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Contains fulltext : 283283.pdf (Publisher’s version ) (Closed access) In early phase clinical studies in oncology, Simon's two-stage designs are widely used. The trial design could be made more efficient by stopping early in the second stage when the required number of responses is reached, or when it has become clear that this target can no longer be met (a form of non-stochastic curtailment). Early stopping, however, will affect proper estimation of the response rate. We propose a uniformly minimum-variance unbiased estimator (UMVUE) for the response rate in this setting. The estimator is proven to be UMVUE using the Rao-Blackwell theorem. We evaluate the estimator's properties in terms of bias and mean squared error, both analytically and via simulations. We derive confidence intervals based on sample space orderings, and assess the coverage. For various design options, we evaluate the reduction in expected sample size as a function of the true response rate. Our method provides a solution for estimating response rates in case of a non-stochastic curtailment Simon's two-stage design.

Contains fulltext : 283289.pdf (Publisher’s version ) (Open Access) Estimands aim to incorporate intercurrent events in design, data collection and estimation of treatment effects in clinical trials. Our aim was to understand what estimands may correspond to efficacy analyses commonly employed in clinical trials conducted before publication of ICH E9(R1). We re-analysed six clinical trials evaluating a new anti-depression treatment. We selected the following analysis methods-ANCOVA on complete cases, following last observation carried forward (LOCF) imputation and following multiple imputation; mixed-models for repeated measurements without imputation (MMRM), MMRM following LOCF imputation and following jump-to-reference imputation; and pattern-mixture mixed models. We included a principal stratum analysis based on the predicted subset of the study population who would not discontinue due to adverse events or lack of efficacy. We translated each analysis into the implicitly targeted estimand, and formulated corresponding clinical questions. We could map six estimands to analysis methods. The same analysis method could be mapped to more than one estimand. The major difference between estimands was the strategy for intercurrent events, with other attributes mostly the same across mapped estimands. The quantitative differences in MADRS10 population-level summaries between the estimands were 4-8 points. Not all six estimands had a clinically meaningful interpretation. Only a few analyses would target the same estimand, hence only few could be used as sensitivity analyses. The fact that an analysis could estimate different estimands emphasises the importance of prospectively defining the estimands targeting the primary objective of a trial. The fact that an estimand can be targeted by different analyses emphasises the importance of prespecifying precisely the estimator for the targeted estimand.

Hereditary proximal spinal muscular atrophy causes weakness and increased fatigability of repetitive motor functions. The neuromuscular junction is anatomically and functionally abnormal in patients with spinal muscular atrophy. Pharmacological improvement of neuromuscular transmission may therefore represent a promising additional treatment strategy. We conducted a Phase II, monocentre, placebo-controlled, double-blind, cross-over trial with the acetylcholinesterase inhibitor pyridostigmine in treatment-naïve patients with spinal muscular atrophy types 2–4. We investigated the safety and efficacy of pyridostigmine on fatigability and motor function. Each participant received pyridostigmine and a placebo for 8 weeks, in random order. Primary outcomes were the repeated nine-hole peg test for fatigability and motor function measure. Secondary outcomes were patient-reported effects, endurance shuttle test combined scores and adverse events. We included 35 patients. For the repeated nine-hole peg test, the mean difference was 0.17 s/trial (95% confidence interval: −1.17–1.49; P = 0.8), favouring placebo, and for the motor function measure, 0.74% (95% confidence interval: 0.00–1.49; P = 0.05), favouring pyridostigmine. Around 74% of patients reported medium-to-large beneficial effects of pyridostigmine on fatigability, compared with 29.7% in the placebo arm. This was paralleled by a reduced dropout risk of 70% on the endurance shuttle test combined scores (hazard ratio: 0.30; 95% confidence interval: 0.15–0.58) under pyridostigmine. Adverse events, mostly mild and self-limiting, occurred more frequently under pyridostigmine. No serious adverse events related to the study medication were observed. Patients with spinal muscular atrophy tolerated pyridostigmine well. There were no significant differences in primary outcomes, but the self-reported reduction of fatigability and improved endurance shuttle test combined score performance suggest that pyridostigmine may be useful as an additional therapy to survival motor neuron-augmenting drugs.Trial registration number: EudraCT: 2011–004369-34, NCT02941328

