address this issue, we applied the Topological Data Analysis (TDA) Mapper to sequentially interpret multiple indices, thus obtaining a visual classification of the diseases commonly associated with depression. Under this approach, the visual and continuous classification of related diseases may contribute to precision medicine research and can help pharmaceutical companies provide appropriate personalized medical care., Competing Interests: YK and MF are salaried employees of Shionogi & Co. and BB is a salaried employee of Shionogi Inc. This does not alter our adherence to all PLOS ONE policies on sharing data and materials.

Inconsistent results across regions have been reported in a number of recent large trials. In this research, by reviewing results from studies that showed inconsistent treatment effects, and summarizing lessons learned, we provide some recommendations for minimizing the chance of inconsistency and allowing more accurate interpretation when such signs of heterogeneity arise, for example: keep the number of regions for consistency evaluation at a minimum to avoid observing false inconsistency signals; proactively address in the protocol the differences in culture, medical practices, and other factors that are potentially different across regions; closely monitor the blinded data from early-enrolled patients to more effectively identify and address issues such as imbalance of baseline covariates or inconsistency of primary outcome rates across regions. For treatments of life-threatening conditions, the stakes for accurate interpretation of MRCT results are high; the criteria for decisions warrant careful consideration.

Serious and chronic health conditions such as cardiovascular diseases (CVDs) are posing challenges to the health system. Recently clinical trials in these fields have focused on composite endpoints that take into account both disease-related mortality and major disease-related morbidity events. It is the time to the first component of the composite endpoint experienced by a patient that is the traditional study endpoint and treatment aims are to delay the time to the first event and to reduce its frequency. As the name implies, the time-to-first composite event analysis approach focuses only on the first composite event and ignores subsequent events. For a chronic disease, this can lead to a substantial loss of potentially important information. For instance, in chronic heart failure (HF) studies, the traditional composite endpoint of HF-related hospitalizations and CVD death will ignore CVD deaths that are preceded by HF-related hospitalizations. This paper explores the limitations of the traditional time-to-first event approach and discusses the potential value of incorporating all events. The authors argue that endpoints capturing recurrent event information can lead to interpretable measures of treatment effect that better reflect disease burden than traditional time-to-first event endpoints by using the available information beyond the first event. This paper aims to raise awareness of the value and potential pitfalls of alternative treatment effect measures to facilitate meaningful cross-functional conversations among trialists and other stakeholders such as regulators, payers, and treating physicians who all are striving to the same goal-to deliver the most effective treatments to patients.

Extensive research has been conducted in the Multi-Regional Clinical Trial (MRCT) area. To effectively apply an appropriate approach to a MRCT, we need to synthesize and understand the features of different approaches. In this paper, examples are used to illustrate considerations regarding design, conduct, analysis and interpretation of result of MRCTs. We start with a brief discussion of region definitions and the scenarios where different regions have differing requirements for a MRCT. We then compare different designs and models as well as the corresponding interpretation of the results. We highlight the importance of paying special attention to trial monitoring and conduct to prevent potential issues associated with the final trial results. Besides evaluating the overall treatment effect for the entire MRCT, we also consider other key analyses including quantification of regional treatment effects within a MRCT, and assessment of consistency of these regional treatment effects., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Multiplicity is an important statistical issue that arises in clinical trials when the efficacy of the test treatment is evaluated in multiple ways. The major concern for multiplicity is that uncontrolled multiple assessments lead to inflated family-wise Type I error, and they thereby undermine the integrity of the statistical inferences. Multiplicity comes from different sources, for example, making inferences either on the overall population or some pre-specified sub-populations, while multiple endpoints need to be evaluated for each population. Therefore, a sound statistical strategy that controls the family-wise Type I error rate in a strong sense, without excessive loss of power from over-control, is crucial for the success of the trial. For a recent phase III cardiovascular trial with such complex multiplicity, we illustrate the use of a closed testing strategy that begins with a global test; and subsequent testing only proceeds when the global test is rejected. Also, we discuss a simulation study based on this trial to compare the power of the illustrated closed testing strategy to some well-known alternative approaches. We found that this strategy can comprehensively meet most of the primary objectives of the trial effectively with reasonably high overall power.

