Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

The interplay of psychosis and non-compliance with fatal outcome in an adult with MSUD.

Significant progress has been achieved in enhancing early outcomes for individuals with maple syrup urine disease (MSUD), a rare metabolic disorder that leads to the accumulation of branched-chain amino acids leucine, isoleucine, and valine, where leucine is known as the primary neurotoxic metabolite. Newborn screening is helpful in early diagnosis and implementation of dietary treatment, thus reducing neurological deterioration and complications in young children. However, patients face the life-long challenge of maintaining metabolic control through adherence to a strict low-leucine diet to avoid long-term consequences of chronic hyperleucinemia, which include cognitive deficits, mood disorders, and movement disorders. This case report exemplifies the complex involvement of MSUD in adult survivors. Despite presenting early in life, the patient thrived until the onset of psychiatric symptoms. The subject of this case is a 25-year-old woman with MSUD, who remained in her usual state of health until presentation to the emergency department (ED) with psychosis and altered mental status. However, due to a lack of medical records and poor communication, there was a delay in considering MSUD as a primary cause of her psychiatric symptoms. Although a genetics consultation was later arranged and efforts were made to decrease plasma leucine to the therapeutic range, these interventions proved inadequate in halting her deterioration in health. Her condition worsened within 72 h, culminating in her untimely death. This case emphasizes the comorbidity of psychiatric involvement in MSUD, which contributes to metabolic decompensation that can lead to cerebral edema and death. This case also highlights the pressing need for enhanced strategies for the acute management and long-term care of MSUD patients with psychiatric involvement, particularly in scenarios where mental disturbance could lead to noncompliance.

Risk factors for non-participation in ivermectin and dihydroartemisinin-piperaquine mass drug administration for malaria control in the MASSIV trial.

BACKGROUND: Mass Drug Administration (MDA) has become a mainstay for the control of several diseases over the last two decades. Successful implementation of MDA programmes requires community participation and can be threatened by systematic non-participation. Such concerns are particularly pertinent for MDA programmes against malaria, as they require multi-day treatment over several consecutive months. Factors associated with non-participation to the MDA campaign with ivermectin (IVM) and dihydroartemisinin-piperaquine (DHP) implemented within the MASSIV cluster randomized trial were determined. METHODS: Coverage data was extracted from the MASSIV trial study database, with every datapoint being a directly observed therapy (DOT). A complete month of MDA was classified as receiving all three daily doses of treatment. For both ivermectin and DHP, ordinal logistic regression was used to identify individual and household level variables associated with non-participation. RESULTS: For ivermectin, 51.5% of eligible participants received all 3 months of treatment while 30.7% received either one or two complete months. For DHP, 56.7% of eligible participants received all 3 months of treatment and 30.5% received either one or two complete months. Children aged 5-15 years and adults aged more than 50 years were more likely to receive at least one complete month of MDA than working age adults, both for ivermectin (aOR 4.3, 95% CI 3.51-5.28 and aOR of 2.26, 95% CI 1.75-2.95) and DHP (aOR 2.47, 95%CI 2.02-3.02 and aOR 1.33, 95%CI 1.01-1.35), respectively. Members of households where the head received a complete month of MDA were more likely to themselves have received a complete month of MDA, both for ivermectin (aOR 1.71, 95%CI 1.35-2.14) and for DHP (aOR 1.64, 95%CI 1.33-2.04). CONCLUSION: Personal and household-level variables were associated with participation in the MDA programme for malaria control. Specific strategies to (increase participation amongst some groups may be important to ensure maximum impact of MDA strategies in achieving malaria elimination. TRIAL REGISTRATION: The MASSIV trial is registered under NCT03576313.

Principal stratification analysis of noncompliance with time-to-event outcomes.

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325 mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.

How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study.