Amyotrophic lateral sclerosis is a rapidly progressive disease leading to death in, on average, 3-5 years after first symptom onset. Consequently, there are frequently a non-negligible number of patients who die during the course of a clinical trial. This introduces bias in end points such as daily functioning, muscle strength, and quality of life. In this paper, we outline how the choice of strategy to handle death affects the interpretation of the trial results. We provide a general overview of the considerations, positioned in the estimand framework, and discuss the possibility that not every strategy provides a clinically relevant answer in each setting. The relevance of a strategy changes as a function of the intended trial duration, hypothesized treatment effect, and population included. It is important to consider this trade-off at the design stage of a clinical trial, as this will clarify the exact research question that is being answered, and better guide the planning, design, and analysis of the study.

UNSTRUCTURED Despite recent and potent technological advances, the real-world implementation of remote digital health technology in the care and monitoring of patients with motor neuron disease has not yet been realized. Digital health technology may increase the accessibility to and personalization of care, whereas remote biosensors could optimize the collection of vital clinical parameters, irrespective of patients’ ability to visit the clinic. To facilitate the wide-scale adoption of digital health care technology and to align current initiatives, we outline a road map that will identify clinically relevant digital parameters; mediate the development of benefit-to-burden criteria for innovative technology; and direct the validation, harmonization, and adoption of digital health care technology in real-world settings. We define two key end products of the road map: (1) a set of reliable digital parameters to capture data collected under free-living conditions that reflect patient-centric measures and facilitate clinical decision making and (2) an integrated, open-source system that provides personalized feedback to patients, health care providers, clinical researchers, and caregivers and is linked to a flexible and adaptable platform that integrates patient data in real time. Given the ever-changing care needs of patients and the relentless progression rate of motor neuron disease, the adoption of digital health care technology will significantly benefit the delivery of care and accelerate the development of effective treatments.

Although the cost of clinical trials is ever-increasing, in-silico trials, which rely on virtual populations and interventions simulated using patient-specificc models, may offer a solution to contain these costs. However, in-silico trial endpoints need to be compared to those available from conventional clinical trials to ensure that the predictions of safety or effcacy from the in-silico approach are valid. Here, we present the flow diverter performance assessment (FDPASS) in-silico trial, which modelled the treatment of intracranial aneurysms in 82 virtual patients with a flow-diverting stent, using computational fluid dynamics (CFD) to quantify post-treatment flow reduction in the aneurysm sac. The predicted FD-PASS flow-diversion success rate replicated the values previously reported in three reference clinical trials. The in-silico approach allowed broader investigation of factors associated with insuficient flow reduction and increased stroke risk after flow diversion than would be feasible in a conventional trial. These ndings demonstrate for the rst time that in-silico trials of medical devices can (i) replicate ndings of conventional clinical trials and (ii) incorporate virtual experiments that are impossible in conventional trials.

Background: Trends in the detection of suspicious microcalcifications at mammography screening and the yield of these lesions after recall are unknown.Purpose: To determine trends in recall and outcome of screen-detected microcalcifications during 20 years of mammography screening.Materials and Methods: The authors performed a retrospective analysis of a consecutive series of 817 656 screening examinations (January 1997 to January 2017) in a national breast screening program. In 2009-2010 (transition period), screen-film mammography (SFM) was gradually replaced by full-field digital mammography (FFDM). The recalls of suspicious microcalcifications from all radiology reports and pathologic outcome of recalled women with 2-year follow-up were analyzed. Screening outcome in the era of SFM (1997-2008), the transition period (2009-2010), and the era of FFDM (2011-2016) were compared. Trends over time and variations between the SFM and FFDM periods were expressed by using proportions with 95% confidence intervals (CIs). In cases where the analysis based on the CI confirmed clear periods (eg, before and after introduction of FFDM), pre- and postchange outcomes were compared by using chi(2) tests.Results: A total of 18 592 women (median age, 59 years; interquartile range, 14 years) were recalled at mammography screening, 3556 of whom had suspicious microcalcifications. The recall rate for microcalcifications increased from 0.1% in 1997-1998 to 0.5% in 2015-2016 (P,.001). This was temporally associated with the change from SFM to FFDM. The recalls yielding ductal carcinoma in situ (DCIS) increased from 0.3 per 1000 screening examinations with SFM to 1.1 per 1000 screening examinations with FFDM (P Conclusion: The recall rate for suspicious microcalcifications at mammographic screening increased during the past 2 decades, whereas the ductal carcinoma in situ detection rate increased less rapidly, resulting in a lower positive predictive value for recall. (C) RSNA, 2020.