Background: This article describes follow-up work extended from Tanaka et al (2011) in defining the region in multiregional clinical trials (MRCTs). The previous paper advocated a systematic approach to defining regions and recommended the sponsor to think through carefully, prespecify, and justify any regional definitions as well as obtain regulatory concurrence prior to study conduct. Particular attention was advised for intrinsic and extrinsic factors such as race/ethnicity, disease epidemiology, medical practice, and geographic proximity. An analytical approach such as a cluster method to define region was introduced, and references on observed regional differences were provided., Methods: A closer review of those references and others from a more comprehensive literature search was conducted to gain a deeper understanding of intrinsic and extrinsic factors in relation to the observed regional differences in the treatment effect. The application of a k-means cluster method to define region was explored, as well as the implication to regional sample size for MRCTs., Results: Of extrinsic factors, diagnostic capabilities, medical/clinical practice such as aspirin use, patient care such as site's usual practice, intervention, capability, and concomitant medications were considered potential contributors to differences in clinical outcomes or measures. Commonly reported intrinsic factors were underlying etiology, epidemiology, genetics, and patient characteristics (age, ethnicity, race, risk factors)., Conclusion: Using a checklist to identify intrinsic/extrinsic factors that might lead to differences in treatment effect allows one to scientifically define more meaningful regions from the identified factors, which will help with estimating the relative treatment outcome as well as exploring appropriate regional sample size.

We can apply both fixed and random effects models to multi-regional clinical trial (MRCT) design and data analysis. Thoroughly, understanding the features of these models in an MRCT setting will help assessing their applicability to an MRCT. In this paper, we discuss the interpretations of trial results from these models. We also evaluate the impact of the number of regions and the sample size configuration across the regions on the required total sample size for the overall treatment effect assessment. For quantifying treatment effects of individual regions, the empirical shrinkage estimator and the James-Stein type shrinkage estimator associate with smaller variability compared with the regular sample estimator. We conduct computation and simulation to compare the performance of these estimators when they are applied to assess consistency of treatment effects across regions. We use a multinational trial example to illustrate the application of these methods., (Copyright © 2014 John Wiley & Sons, Ltd.)

The use of healthcare databases for comparative effectiveness research (CER) is increasing exponentially despite its challenges. Researchers must understand their data source and whether outcomes, exposures and confounding factors are captured sufficiently to address the research question. They must also assess whether bias and confounding can be adequately minimized. Many study design characteristics may impact on the results; however, minimal if any sensitivity analyses are typically conducted, and those performed are post hoc. We propose pre-study steps for CER feasibility assessment and to identify sensitivity analyses that might be most important to pre-specify to help ensure that CER produces valid interpretable results.

Background: One key objective of a multi-regional clinical trial (MRCT) is to use the trial results to 'bridge' from the global level to local region in support of local registrations. However, data from each individual country are typically limited and the large number of countries will increase the chance of false positive findings., Purpose: Graphical tools to facilitate identification of potential outlying countries could be useful for country-level assessment. Existing methods such as funnel plot and expected range of treatment effect can substantially increase the false positive rate. The expected range approach can also have a very low power when there are a large number of small countries, which is typical in a MRCT., Methods: In this article, we apply normal probability plots, commonly used as a diagnostic tool in linear regression analysis, to assess the differences among countries. Evidence of possible inconsistency, which incorporates both the estimated treatment effect and sample size, is plotted against its expected order statistic., Results: A simulation study is conducted to assess the impact of the negative correlation among residuals due to unequal sample sizes among countries and the performance of the proposed methods compared to existing approaches. The proposed methods tend to have a balanced consideration with substantially smaller false positive rate and reasonable probability to identify outlying countries in realistic scenarios., Limitations: While much lower than that of commonly used methods, the false positive rates of the proposed methods are not strictly controlled. This may be acceptable for these graphical tools with intention to flag potential outliers for investigation., Conclusions: We recommend routine use of normal probability plots in MRCTs as a tool to identify potential outliers. If the normal probability plot is approximately linear but has heavy tails with a few outlying countries, these potential outliers should be examined carefully to understand the possible reasons.