A coordinated testing policy is an essential tool for responding to emerging epidemics, as was seen with COVID-19. However, it is very difficult to agree on the best policy when there are multiple conflicting objectives. A key objective is minimizing cost, which is why pooled testing (a method that involves pooling samples taken from multiple individuals and analyzing this with a single diagnostic test) has been suggested. In this article, we present results from an extensive and realistic simulation study comparing testing policies based on individually testing subjects with symptoms (a policy resembling the UK strategy at the start of the COVID-19 pandemic), individually testing subjects at random or pools of subjects randomly combined and tested. To compare these testing methods, a dynamic model compromised of a relationship network and an extended SEIR model is used. In contrast to most existing literature, testing capacity is considered as fixed and limited rather than unbounded. This article then explores the impact of the proportion of symptomatic infections on the expected performance of testing policies. Symptomatic testing performs better than pooled testing unless a low proportion of infections are symptomatic. Additionally, we include the novel feature for testing of non-compliance and perform a sensitivity analysis for different compliance assumptions. Our results suggest for the pooled testing scheme to be superior to testing symptomatic people individually, only a small proportion of the population ( > 10 % $$ >10\% $$ ) needs to not comply with the testing procedure.

An assessment of potential interventions to reduce the totoaba illegal trade market.

The illegal trade in totoaba (Totoaba macdonaldi) is causing adverse social, ecological, and economic impacts. This illegal activity is accelerating the overexploitation of totoaba and pushing the critically endangered vaquita (Phocoena sinus) closer to extinction. Despite extensive efforts to recover vaquita populations, scant attention has been given to the totoaba trade as an independent issue. As a result, data on the totoaba trade are limited, which hampers robust analyses and development of effective interventions to reduce illegal harvesting. We used a previously developed framework specifically designed to examine dynamics of illegal markets and guide measures to mitigate illegal use of totoaba. This framework separates markets into 3 analytical levels: characterization of participating actors (e.g., fishers, intermediaries); examination of how actors interact within the market (e.g., organization of supply chains); and assessment of the overall market dynamics that result from these interactions (e.g., factors determining price and quantity). We reviewed existing literature (108 initial articles) and interviewed key market actors, academics, and nongovernmental organization experts (14) to obtain data for this framework. Our findings offer an overview of the totoaba illegal market operation, highlighting intervention points (e.g., customs agents) and areas where additional information is required to decrease information gaps (e.g., US local market). We describe the structure and complexity of this market, emphasizing the influential role of organized crime in shaping its dynamics (e.g., controlling prices paid to fishers and stockpiling). By providing a systematic and in-depth understanding of the market operation, we aimed to establish a benchmark for effective interventions and future research aimed at reducing uncertainties. Our results provide a crucial step toward addressing this critical issue and can help facilitate development of effective strategies to combat the illegal totoaba trade and promote biodiversity conservation more broadly.

Evaluación de las intervenciones potenciales para reducir el mercado ilegal de la totoaba Resumen El mercado ilegal de totoaba (Totoaba macdonaldi) causa impactos sociales, ecológicos y económicos adversos. Esta actividad ilegal acelera la sobreexplotación de la totoaba y acerca a la extinción a la vaquita marina (Phocoena sinus), especie en peligro crítico de extinción. A pesar de los grandes esfuerzos por recuperar las poblaciones de vaquita, el comercio de totoaba recibe poca atención como problema independiente. Como resultado, los datos sobre este comercio son limitados, lo que dificulta el análisis sólido y el desarrollo de intervenciones eficaces para reducir la captura ilegal. Utilizamos un marco desarrollado previamente y diseñado específicamente para examinar la dinámica de los mercados ilegales y orientar las medidas para mitigar el uso ilegal de la totoaba. Este marco separa los mercados en tres niveles analíticos: caracterización de los actores participantes (por ejemplo, pescadores, intermediarios); análisis de cómo interactúan los actores dentro del mercado (por ejemplo, organización de las cadenas de suministro); y evaluación de la dinámica general del mercado que resulta de estas interacciones (por ejemplo, factores que determinan el precio y la cantidad). Revisamos la bibliografía existente (108 artículos iniciales) y entrevistamos a actores clave del mercado, académicos y expertos de organizaciones no gubernamentales (14) para obtener datos para este marco. Nuestras conclusiones ofrecen una visión general del funcionamiento del mercado ilegal de totoaba y destacan los puntos de intervención (por ejemplo, los agentes aduanales) y las áreas en las que se requiere información adicional para reducir los vacíos informativos (por ejemplo, el mercado local estadunidense). Describimos la estructura y complejidad de este mercado, destacando el influyente papel de la delincuencia organizada en la configuración de su dinámica (por ejemplo, controlando los precios pagados a los pescadores y el almacenamiento). Al proporcionar una comprensión sistemática y en profundidad del funcionamiento del mercado, pretendemos establecer un punto de referencia para intervenciones eficaces y futuras investigaciones encaminadas a reducir las incertidumbres. Nuestros resultados suponen un paso crucial para abordar esta cuestión crítica y pueden ayudar a facilitar el desarrollo de estrategias eficaces para combatir el comercio ilegal de totoaba y promover la conservación de la biodiversidad de forma más amplia.