In order for historical data to be considered for inclusion in the design and analysis of clinical trials, prospective rules are essential. Incorporation of historical data may be of particular interest in the case of small populations where available data is scarce and heterogeneity is not as well understood, and thus conventional methods for evidence synthesis might fall short. The concept of power priors can be particularly useful for borrowing evidence from a single historical study. Power priors employ a parametermml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:miγ/mml:mimml:mo∈/mml:momml:mo[/mml:momml:mn0/mml:mnmml:mo,/mml:momml:mn1/mml:mnmml:mo]/mml:mo/mml:maththat quantifies the heterogeneity between the historical study and the new study. However, the possibility of borrowing data from a historical trial will usually be associated with an inflation of the type I error. We suggest a new, simple method of estimating the power parameter suitable for the case when only one historical dataset is available. The method is based on predictive distributions and parameterized in such a way that the type I error can be controlled by calibrating to the degree of similarity between the new and historical data. The method is demonstrated for normal responses in a one or two group setting. Generalization to other models is straightforward.

Apart from their uses in the context of statistical process control, control charts are also used as an exploratory tool in the context of exploratory data analysis. These two application situations are not similar and, as a consequence, control charting methodology should be adjusted to the exploratory context. In this article we make an inventory of the requirements for control charts that are used for exploratory analysis and we propose a procedure that meets these requirements. Robustness against assignable causes of variation appears to be important. The proposed methodology is illustrated from two real-life examples.

The CPMP Points to Consider on Adjustment for Baseline Covariates recently came into operation. As well as providing sound guidance, this document states a strong position against dynamic allocation. This paper reviews the most important issues involved and aims to stimulate interest in further investigating alternative allocation methods in clinical trials. Copyright © 2004 John Wiley & Sons, Ltd.

Observational studies including time-varying treatments are prone to confounding. We compared time-varying Cox regression analysis, propensity score (PS) methods, and marginal structural models (MSMs) in a study of antidepressant [selective serotonin reuptake inhibitors (SSRIs)] use and the risk of hip fracture.A cohort of patients with a first prescription for antidepressants (SSRI or tricyclic antidepressants) was extracted from the Dutch Mondriaan and Spanish Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) general practice databases for the period 2001-2009. The net (total) effect of SSRI versus no SSRI on the risk of hip fracture was estimated using time-varying Cox regression, stratification and covariate adjustment using the PS, and MSM. In MSM, censoring was accounted for by inverse probability of censoring weights.The crude hazard ratio (HR) of SSRI use versus no SSRI use on hip fracture was 1.75 (95%CI: 1.12, 2.72) in Mondriaan and 2.09 (1.89, 2.32) in BIFAP. After confounding adjustment using time-varying Cox regression, stratification, and covariate adjustment using the PS, HRs increased in Mondriaan [2.59 (1.63, 4.12), 2.64 (1.63, 4.25), and 2.82 (1.63, 4.25), respectively] and decreased in BIFAP [1.56 (1.40, 1.73), 1.54 (1.39, 1.71), and 1.61 (1.45, 1.78), respectively]. MSMs with stabilized weights yielded HR 2.15 (1.30, 3.55) in Mondriaan and 1.63 (1.28, 2.07) in BIFAP when accounting for censoring and 2.13 (1.32, 3.45) in Mondriaan and 1.66 (1.30, 2.12) in BIFAP without accounting for censoring.In this empirical study, differences between the different methods to control for time-dependent confounding were small. The observed differences in treatment effect estimates between the databases are likely attributable to different confounding information in the datasets, illustrating that adequate information on (time-varying) confounding is crucial to prevent bias.

