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The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case-control studies, accommodating scenarios with additional matching and non-proportionality. We also consider the case where data for different causes are obtained from different studies conducted in the same cohort. It is demonstrated that while only modest gains in efficiency are possible in full cohort analyses, substantial gains may be attained in nested case-control analyses for failure types that are relatively rare. Extensive simulation studies are conducted and real data analyses are provided using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) study., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: No-shows are detrimental to both patients' health and health care systems. Literature documents no-show rates ranging from 10% in primary care clinics to over 60% in mental health clinics. Our model predicts the probability that a mental health clinic outpatient appointment will not be completed and identifies actionable variables associated with lowering the probability of no-show., Materials and Methods: We were granted access to de-identified administrative data from the Veterans Administration Corporate Data Warehouse related to appointments at 13 Veterans Administration Medical Centers. Our modeling data set included 1,206,271 unique appointment records scheduled to occur between January 1, 2013 and February 28, 2017. The training set included 846,668 appointment records scheduled between January 1, 2013 and December 31, 2015. The testing set included 359,603 appointment records scheduled between January 1, 2016 and February 28, 2017. The dependent binary variable was whether the appointment was completed or not. Independent variables were categorized into seven clusters: patient's demographics, appointment characteristics, patient's attendance history, alcohol use screening score, medications and medication possession ratios, prior diagnoses, and past utilization of Veterans Health Administration services. We used a forward stepwise selection, based on the likelihood ratio, to choose the variables in the model. The predictive model was built using the SAS HPLOGISTIC procedure., Results: The best indicator of whether someone will miss an appointment is their historical attendance behavior. The top three variables associated with higher probabilities of a no-show were: the no-show rate over the previous 2 years before the current appointment, the no-show probability derived from the Markov model, and the age of the appointment. The top three variables that decrease the chance of no-showing were: the appointment was a new consult, the appointment was an overbook, and the patient had multiple appointments on the same day. The average of the areas under the receiver operating characteristic curves was 0.7577 for the training dataset, and 0.7513 for the test set., Conclusions: The National Initiative to Reduce Missed Opportunities-2 confirmed findings that previous patient attendance is one of the key predictors of a future attendance and provides an additional layer of complexity for analyzing the effect of a patient's past behavior on future attendance. The National Initiative to Reduce Missed Opportunities-2 establishes that appointment attendance is related to medication adherence, particularly for medications used for treatment of mood disorders or to block the effects of opioids. However, there is no way to confirm whether a patient is actually taking medications as prescribed. Thus, a low medication possession ratio is an informative, albeit not a perfect, measure. It is our intention to further explore how diagnosis and medications can be better captured and used in predictive modeling of no-shows. Our findings on the effects of different factors on no-show rates can be used to predict individual no-show probabilities, and to identify patients who are high risk for missing appointments. The ability to predict a patient's risk of missing an appointment would allow for both advanced interventions to decrease no-shows and for more efficient scheduling., (© Association of Military Surgeons of the United States 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Treatment approaches for mild traumatic brain injury (mTBI) have evolved to focus on active and targeted therapies, but the effect of compliance with therapy has not been investigated. The purpose of this study was to examine the role of patient compliance with prescribed therapies on clinical outcomes following mTBI., Materials and Methods: Participants were aged 18-60 years with chronic (ie, 6+ months) mTBI symptoms who were previously recalcitrant (n = 66). Participants were diagnosed with a vestibular disorder and were prescribed vestibular and exertion therapies. Participants were instructed to continue the exercise regimen during the 6-month treatment phase at home. Participant compliance was evaluated by clinicians at patients' follow up visit as: (1) high, (2) moderate, or (3) low compliance based on patient report and clinician interview. High-compliance was compared to a combined low- and moderate-compliance group on the outcomes using a 2 (group) × 2 (time) analysis of variance., Results: 39 of the 66 (59%) participants with vestibular disorder returned for a 6-month evaluation and were included in the analyses. Of these 39 participants, 16 (41%) were high-compliance (36.7 ± 10.9 years, 18.8% female), 17 (44%) were moderate-compliance (32.5 ± 5.5 years, 23.5% female), and 6 (15%) were low-compliance (32.7 ± 3.3 years, 0% female)., Conclusion: High compliance significantly reduced total Vestibular/Ocular Motor Screening scores compared to low/middle compliance (P = .005). Post-Concussion Symptom Scale was reduced by 48% and dizziness symptoms reduced by 31% in the high-compliance cohort. High compliance with prescribed exertion/vestibular rehabilitation therapies enhanced clinical outcomes for previously recalcitrant patients with chronic mTBI-related vestibular disorders., (© The Association of Military Surgeons of the United States 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Recently dynamic treatment regimens (DTRs) have drawn considerable attention, as an effective tool for personalizing medicine. Sequential Multiple Assignment Randomized Trials (SMARTs) are often used to gather data for making inference on DTRs. In this article, we focus on regression analysis of DTRs from a two-stage SMART for competing risk outcomes based on cumulative incidence functions (CIFs). Even though there are extensive works on the regression problem for DTRs, no research has been done on modeling the CIF for SMART trials. We extend existing CIF regression models to handle covariate effects for DTRs. Asymptotic properties are established for our proposed estimators. The models can be implemented using existing software by an augmented-data approximation. We show the improvement provided by our proposed methods by simulation and illustrate its practical utility through an analysis of a SMART neuroblastoma study, where disease progression cannot be observed after death., (© The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Noncompliance or non-adherence to randomized treatment is a common challenge when interpreting data from randomized clinical trials. The effect of an intervention if all participants were forced to comply with the assigned treatment (i.e., the causal effect) is often of primary scientific interest. For example, in trials of very low nicotine content (VLNC) cigarettes, policymakers are interested in their effect on smoking behavior if their use were to be compelled by regulation. A variety of statistical methods to estimate the causal effect of an intervention have been proposed, but these methods, including inverse probability of compliance weighted (IPCW) estimators, assume that participants' compliance statuses are reported without error. This is an untenable assumption when compliance is based on self-report. Biomarkers (e.g., nicotine levels in the urine) may provide more reliable indicators of compliance but cannot perfectly discriminate between compliers and non-compliers. However, by modeling the distribution of the biomarker as a mixture distribution and writing the probability of compliance as a function of the mixture components, we show how the probability of compliance can be directly estimated from the data even when compliance status is unknown. To estimate the causal effect, we develop a new approach which re-weights participants by the product of their probability of compliance given the observed data and the inverse probability of compliance given confounders. We show that our proposed estimator is consistent and asymptotically normal and show that in some situations the proposed approach is more efficient than standard IPCW estimators. We demonstrate via simulation that the proposed estimator achieves smaller bias and greater efficiency than ad hoc approaches to estimating the causal effect when compliance is measured with error. We apply our method to data from a recently completed randomized trial of VLNC cigarettes., (© The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Despite clear evidence of important genetic influences on schizophrenia, identifying the genes involved has been difficult because of the genetic complexity of the phenotype. The use of additional phenotypic measures that are more sensitive to the genetic liability than is the clinical diagnosis should enhance the power to detect small individual genetic effects. The present study assessed the neuropsychological performance of 30 male schizophrenia probands, 30 of their unaffected male siblings, and 20 well controls matched on age, sex, and education in order to identify measures that may be particularly sensitive to the genetic liability to schizophrenia and thus may be useful in gene mapping studies. Siblings showed impaired neuropsychological performance compared to controls on four out of the five measures used. Additional results suggested that Trails B was especially effective at discriminating index siblings from controls, thus supporting its potential utility as a candidate quantitative phenotype to aid in gene mapping studies of the disorder.