Underreporting of non-study cigarette use by study participants confounds the interpretation of results from ambulatory clinical trial of reduced nicotine cigarettes.

BACKGROUND: As part of its comprehensive plan to significantly reduce the harm from tobacco products, the US Food and Drug Administration is establishing a product standard to lower nicotine in conventional cigarettes to make them "minimally addictive or non-addictive". Many clinical studies have investigated the potential impact of such a standard on smoking behavior and exposure to cigarette constituents. These ambulatory studies required participants who smoke to switch to reduced nicotine study cigarettes. In contrast to clinical trials on pharmaceuticals or medical devices, participants had ready access to non-study conventional nicotine cigarettes and high rates of non-study cigarette use were consistently reported. The magnitude of non-compliance, which could impact the interpretation of the study results, was not adequately assessed in these trials. METHODS: We conducted a secondary analysis of data from a large, randomized trial of reduced nicotine cigarettes with 840 participants to estimate the magnitude of non-compliance, i.e., the average number of non-study cigarettes smoked per day by study participants assigned to reduced nicotine cigarettes. Individual participants' non-study cigarette use was estimated based on his/her urinary total nicotine equivalent level, the nicotine content of the study cigarette assigned and the self-reported number of cigarettes smoked, using a previously published method. RESULTS: Our analysis showed that (1) there is a large variation in the number of non-study cigarettes smoked by participants within each group (coefficient of variation 90-232%); (2) participants in reduced nicotine cigarette groups underreported their mean number of non-study cigarettes smoked per day by 85-91%; and (3) the biochemical-based estimates indicate no reduction in the mean number of total cigarettes smoked per day for any group assigned to reduced nicotine cigarettes after accounting for non-study cigarettes. CONCLUSIONS: High levels of non-compliance, in both the rate and magnitude of non-study cigarette use, are common in ambulatory reduced nicotine cigarette trials where participants have access to conventional nicotine non-study cigarettes. The potential impact of high non-compliance on study outcomes should be considered when interpreting the results from such ambulatory studies.

Professionalism in Hand Surgery: Treating the Noncompliant Patient.

Every practicing hand surgeon has had the challenging experience of treating a patient who demonstrates difficulty with, or inability to comply with medical advice. Patient noncompliance can lead to not only poor patient outcomes but also deterioration in the therapeutic relationship, physician burnout, high cost of care, and medical-legal risk. The guiding principles in the ethical practice of medicine render it important to consider noncompliance as a potentially modifiable risk factor, and every attempt should be made to work with these noncompliant patients to achieve the best possible outcomes. Data suggest that noncompliance may be affected by socioeconomic status and race; many of these patients are among the vulnerable. However, in some instances, treatment options may warrant alteration or adjustment to reflect the noncompliance of the patient. Rarely, it may be reasonable for a physician to discharge a patient from care once any urgent problems have been managed. Ethical and responsible management of a noncompliant patient requires a thoughtful and measured approach.

Lived experiences of adults' non-compliance with psychiatric medication for depression.

Background: Non-compliance with psychiatric medication among patients diagnosed with depression ranges from 28% to 52% exacerbating illness and reducing treatment effectiveness. There is a paucity of research on medication non-compliance and its causes in South Africa and globally, and an urgent need to develop appropriate interventions. Aim: This study aimed to explore and describe the experiences of adults living with depression who are non-compliant with their psychiatric medication and formulate recommendations to facilitate their medication compliance. Setting: The study was conducted in a psychiatric ward at a public hospital in Gauteng, South Africa. Methods: The study employed a qualitative, exploratory, descriptive and contextual research design. Ten adults' lived experiences were explored using in-depth individual interviews, and Tech's coding method was used to analyse data. Results: Two themes emerged from the data: adults living with major depression offered several reasons for non-compliance, and adults living with major depression experienced non-compliance, which created a setback to their recovery. Conclusion: Non-compliance with medication is a common challenge among adults receiving mental health care and treatment. Ensuring compliance to medication is crucial for improving the prognosis of psychiatric conditions. Therefore, it is essential for healthcare practitioners in the field of psychiatry to have a comprehensive understanding of medication compliance and to effectively address any challenges that may arise in this area. Contribution: This paper contributes to the research field and adds knowledge to clinical nursing practice by exploring adults' experiences with non-compliance to psychiatric medications while living with depression in the South African context.