Background: Unexpected regional difference in treatment effect has been reported in recent multiregional clinical trials (MRCTs). This may cause difficulty in interpreting results and can have regulatory implications such as marketing approvals and/or product labels in various markets. Careful consideration of consistency across regions and appropriate plans to address potential regional difference are necessary at the design stage. However, assessment of consistency in treatment effect is generally not the primary objective, and therefore, when there is no strong a priori reason to expect a regional difference, a MRCT is not usually designed to address the regional consistency. Unexpected regional finding may arise and increase the risk of ambiguous or controversial results at the end of the study., Purpose: To mitigate this risk, we propose an adaptive strategy for regional assessment based upon accumulated blinded data., Methods: If review of accumulated blinded data shows unexpectedly severe imbalance in an intrinsic or extrinsic factor, and further assessment indicates that this factor could be a potential effect modifier as supported by biological plausibility or blinded correlation analysis, a stratified regional analysis controlled for this factor may be specified and documented before database lock., Results: The proposed adaptive strategy can help with the interpretation of unexpected regional finding. A recent trial is used to illustrate the approach., Limitations: Even if the imbalanced factor may appear to explain away the regional difference, establishment of causal effect is usually difficult and requires more involved effort., Conclusions: This approach, by prespecifying the stratified analysis, can reduce the risk of post hoc exaggerated emphases across many possible exploratory analyses and provide greater confidence in the validity of the conclusions. If a causal effect can be established that the apparent regional difference is likely caused by this intrinsic or extrinsic factor, this prespecified analysis can also help guide clinical practice.

A key issue in multi-regional clinical trials (MRCTs) is how to assess the consistency of treatment effect across regions, although there is no a priori reason to believe that the treatment effect should vary across the regions. In this article, we define the research question as an assessment of overall consistency across all regions for which all regions are considered equally important. This is different from the region/country-specific analyses (e.g. US vs Non-US), which are frequently requested by local regulatory agencies and usually performed for multiple agencies. We provide a systematic review of methods that may potentially be used for assessing consistency across regions, including commonly used quantitative/qualitative interaction tests, Japanese Pharmaceutical Medical Device Agency (PMDA) Methods 1 & 2, and those proposed for different purposes (e.g. bridging studies, meta-analysis, and vaccine lot consistency, among others). These methods are classified into three groups: global methods, multivariate quantitative methods, and multivariate qualitative methods. A case study is used to illustrate these methods. We also provide recommendations on how to choose appropriate methods and incorporate them in the study design., (Copyright © 2010 John Wiley & Sons, Ltd.)

During the last several decades, the scientific and ethics communities have addressed important ethical issues in medical research, resulting in the elaboration and adoption of concepts, guidelines, and codes. Ethical issues in the conduct of Multiregional Clinical Trials have attracted significant attention mainly in the last two decades. With the globalization of clinical research and the rapid expansion to countries with a limited tradition of biomedical research, sponsors must proactively address local ethical issues, the adequacy of oversight as well as the applicability and validity of data, and scientific conclusions drawn from diverse patient populations. This paper highlights some core ethical principles and milestones in medical research, and, from an industry perspective, it discusses ethical issues that the clinical trial team may face when conducting Multiregional Clinical Trials (MRCT, clinical trials conducted at sites located across multiple geographic regions of the world). This paper further highlights the areas of consensus and controversies and proposes points to consider., (Copyright © 2010 John Wiley & Sons, Ltd.)