The proconvulsant effect of biphenyl acetic acid (BPAA) on several fluoroquinolones (FQs) was investigated in vivo, by measuring drug concentrations in the biophase at the onset of convulsions. Male Sprague-Dawley rats (n = 134) were given BPAA orally, at various doses 1 h before starting FQ infusion, which was maintained until the onset of maximal seizures, when cerebrospinal fluid (CSF) and plasma samples were collected for drug concentration determination. The FQ-BPAA interactions in the biophase (CSF) were adequately described on most occasions by an inhibitory Emax effect model with a baseline effect parameter. The efficacy of the proconvulsant effect was characterized by the ratio of the CSF concentrations of FQs at the onset of convulsant activity when BPAA was absent (CCSF0, FQs) and as BPAA CSF concentrations tended toward infinity (CCSFbase, FQs). This ratio varied from 15 for enoxacin to 1.9 for sparfloxacin. The potency of the proconvulsant effect was characterized by the CSF concentration of BPAA corresponding to a proconvulsant effect half of its maximum. This parameter varied between 0.18 +/- 0.06 micromol/L with enoxacin and 15.0 +/- 12.1 micromol/L with sparfloxacin. The CSF diffusion of all FQs was apparently non-linear, as well as the plasma protein binding of BPAA, complicating interpretation of plasma data. The important variability in the proconvulsant effect of BPAA demonstrated in this study between various FQs suggests that in vitro gamma-aminobutyric acid (GABA) binding experiments conducted in the presence of BPAA are unlikely to be good predictors of FQ convulsant risk in clinical practice.

The main objective of this study was to compare the cerebrospinal fluid (CSF) diffusion of imipenem and meropenem at steady state, following intravenous infusions at various rates in rats. A preliminary experiment was conducted to estimate the elimination half-lives of these two carbapenem antibiotics, and then to evaluate the infusion duration necessary to reach steady state. CSF diffusion of imipenem was essentially linear over the wide range of infusion rates (66-1,320microg min(-1)) and corresponding steady-state plasma concentrations (11.7-443.0 microg mL(-1)). Conversely the CSF diffusion of meropenem was saturable, with a predicted maximum CSF concentration equal to 1.3 microg mL(-1). Extrapolation of these data to the clinical situation may not be possible since the rats had normal blood-brain and blood-CSF barriers whereas patients with diseases such as meningitis may not. However, it is suggested that the observed differences in the diffusion characteristics of imipenem and meropenem may be partly responsible for their differences in toxicity and efficacy at the central level.

The effect of tamoxifen on the pharmacokinetics of theophylline was investigated in male Sprague-Dawley rats. The oral administration of tamoxifen at a dose equal to 40 mg kg-1 48 h before the intravenous injection of theophylline 10 mg kg-1, significantly (P < 0.05) increased the clearance of theophylline by 39%, with no apparent effect on the volume of distribution. As a consequence, the elimination half-life of theophylline was significantly (P < 0.05) shortened in the tamoxifen-treated rats (3.56 +/- 0.39 h vs 5.25 +/- 0.48 h) as well as its mean residence time (5.04 +/- 0.60 h vs 7.50 +/- 0.75 h). Although these data cannot be directly extrapolated to the clinical situation, they provide experimental support to suggest that more attention should be paid to the potential risk of pharmacokinetic interactions in the presence of tamoxifen.