Estimating the Effect of a Treatment When There Is Nonadherence in a Trial.

Randomized trials offer a powerful strategy for estimating the effect of a treatment on an outcome. However, interpretation of trial results can be complicated when study subjects do not take the treatment to which they were assigned; this is referred to as nonadherence. Prior authors have described instrumental variable approaches to analyze trial data with nonadherence; under their approaches, the initial assignment to treatment is used as an instrument. However, their approaches require the assumption that initial assignment to treatment has no direct effect on the outcome except via the actual treatment received (i.e., the exclusion restriction), which may be implausible. We propose an approach to identification of a causal effect of treatment in a trial with 1-sided nonadherence without assuming exclusion restriction. The proposed approach leverages the study subjects initially assigned to control status as an unexposed reference population; we then employ a bespoke instrumental variable analysis, where the key assumption is "partial exchangeability" of the association between a covariate and an outcome in the treatment and control arms. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical application.

Instrumental variable analysis for cost outcome: Application to the effect of primary care visit on medical cost among low-income adults.

Medical cost data often consist of zero values as well as extremely right-skewed positive values. A two-part model is a popular choice for analyzing medical cost data, where the first part models the probability of a positive cost using logistic regression and the second part models the positive cost using a lognormal or Gamma distribution. To address the unmeasured confounding in studies on cost outcome under two-part models, two instrumental variable (IV) methods, two-stage residual inclusion (2SRI) and two-stage prediction substitution (2SPS) are widely applied. However, previous literature demonstrated that both the 2SRI and the 2SPS could fail to consistently estimate the causal effect among compliers under standard IV assumptions for binary and survival outcomes. Our simulation studies confirmed that it continued to be the case for a two-part model, which is another nonlinear model. In this article, we develop a model-based IV approach, Instrumental Variable with Two-Part model (IV2P), to obtain a consistent estimate of the causal effect among compliers for cost outcome under standard IV assumptions. In addition, we develop sensitivity analysis approaches to allow the evaluation of the sensitivity of the causal conclusions to potential quantified violations of the exclusion restriction assumption and the randomization of IV assumption. We apply our method to a randomized cash incentive study to evaluate the effect of a primary care visit on medical cost among low-income adults newly covered by a primary care program.

A systematic review of simulation studies which compare existing statistical methods to account for non-compliance in randomised controlled trials.

INTRODUCTION: Non-compliance is a common challenge for researchers and may reduce the power of an intention-to-treat analysis. Whilst a per protocol approach attempts to deal with this issue, it can result in biased estimates. Several methods to resolve this issue have been identified in previous reviews, but there is limited evidence supporting their use. This review aimed to identify simulation studies which compare such methods, assess the extent to which certain methods have been investigated and determine their performance under various scenarios. METHODS: A systematic search of several electronic databases including MEDLINE and Scopus was carried out from conception to 30th November 2022. Included papers were published in a peer-reviewed journal, readily available in the English language and focused on comparing relevant methods in a superiority randomised controlled trial under a simulation study. Articles were screened using these criteria and a predetermined extraction form used to identify relevant information. A quality assessment appraised the risk of bias in individual studies. Extracted data was synthesised using tables, figures and a narrative summary. Both screening and data extraction were performed by two independent reviewers with disagreements resolved by consensus. RESULTS: Of 2325 papers identified, 267 full texts were screened and 17 studies finally included. Twelve methods were identified across papers. Instrumental variable methods were commonly considered, but many authors found them to be biased in some settings. Non-compliance was generally assumed to be all-or-nothing and only occurring in the intervention group, although some methods considered it as time-varying. Simulation studies commonly varied the level and type of non-compliance and factors such as effect size and strength of confounding. The quality of papers was generally good, although some lacked detail and justification. Therefore, their conclusions were deemed to be less reliable. CONCLUSIONS: It is common for papers to consider instrumental variable methods but more studies are needed that consider G-methods and compare a wide range of methods in realistic scenarios. It is difficult to make conclusions about the best method to deal with non-compliance due to a limited body of evidence and the difficulty in combining results from independent simulation studies. PROSPERO REGISTRATION NUMBER: CRD42022370910.