Background: Residual cardiovascular (CV) risk often remains high despite statin therapy to lower low-density lipoprotein cholesterol (LDL-C). New therapies to raise high-density lipoprotein cholesterol (HDL-C) are currently being investigated. Anacetrapib is a cholesteryl ester transfer protein (CETP) inhibitor that raises HDL-C and reduces LDL-C when administered alone or with a statin. Adverse effects on blood pressure, electrolytes, and aldosterone levels, seen with another drug in this class, have not been noted in studies of anacetrapib to date., Methods: Determining the EFficacy and Tolerability of CETP INhibition with AnacEtrapib (DEFINE) is a randomized, double-blind, placebo-controlled trial to assess the efficacy and safety profile of anacetrapib in patients with coronary heart disease (CHD) or CHD risk equivalents (clinical trials.gov NCT00685776). Eligible patients at National Cholesterol Education Program-Adult Treatment Panel III LDL-C treatment goal on a statin, with or without other lipid-modifying medications, are treated with anacetrapib, 100 mg, or placebo for 18 months, followed by a 3-month, poststudy follow-up. The primary end points are percent change from baseline in LDL-C and the safety and tolerability of anacetrapib. Comprehensive preplanned interim safety analyses will be performed at the 6- and 12-month time points to examine treatment effects on key safety end points, including blood pressure and electrolytes. A preplanned Bayesian analysis will be performed to interpret the CV event distribution, given the limited number of events expected in this study., Results: A total of 2,757 patients were screened at 153 centers in 20 countries, and 1,623 patients were randomized into the trial. Lipid results, clinical CV events, and safety outcomes from this trial are anticipated in 2010.

The primary aim of a phase I cancer clinical trial is to determine the maximum tolerated dose (MTD) of a new agent. The MTD is determined as the highest dose level of a potential therapeutic agent at which the patients have experienced an acceptable level of dose limiting toxicity. Although many other types of designs have been proposed in recent years, the traditional algorithm-based designs, especially the 3+3 designs, are still widely used due to their practical simplicity. Simulation studies have shown that the traditional algorithm-based designs cannot provide reasonable estimates of the MTD due to their intrinsic design limitations. In this paper, we propose a model-based approach in the estimation of the MTD following a traditional 3+3 design. Simulation results indicate that our model-based approach produces much less biased estimates of the MTD compared to the estimates obtained from the traditional 3+3 designs. Furthermore, our model-based approach can be easily extended to any traditional A+B design., (Copyright (c) 2005 John Wiley & Sons, Ltd.)

To minimize potential controversies in determining the need for multiplicity adjustment for multiple hypotheses, we propose a decision rule based multiplicity adjustment strategy in this paper. Resorting to a predefined decision rule of a clinical trial, one may link the different hypotheses by their logical relationships and divide them into different families. A proper multiplicity adjustment procedure can then be developed by maintaining strong control of Type I error rate within each family. The paper applies the proposed multiplicity adjustment strategy to a published raloxifene clinical trial.

Monte Carlo simulations are commonplace in quantitative risk assessments (QRAs). Designed to propagate the variability and uncertainty associated with each individual exposure input parameter in a quantitative risk assessment, Monte Carlo methods statistically combine the individual parameter distributions to yield a single, overall distribution. Critical to such an assessment is the representativeness of each individual input distribution. The authors performed a literature review to collect and compare the distributions used in published QRAs for the parameters of body weight, food consumption, soil ingestion rates, breathing rates, and fluid intake. To provide a basis for comparison, all estimated exposure parameter distributions were evaluated with respect to four properties: consistency, accuracy, precision, and specificity. The results varied depending on the exposure parameter. Even where extensive, well-collected data exist, investigators used a variety of different distributional shapes to approximate these data. Where such data do not exist, investigators have collected their own data, often leading to substantial disparity in parameter estimates and subsequent choice of distribution. The present findings indicate that more attention must be paid to the data underlying these distributional choices. More emphasis should be placed on sensitivity analyses, quantifying the impact of assumptions, and on discussion of sources of variation as part of the presentation of any risk assessment results. If such practices and disclosures are followed, it is believed that Monte Carlo simulations can greatly enhance the accuracy and appropriateness of specific risk assessments. Without such disclosures, researchers will be increasing the size of the risk assessment "black box," a concern already raised by many critics of more traditional risk assessments.