The purpose of this study is to evaluate the performance and validity of the case-crossover (CCO) and self-controlled case-series (SCCS) designs when studying the association between hip/femur fracture (HF) and antidepressant (AD) use in general practitioner databases. In addition, comparability with cohort and case-control designs is discussed.Adult patients with HF and who received an AD prescription during 2001-2009 were identified from UK's The Health Improvement Network (THIN) and the Dutch Mondriaan databases. AD exposure was classified into current, recent and past/non-use (reference). In the CCO, for each patient, a case moment (date of HF) and four prior control moments at -91, -182, -273 and -365 days were defined. In SCCS, incidence of HF was compared between exposure states. Conditional logistic regression was used in the CCO and Poisson regression in the SCCS to compute odds ratios and incidence rate ratios, respectively. In CCO, we adjusted for time-varying co-medication and in SCCS for age.Adjusted estimates for the effect of current AD exposure on HF were higher in the CCO (co-medication-adjusted odds ratio, THIN: 2.24, 95% confidence interval [CI]: 2.04-2.47; Mondriaan: 2.57, 95%CI [1.50, 4.43]) than in the SCCS (age-adjusted incidence rate ratio, THIN: 1.41, 95%CI [1.32, 1.49]; Mondriaan: 2.14, 95%CI [1.51, 3.03]). The latter were comparable with the traditional designs.Case-only designs confirmed the association between AD and HF. The CCO design violated assumptions in this study with regard to exchangeability and length of exposure, and transient effects on outcome. The SCCS seems to be an appropriate design for assessing AD-HF association.

Instrumental variable (IV) analysis can control for unmeasured confounding, yet it has not been widely used in pharmacoepidemiology. We aimed to assess the performance of IV analysis using different IVs in multiple databases in a study of antidepressant use and hip fracture.Information on adults with at least one prescription of a selective serotonin reuptake inhibitor (SSRI) or tricyclic antidepressant (TCA) during 2001-2009 was extracted from the THIN (UK), BIFAP (Spain), and Mondriaan (Netherlands) databases. IVs were created using the proportion of SSRI prescriptions per practice or using the one, five, or ten previous prescriptions by a physician. Data were analysed using conventional Cox regression and two-stage IV models.In the conventional analysis, SSRI (vs. TCA) was associated with an increased risk of hip fracture, which was consistently found across databases: the adjusted hazard ratio (HR) was approximately 1.35 for time-fixed and 1.50 to 2.49 for time-varying SSRI use, while the IV analysis based on the IVs that appeared to satisfy the IV assumptions showed conflicting results, e.g. the adjusted HRs ranged from 0.55 to 2.75 for time-fixed exposure. IVs for time-varying exposure violated at least one IV assumption and were therefore invalid.This multiple database study shows that the performance of IV analysis varied across the databases for time-fixed and time-varying exposures and strongly depends on the definition of IVs. It remains challenging to obtain valid IVs in pharmacoepidemiological studies, particularly for time-varying exposure, and IV analysis should therefore be interpreted cautiously.

Instrumental variable (IV) analysis with physician's prescribing preference (PPP) as IV is increasingly used in pharmacoepidemiology. However, it is unclear whether this IV performs consistently across databases. We aimed to evaluate the validity of different PPPs in a study of inhaled long-acting beta2-agonist (LABA) use and myocardial infarction (MI).Information on adults with asthma and/or COPD and at least one prescription of beta2-agonist, or muscarinic antagonist was extracted from the CPRD (UK) and the Mondriaan (Netherlands) databases. LABA exposure was considered time-fixed or time-varying. We measured PPPs using previous LABA prescriptions of physicians or proportion of LABA prescriptions per practice. Correlation (r) and standardized difference (SDif) were used to assess assumption of IV analysis.For time-fixed LABA, the IV based on 10 previous prescriptions outperformed the other IVs regarding strength of the IV (r ≥ 0.15) and balance of confounders between IV categories (SDif 0.10). None of the IVs we considered appeared to be valid for time-varying LABA. In CPRD (n = 490,499), which included approximately 18 times more subjects than Mondriaan (n = 27,459), IVs appeared more valid. LABA was not associated with MI; hazard ratios ranged from 0.86 to 1.18 for conventional analysis, and from 0.61 to 1.24 for the IV analyses with apparent valid IVs.The validity of physician's prescribing preference as IV strongly depends on how this IV is defined and in which database it is applied. Hence, general recommendations cannot be made, other than to generate several plausible IVs, assess their validity, and report the estimate(s) from apparently valid IVs.