The Trial within Cohorts (TwiCs) study design in oncology: experience and methodological reflections.

A Trial within Cohorts (TwiCs) study design is a trial design that uses the infrastructure of an observational cohort study to initiate a randomized trial. Upon cohort enrollment, the participants provide consent for being randomized in future studies without being informed. Once a new treatment is available, eligible cohort participants are randomly assigned to the treatment or standard of care. Patients randomized to the treatment arm are offered the new treatment, which they can choose to refuse. Patients who refuse will receive standard of care instead. Patients randomized to the standard of care arm receive no information about the trial and continue receiving standard of care as part of the cohort study. Standard cohort measures are used for outcome comparisons. The TwiCs study design aims to overcome some issues encountered in standard Randomized Controlled Trials (RCTs). An example of an issue in standard RCTs is the slow patient accrual. A TwiCs study aims to improve this by selecting patients using a cohort and only offering the intervention to patients in the intervention arm. In oncology, the TwiCs study design has gained increasing interest during the last decade. Despite its potential advantages over RCTs, the TwiCs study design has several methodological challenges that need careful consideration when planning a TwiCs study. In this article, we focus on these challenges and reflect on them using experiences from TwiCs studies initiated in oncology. Important methodological challenges that are discussed are the timing of randomization, the issue of non-compliance (refusal) after randomization in the intervention arm, and the definition of the intention-to-treat effect in a TwiCs study and how this effect is related to its counterpart in standard RCTs.

Fentanyl-Induced Rigid Chest Syndrome in Critically Ill Patients.

BACKGROUND: Opioid induced chest wall rigidity was first described in the early 1950s during surgical anesthesia and has often been referred to as fentanyl induced rigid chest syndrome (FIRCS). It has most commonly been described in the setting of procedural sedation and bronchoscopy, characterized by pronounced abdominal and thoracic rigidity, asynchronous ventilation, and respiratory failure. FIRCS has been infrequently described in the setting of continuous analgesia in critically ill adult patients. We postulate that FIRCS can occur in this setting and is likely under recognized, leading to increased morbidity and mortality. METHODS: Patients admitted to the intensive care unit with suspected FIRCS were included in this retrospective analysis. The objective of this analysis is to describe the clinical presentation and treatment strategies for FIRCS. RESULTS: Forty-two patients exhibiting symptoms of FIRCS were included in this analysis. Twenty-two of the forty-two patients with descriptive documentation had evidence of thoracic or abdominal rigidity on examination (52.4%). Twelve of sixteen (75%) patients treated solely with naloxone had documented ventilator compliance following intervention, compared to six of eleven (55%) managed with cisatracurium alone. Nine of twelve patients who ultimately received naloxone after initial treatment with cisatracurium had documented ventilator compliance following naloxone administration (75%). Standard interventions, including sedation optimization and ventilator adjustments were attempted to rule out and treat other potential causes of dyssynchrony. In most cases, the administration of naloxone resulted in appropriate compliance with both ventilator and patient-initiated breaths, suggesting the ventilator dyssynchrony was due to fentanyl. CONCLUSIONS: This is the largest case series to date describing FIRCS in the intensive care setting. Recognition and prompt management is necessary for improved patient outcomes. Research is needed to increase awareness and recognition, identify patient risk factors, and analyze the efficacy and safety of interventions.

Weaving rapport: doctors' strategies towards patients' noncompliance.

BACKGROUND: A successful therapeutic rapport between doctors and patients is built on effective doctor-patient communication. Noncompliance of patients which challenges their communication has been described in the research, yet the rapport strategies are not well discussed. METHODS: This qualitative study investigates the rapport strategies when doctors face noncompliance in consultations and its pragmatic effects achieved through the doctors' speeches. The 10-hour recordings come from the doctor-patient communication in the hospital setting. Thereafter, we analyze their conversation following the Spencer Oatey's rapport management model. RESULTS: Compliments and joking in the illocutionary domain, storytelling in the discourse domain, the doctors' participation in the participation domain and the choice of appropriate titles in the stylistic domain are identified and analyzed as the rapport-building strategies. CONCLUSION: The present study has offered insights into physicians' rapport-building strategies in the face of rapport-threatening behavior from patients. These strategies will help the doctors to deal with rapport-challenging behavior and boost overall patient wellness.

Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach.

BACKGROUND: Medication noncompliance is a critical issue because of the increased number of drugs sold on the web. Web-based drug distribution is difficult to control, causing problems such as drug noncompliance and abuse. The existing medication compliance surveys lack completeness because it is impossible to cover patients who do not go to the hospital or provide accurate information to their doctors, so a social media-based approach is being explored to collect information about drug use. Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients. OBJECTIVE: This study aimed to assess how the structural similarity of drugs affects the efficiency of machine learning models for text classification of drug noncompliance. METHODS: This study analyzed 22,022 tweets about 20 different drugs. The tweets were labeled as either noncompliant use or mention, noncompliant sales, general use, or general mention. The study compares 2 methods for training machine learning models for text classification: single-sub-corpus transfer learning, in which a model is trained on tweets about a single drug and then tested on tweets about other drugs, and multi-sub-corpus incremental learning, in which models are trained on tweets about drugs in order of their structural similarity. The performance of a machine learning model trained on a single subcorpus (a data set of tweets about a specific category of drugs) was compared to the performance of a model trained on multiple subcorpora (data sets of tweets about multiple categories of drugs). RESULTS: The results showed that the performance of the model trained on a single subcorpus varied depending on the specific drug used for training. The Tanimoto similarity (a measure of the structural similarity between compounds) was weakly correlated with the classification results. The model trained by transfer learning a corpus of drugs with close structural similarity performed better than the model trained by randomly adding a subcorpus when the number of subcorpora was small. CONCLUSIONS: The results suggest that structural similarity improves the classification performance of messages about unknown drugs if the drugs in the training corpus are few. On the other hand, this indicates that there is little need to consider the influence of the Tanimoto structural similarity if a sufficient variety of drugs are ensured.

Stigmatization and Self-Perception regarding issues related to Mental Health: A qualitative survey from a lower and middle-income country.

Objective: To assess the understanding of the patients with common mental disorders, towards issues related to their mental health. Methods: This qualitative study was conducted from December 2018 to April 2020. Thirty-four patients, suffering from common mental disorders, were interviewed in public and private sector hospitals of Peshawar. The interviews were recorded, transcribed, translated into English, and themes were generated from their responses. Content analysis was carried out on the data obtained. The themes resulting from each interview were then further comparatively analyzed. Results: The mean age of the sample was 31.9±10.61 years. Most of the patients (n=24, 70.6%) were aware that the nature of their illness was a psychological one with a majority (n=17, 50%) describing it with the symptoms of headache or burden on the head. Most of the patients (n=14, 41.1%) were unaware of the general public opinion towards mental disorders but those who were aware described these with stigmatizing descriptions e.g., "people call them crazy" etc. Most of the patients (n=20, 58.8%) were unaware about their own opinion regarding their illness and some said that they tried to conceal their illness from others. Unfortunately, most of the patients (n=19, 55.8%) were not aware of mental healthcare professionals or the existence of psychiatry as a profession. Conclusion: Stigma, both public and personal, was quite high, which caused patients to feel compelled to conceal their illness. There was also a general lack of knowledge with regard to mental disorders in our society. The general public opinion about mental health professionals was not favorable.

Estimating the Complier Average Causal Effect in a Meta-Analysis of Randomized Clinical Trials With Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach.

Noncompliance, a common problem in randomized clinical trials (RCTs), can bias estimation of the effect of treatment receipt using a standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that would comply with their assigned treatment. Although several methods have been developed to estimate the CACE in analyzing a single RCT, methods for estimating the CACE in a meta-analysis of RCTs with noncompliance await further development. This article reviews the assumptions needed to estimate the CACE in a single RCT and proposes a frequentist alternative for estimating the CACE in a meta-analysis, using a generalized linear latent and mixed model with SAS software (SAS Institute, Inc.). The method accounts for between-study heterogeneity using random effects. We implement the methods and describe an illustrative example of a meta-analysis of 10 RCTs evaluating the effect of receiving epidural analgesia in labor on cesarean delivery, where noncompliance varies dramatically between studies. Simulation studies are used to evaluate the performance of the proposed method.