In the process of designing a clinical trial, the accuracy and precision of an endpoint is of critical importance in being able to determine valid results. In the creation and subsequent testing of the validity of an endpoint, it is desirable to show that on repeated measurements the endpoint can be measured precisely, and that it is reproducible with not only itself, but with any "gold standard" that can assess accuracy. Short of having this gold standard, we rely on showing that the endpoint is reliable. Agreement is the extent to which the measurement of the variable of interest yields the same results (consistency) on repeated trials. Thus the more consistent the results given by repeated measurements, the more reliable the measuring procedure. In this article, we examine the sensitivity to location and scale shift of different methods for assessing agreement. These tools include Pearson's correlation coefficient, the intraclass correlation coefficient, the concordance correlation coefficient, the Bradley-Blackwood procedure, and the within patient coefficient of variation. Our simulation studies showed that there are situations wherein the various methods for assessing agreement give conflicting results. On the basis of the results of the simulation studies, there are three important components to consider when deciding whether two sets of measurements are in agreement or not. The first component is the degree of linear relationship between the two sets; the second is the amount of bias as represented by the difference in the means; and the third is the difference between the two variances. The purpose of this article is to help interpret numerical results from the measures of agreement and to establish a criterion or range of values for each agreement measure that we consider to indicate agreement. We clarify what is meant by agreement by placing the measures in context based on scale and location shifts. Furthermore, we present guidelines for assessing agreement or endpoint validation when there are plans to design a clinical trial, and we give an actual example from a recent study.

Background: Androgenetic alopecia (male pattern hair loss) is caused by androgen-dependent miniaturization of scalp hair follicles, with scalp dihydrotestosterone (DHT) implicated as a contributing cause. Finasteride, an inhibitor of type II 5alpha-reductase, decreases serum and scalp DHT by inhibiting conversion of testosterone to DHT., Objective: Our purpose was to determine whether finasteride treatment leads to clinical improvement in men with male pattern hair loss., Methods: In two 1-year trials, 1553 men (18 to 41 years of age) with male pattern hair loss received oral finasteride 1 mg/d or placebo, and 1215 men continued in blinded extension studies for a second year. Efficacy was evaluated by scalp hair counts, patient and investigator assessments, and review of photographs by an expert panel., Results: Finasteride treatment improved scalp hair by all evaluation techniques at 1 and 2 years (P < .001 vs placebo, all comparisons). Clinically significant increases in hair count (baseline = 876 hairs), measured in a 1-inch diameter circular area (5.1 cm2) of balding vertex scalp, were observed with finasteride treatment (107 and 138 hairs vs placebo at 1 and 2 years, respectively; P < .001). Treatment with placebo resulted in progressive hair loss. Patients' self-assessment demonstrated that finasteride treatment slowed hair loss, increased hair growth, and improved appearance of hair. These improvements were corroborated by investigator assessments and assessments of photographs. Adverse effects were minimal., Conclusion: In men with male pattern hair loss, finasteride 1 mg/d slowed the progression of hair loss and increased hair growth in clinical trials over 2 years.

Background: The Early Intervention Study, a placebo-controlled clinical trial, enrolled approximately 200 patients between age 45-65 years who had mild symptoms associated with early benign hyperplasia (BPH). Prostate volume was measured by magnetic resonance imaging (MRI) and read by the local radiologist, who was blinded to the treatment group but not to the sequence of MRI scans. In order to ascertain whether knowledge of the sequence of the MRI scans by the local radiologist was introducing bias in prostate volume changes, a single radiologist was selected to reread all MRI scans at the end of the study., Methods: Each film was masked as to patient identity, study drug, and date. A new randomization schedule was prepared to blind the single reader to both sequence and treatment. The two sets of readings were compared., Results: Accuracy was dramatically improved when defined as percentage of patients with a 20% or more decrease in prostate volume, from 14% to 0% in placebo patients, and from 38% to 29% in finasteride patients. The variability was significantly reduced (by 50%) when read by a single observer [1]., Conclusions: A single reader, blinded to time sequence as well as to therapy, improves the accuracy and precision of the measurements when compared to multiple readers, blinded only to drug therapy.