Prior event rate ratio (PERR) adjustment method has been proposed to control for unmeasured confounding. We aimed to assess the performance of the PERR method in realistic pharmacoepidemiological settings.Simulation studies were performed with varying effects of prior events on the probability of subsequent exposure and post-events, incidence rates, effects of confounders, and rate of mortality/dropout. Exposure effects were estimated using conventional rate ratio (RR) and PERR adjustment method (i.e. ratio of RR post-exposure initiation and RR prior to initiation of exposure).In the presence of unmeasured confounding, both conventional and the PERR method may yield biased estimates, but PERR estimates appear generally less biased estimates than the conventional method. However, when prior events strongly influence the probability of subsequent exposure, the exposure effect from the PERR method was more biased than the conventional method. For instance, when the effect of prior events on the exposure was RR = 1.60, the effect estimate from the PERR method was RR = 1.13 and from the conventional method was RR = 2.48 (true exposure effect, RR = 2). In all settings, the variation of the estimates was larger for the PERR method than for the conventional method.The PERR adjustment method can be applied to reduce bias as a result of unmeasured confounding. However, only in particular situations, it can completely remove the bias as a result of unmeasured confounding. When applying this method, theoretical justification using available clinical knowledge for assumptions of the PERR method should be provided.

Prior to setting up a designed experiment or setting up a monitoring system for a process most experimenters perform an exploratory study to investigate thestructure of the process' variation. Tools that are frequently used for such a study are predominantly graphical in nature.The multi-vari chart is a useful tool to analyse measurements from a process in which several variance components can be discerned. The chart differentiatesbetween various sources of variation, related to, e.g., time and streams of products. Visually from the chart it can be estimated whether the error variance is consistent over time and whether the other sources of variation are significant compared to the error variation.Instead of making inferences from the chart purely intuitively, this paper establishes more formal statistical tests for the analysis of multi-varicharts. For this purpose, we state a number of hypotheses that emerge frequently in multi-vari studies. To decide on these hypotheses we constructtests based on control charting methodology. Some of the hypotheses and tests are based on a random effects model whereas others are based on a mixed effectsmodel. We study both models seperately.Augmented with these tests, the multi-vari chart turns out to be a powerful extension of the regular control chart.

Statistical process control (SPC) is an important ingredient of quality management. SPC has evolved from a technique to a philosophy, and now includes a large number of techniques and methods directed to quality control and quality improvement. Few processes can be controlled through only one parameter. Hence research into multivariate methods applied to SPC has been undertaken already long ago. Hotelling introduced in 1947 the T2 control chart as a technique for monitoring a multivariate process, and several authors have since elaborated on this. In essence the aim to approach the problem of SPC from a multivariate perspective is to make more efficient use of the data by incorporating the covariances in the model. However, multivariate methods have not gained much popularity. This is due to two important drawbacks: interpretation of out-of-control situations signalled by a multivariate chart is usually difficult and involves further statistical evaluation of the data; and estimation of the process-inherent covariance matrix is sensitive to out-of-control conditions. This paper illustrates some important control chart methods for multivariate problems. A real-world case is analysed by several authors in different ways. From this case and many others we conclude that it is often sufficient to use a few univariate control charts, even though the nature of the process is multivariate. The key argument for this conclusion is that direct interpretation on the shop floor is very important. A brief introduction to the implementation of SPC is given in this paper as well. Copyright © 1999 John Wiley & Sons, Ltd.

Q charts provide means for statistical process control in low-volume processes and start-up phases of production. Concerns on their performance have led to research into different types of enhancements and much discussion on the appropriateness of these. Driven by the aim to implement control charts in the low-volume production of advanced wafer steppers, we investigate the performance of additional run rules and tightening control limits on the traditional Q chart compared with an exponentially weighted moving average (EWMA). Furthermore, we develop an alternative QR chart based on the mean moving range as estimator of the process standard deviation and consider the economics of low-volume processes by means of a specific cost model. The comparisons are based on the run length distributions after a permanent shift and trend, both with an onset early in the process. Real life examples are given for various important variables in wafer stepper production. It is concluded that the EWMA based on QR statistics provides the best performance throughout. Competing alternatives with almost equal performance are the EWMA of Q statistics and the combination of four tests of special causes (1-of-1, 2-of-3, 4-of-5 and 8-of-8) applied on either the Q or QR chart. Overall, the mean moving range performs better. Copyright © 1999 John Wiley & Sons, Ltd.