Dialysis Nonadherence and Kidney Transplant Outcomes: A Retrospective Cohort Study.

RATIONALE & OBJECTIVE: Concerns about nonadherent behaviors often prevent dialysis patients from entering waitlists for transplant even though there is an inconsistent association of these behaviors with posttransplant outcomes. We examined the association between plausible metrics of nonadherence related to dialysis treatment and posttransplant outcomes. STUDY DESIGN: Retrospective cohort. We linked national dialysis treatment data with transplant registry data. SETTING AND PARTICIPANTS: Adult patients receiving maintenance hemodialysis from January 1, 2004, through December 31, 2014, who received a kidney transplant at a US center. EXPOSURES: We examined 5 nonadherence metrics: serum potassium level (≥5.2 mEq/L), serum phosphorus level (>5.5 mg/dL), interdialytic weight gain (IDWG; ≥5 L), shortened treatments (≥30 min), and missed treatments (≥1); missed treatment data were available only for 2004-2009. These metrics were characterized per proportion of time under observation. Dialysis observation time was divided into 3-month intervals (quarters), and the number of nonadherent measurements in each domain was calculated for each quarter. OUTCOMES: Allograft loss, mortality, and acute rejection in the first posttransplant year. ANALYTICAL APPROACH: Using Cox proportional hazards and logistic regression, we estimated the hazard ratios for graft loss and mortality and odds ratios for rejection. RESULTS: 9,543 patients met inclusion criteria. In our primary model, hyperphosphatemia (adjusted hazard ratio [aHR], 1.27 [95% CI, 1.08-1.49]), large IDWG (aHR, 1.39 [95% CI, 1.23-1.59]), and shortened treatments (aHR, 1.54 [95% CI, 1.12-2.13]) were associated with greater rates of allograft loss, but hyperkalemia was not. Large IDWG (aHR, 1.49 [95% CI, 1.29-1.73]) and shortened treatments (aHR, 1.34 [95% CI, 1.13-1.58]) were associated with mortality, whereas hyperkalemia and hyperphosphatemia were not. Only shortened treatments were associated with an increased risk of acute rejection (adjusted odds ratio, 3.88 [95% CI, 1.98-7.58]). In models limited to the years 2004-2009 that included missed treatments, missed treatments were associated only with mortality. LIMITATIONS: Unmeasured confounding (eg, dietary data); adherence metrics used may have multiple, complex causes. CONCLUSIONS: Plausible measures of dialysis nonadherence have long-term associations with allograft and patient survival. Behavioral metrics were more closely associated with outcomes than laboratory markers were. The implications of nonadherent behaviors for dialysis patients must be carefully considered before patients are excluded from transplantation.

Simulations of topiramate dosage recommendations for poor compliance events.

PURPOSE: To determine the influences of one or two consecutive missed topiramate (TPM) doses on TPM pharmacokinetics and to suggest the proper TPM replacement dosing schemes using Monte Carlo simulations. METHODS: Monte Carlo simulations were performed for various replacement dosing schemes using the parameters from the published population pharmacokinetic models. The lowest percentage of deviation of simulated concentrations outside the reference range of 5-20 mg/L from the compliance scenario for each replacement dosing scheme was used as a criterion for choosing the proper replacement dosing scheme. RESULTS: For the one missed dose, the replacement with an immediate regular dose and a partial dose resulted in the lowest and highest percentages of concentration below 5 mg/L, respectively. While the opposite results were observed for the upper bound of the reference range (20 mg/L). For the two consecutive missed doses, the replacement with one and a half-missed doses resulted in a lower percentage of deviation of concentrations below 5 mg/L from the compliance scenario than the replacement with one regular dose. CONCLUSIONS: For the one missed dose, taking an immediate regular dose might be suitable for patients who require higher TPM levels, while for patients who require lower TPM levels, an immediate partial dose could be used. For the two consecutive missed doses, an immediate one and a half regular dose might be suitable. However, these results were merely based on simulations; thus, they should be used alongside the clinician's justification based on seizure control.