Purpose: We attempted to determine the clinical and biological characteristics of familial benign prostatic hyperplasia (BPH)., Materials and Methods: Urinary flow rate, prostate size, symptom score, serum prostate specific antigen, testosterone and dihydrotestosterone were measured in subjects who participated in the nationwide Merck phase III finasteride clinical trial. Findings in the 69 men with familial BPH (3 or more family members with BPH, including the proband) were compared to those in the 345 with no family history of BPH. Logistic regression was used to detect relationships between familial BPH, and these variables before and after 5 alpha-reductase inhibition with finasteride., Results: Familial BPH was characterized by large prostate size. Mean prostate volume in men with familial and sporadic BPH was 82.7 and 55.5 ml., respectively (p < 0.001). Other clinical findings, including serum androgen levels and response to finasteride, were similar in familial and sporadic BPH. The frequency of familial BPH in patients with prostate size in the largest and smallest deciles was 46 and 13%, respectively., Conclusions: Familial BPH in this group of patients was associated with large prostate size, normal serum androgen levels and normal response to 5 alpha-reductase inhibition. A genetic factor responsible for familial BPH may exert its influence through androgen independent control of prostatic growth.

The effects of urinary symptoms on health-related quality of life (HRQL) are important in therapeutic decision making. Few have evaluated the treatment effects on HRQL in men with benign prostatic hyperplasia (BPH), even though increased urinary symptoms are associated with greater worry, bother, and interference with living activities. We report on patient assessments of such disease-specific measures as well as general HRQL measures from two placebo-controlled clinical trials of finasteride in the treatment of symptomatic BPH. Patients treated with finasteride appeared to have greater improvement than placebo-treated patients in disease-specific measures and in patient global assessment. The treated group appeared to have a greater mean increase in sexual domain scores. As expected, general measures (health rating, life satisfaction, ladder of life) changed little. Thus, treatment with finasteride appears to reduce bother, worry, and activity interference due to symptoms but in a small percentage of men may lead to slightly reduced sexual function.

Measurements of American Urological Association symptom score, peak urine flow rate and prostate specific antigen (PSA) are often followed over time in urological management. However, their interpretation is confounded by within-patient variability due to chance. Data from 2 clinical trials are used to examine the magnitude of this variation. When these measures are repeated at a short interval variation is modest and might easily be misinterpreted as a true change in patient condition. For example, approximately 20% of patients might be expected to have a chance increase or decrease in symptom score by at least 4.9 points, in peak urine flow rate by at least 4.1 ml. per second or in PSA by at least 1.6 ng./ml. Clinicians can use these data to help interpret repeated measures of these variables in patients, and can consider obtaining paired measurements to decrease the effect of chance variation when they plan on following them over time.

Systemic beta-blockade after single doses of ophthalmic beta-blockers (one drop in each eye) was investigated in healthy volunteers in two randomized, double-blind, crossover, placebo-controlled studies. beta-Blockade was evaluated by displacement of the bronchodilator (specific airway conductance), positive chronotropic (heart rate), and tremorogenic (finger tremor amplitude) dose-response curve for inhaled isoproterenol. In study 1, 0.5% betaxolol, 0.6% metipranolol, and 0.5% timolol were tested in 16 subjects. Compared with placebo, all beta-blockers resulted in a significant systemic beta-blockade (p greater than 0.05); the increasing order of potency was betaxolol, metipranolol, and timolol. In study 2, 2% butylamino-phenoxy-propanol-acetate (BPPA; a noncardioselective but topically oculoselective drug) and 1% timolol were investigated in 12 subjects. Placebo and BPPA showed no differences (p greater than 0.05), whereas timolol resulted in a significant beta-blockade (p less than 0.05). Topical oculoselectivity is an important aspect of drug safety of beta-blocking eyedrops. Measure of tremor is appropriate to evaluate beta 2-blockade.