There are ethical and economic reasons to design and conduct medical research as efficiently as possible. Clinical trials often take a long time because of the number of patients that must be included and the follow-up time in the study. Many data already become available during that time. By making interim analyses of these data it is possible to decide whether a final conclusion can be made. This may save time and may lead to a reduction in the number of patients. The statistical methodology of interim analyses is more complex than a fixed sample analysis and must be planned carefully in advance. The most important methods are sequential analysis and group sequential analysis. Both methods are aimed at a low risk of a false-positive conclusion. In this article we explain the application of these methods as well as considerations in the design of a study and interpretation of the results.

Conditional on the propensity score (PS), treated and untreated subjects have similar distribution of observed baseline characteristics when the PS model is appropriately specified. The performance of several PS balance measures in assessing the balance of covariates achieved by a specific PS model and selecting the optimal PS model was evaluated in simulation studies. However, these studies involved only normally distributed covariates. Comparisons in binary or mixed covariate distributions with rare outcomes, typical of pharmacoepidemiologic settings, are scarce.Monte Carlo simulations were performed to examine the performance of different balance measures in terms of selecting an optimal PS model, thus reduction in bias. The balance of covariates between treatment groups was assessed using the absolute standardized difference, the Kolmogorov-Smirnov distance, the Lévy distance, and the overlapping coefficient. Spearman's correlation coefficient (r) between each of these balance measures and bias were calculated.In large sample sizes (n ≥ 1000), all balance measures were similarly correlated with bias (r ranging between 0.50 and 0.68) irrespective of the treatment effect's strength and frequency of the outcome. In smaller sample sizes with mixed binary and continuous covariate distributions, these correlations were low for all balance measures (r ranging between 0.11 and 0.43), except for the absolute standardized difference (r = 0.51).The absolute standardized difference, which is an easy-to-calculate balance measure, displayed consistently better performance across different simulation scenarios. Therefore, it should be the balance measure of choice for measuring and reporting the amount of balance reached, and for selecting the final PS model.

Results are presented from a meta-analysis performed on short-term efficacy data from five randomized, double-blind, amitriptyline-controlled studies of Org 3770. All participating patients (n = 814) had a DSM III diagnosis of moderate or severe Major Depressive Episode, and the majority of them was hospitalized. Efficacy of treatments was compared on an Intent-to-Treat basis; 732 patients were available for this type of analysis. Primary efficacy variables were changes from baseline in group mean 17-item HAMD scores and percentages of responders. The results of the meta-analysis are in agreement with those from individual studies, consistently showing that there are no statistically nor clinically relevant differences in efficacy of Org 3770 and amitriptyline throughout the study period and at endpoint.

Standard textbooks on statistical process control generally offer simple procedures to construct control charts. In many situations the resulting charts are of limited use because the variance components of the process are not adequately taken into account. In this article, an example of this is discussed. The use of an analysis of variance model in constructing more effective control charts is described. Control charts for the mean and linear contrasts are developed, using different estimators of variability. Guidance with respect to construction and evaluation of the charts is provided. The charts are illustrated with the example, and the efficiency is evaluated.

This double-blind study compared mirtazapine's effects on alertness and sleep between parallel groups treated for 2 weeks according to a fixed regimen of 30 mg at bedtime (N = 69) and one that increased in dose from 15 to 30 mg at bedtime after the first week (N = 71). These patients with depression used an interactive telephone/computer system for daily alertness and sleep recordings on self-rating scales before and during treatment. Efficacy (17-item Hamilton Rating Scale for Depression [HAM-D], Clinical Global Impression Scale [CGI]) and safety assessments were made by participating psychiatrists. Both groups' alertness ratings were subnormal at baseline and even lower after the first dose. The ratings recovered after the second dose and increased progressively to levels 18% higher than those at baseline by the end of treatment. Patients receiving the fixed dose reported earlier sleep onset and longer duration. Similar mean changes in HAM-D scores (approximately -40%) and frequencies of CGI responders (>50%) occurred in both groups. The regimens were equally well tolerated. Somnolence, the most frequent side effect, was reported by only 10% of each group during the first week and by fewer patients during the second. Mirtazapine in fixed and ascending nocturnal dosing regimens was found to facilitate sleep, but it does not generally reduce daytime alertness. The fixed regimen seems preferable because of its greater effects on sleep.