Individual meropenem epithelial lining fluid and plasma PK/PD target attainment.

It is unclear whether plasma is a reliable surrogate for target attainment in the epithelial lining fluid (ELF). The objective of this study was to characterize meropenem target attainment in plasma and ELF using prospective samples. The first 24-hour T>MIC was evaluated vs 1xMIC and 4xMIC targets at the patient (i.e., fixed MIC of 2 mg/L) and population [i.e., cumulative fraction of response (CFR) according to EUCAST MIC distributions] levels for both plasma and ELF. Among 65 patients receiving ≥24 hours of treatment, 40% of patients failed to achieve >50% T>4xMIC in plasma and ELF, and 30% of patients who achieved >50% T>4xMIC in plasma had <50% T>4xMIC in ELF. At 1xMIC and 4xMIC targets, 3% and 25% of patients with >95% T>MIC in plasma had <50% T>MIC in ELF, respectively. Those with a CRCL >115 mL/min were less likely to achieve >50%T>4xMIC in ELF (P < 0.025). In the population, CFR for Escherichia coli at 1xMIC and 4xMIC was >97%. For Pseudomonas aeruginosa, CFR was ≥90% in plasma and ranged 80%-85% in ELF at 1xMIC when a loading dose was applied. CFR was reduced in plasma (range: 75%-81%) and ELF (range: 44%-60%) in the absence of a loading dose at 1xMIC. At 4xMIC, CFR for P. aeruginosa was 60%-86% with a loading dose and 18%-62% without a loading dose. We found that plasma overestimated ELF target attainment inup to 30% of meropenem-treated patients, CRCL >115 mL/min decreased target attainment in ELF, and loading doses increased CFR in the population.

Iohexol-measured glomerular filtration rate and urinary biomarker changes between vancomycin and vancomycin plus piperacillin-tazobactam in a translational rat model.

Recent clinical studies have reported additive nephrotoxicity with the combination of vancomycin and piperacillin-tazobactam. However, preclinical models have failed to replicate this finding. This study assessed differences in iohexol-measured glomerular filtration rate (GFR) and urinary injury biomarkers among rats receiving this antibiotic combination. Male Sprague-Dawley rats received either intravenous vancomycin, intraperitoneal piperacillin-tazobactam, or both for 96 hours. Iohexol-measured GFR was used to quantify real-time kidney function changes. Kidney injury was evaluated via the urinary biomarkers: kidney injury molecule-1 (KIM-1), clusterin, and osteopontin. Compared to the control, rats that received vancomycin had numerically lower GFR after drug dosing on day 3. Rats in this group also had elevations in urinary KIM-1 on experimental days 2 and 4. Increasing urinary KIM-1 was found to correlate with decreasing GFR on experimental days 1 and 3. Rats that received vancomycin+piperacillin-tazobactam did not exhibit worse kidney function or injury biomarkers compared to vancomycin alone. The combination of vancomycin+piperacillin-tazobactam does not cause additive nephrotoxicity in a translational rat model. Future clinical studies investigating this antibiotic combination should employ more sensitive biomarkers of kidney function and injury, similar to those utilized in this study.

Machine Learning To Stratify Methicillin-Resistant Staphylococcus aureus Risk among Hospitalized Patients with Community-Acquired Pneumonia.

Methicillin-resistant Staphylococcus aureus (MRSA) is an uncommon but serious cause of community-acquired pneumonia (CAP). A lack of validated MRSA CAP risk factors can result in overuse of empirical broad-spectrum antibiotics. We sought to develop robust models predicting the risk of MRSA CAP using machine learning using a population-based sample of hospitalized patients with CAP admitted to either a tertiary academic center or a community teaching hospital. Data were evaluated using a machine learning approach. Cases were CAP patients with MRSA isolated from blood or respiratory cultures within 72 h of admission; controls did not have MRSA CAP. The Classification Tree Analysis algorithm was used for model development. Model predictions were evaluated in sensitivity analyses. A total of 21 of 1,823 patients (1.2%) developed MRSA within 72 h of admission. MRSA risk was higher among patients admitted to the intensive care unit (ICU) in the first 24 h who required mechanical ventilation than among ICU patients who did not require ventilatory support (odds ratio [OR], 8.3; 95% confidence interval [CI], 2.4 to 32). MRSA risk was lower among patients admitted to ward units than among those admitted to the ICU (OR, 0.21; 95% CI, 0.07 to 0.56) and lower among ICU patients without a history of antibiotic use in the last 90 days than among ICU patients with antibiotic use in the last 90 days (OR, 0.03; 95% CI, 0.002 to 0.59). The final machine learning model was highly accurate (receiver operating characteristic [ROC] area = 0.775) in training and jackknife validity analyses. We identified a relatively simple machine learning model that predicted MRSA risk in hospitalized patients with CAP within 72 h postadmission.

Characterizing Risk Factors for Clostridioides difficile Infection among Hospitalized Patients with Community-Acquired Pneumonia.

Hospitalized patients with community-acquired pneumonia (CAP) are at risk of developing Clostridioides difficile infection (CDI). We developed and tested clinical decision rules for identifying CDI risk in this patient population. The study was a single-center retrospective, case-control analysis of hospitalized adult patients empirically treated for CAP between 1 January 2014 and 3 March 2018. Differences between cases (CDI diagnosed within 180 days following admission) and controls (no test result indicating CDI during the study period) with respect to prehospitalization variables were modeled to generate propensity scores. Postadmission variables were used to predict case status on each postadmission day where (i) ≥1 additional case was identified and (ii) each model stratum contained ≥15 subjects. Models were developed and tested using optimal discriminant analysis and classification tree analysis. Forty-four cases and 181 controls were included. The median time to diagnosis was 50 days postadmission. After weighting, three models were identified (20, 117, and 165 days postadmission). The day 20 model yielded the greatest (weighted [w]) accuracy (weighted area under the receiver operating characteristic curve [wROC area] = 0.826) and the highest chance-corrected accuracy (weighted effect strength for sensitivity [wESS] = 65.3). Having a positive culture (odds, 1:4; P = 0.001), receipt of ceftriaxone plus azithromycin for a defined infection (odds, 3:5; P = 0.006), and continuation of empirical broad-spectrum antibiotics with activity against P. aeruginosa when no pathogen was identified (odds, 1:8; P = 0.013) were associated with CDI on day 20. Three models were identified that accurately predicted CDI in hospitalized patients treated for CAP. Antibiotic use increased the risk of CDI in all models, underscoring the importance of antibiotic stewardship.

Consequences to patients, clinicians, and manufacturers when very serious adverse drug reactions are identified (1997-2019): A qualitative analysis from the Southern Network on Adverse Reactions (SONAR).

BACKGROUND: Adverse drug/device reactions (ADRs) can result in severe patient harm. We define very serious ADRs as being associated with severe toxicity, as measured on the Common Toxicity Criteria Adverse Events (CTCAE)) scale, following use of drugs or devices with large sales, large financial settlements, and large numbers of injured persons. We report on impacts on patients, clinicians, and manufacturers following very serious ADR reporting. METHODS: We reviewed clinician identified very serious ADRs published between 1997 and 2019. Drugs and devices associated with reports of very serious ADRs were identified. Included drugs or devices had market removal discussed at Food and Drug Advisory (FDA) Advisory Committee meetings, were published by clinicians, had sales > $1 billion, were associated with CTCAE Grade 4 or 5 toxicity effects, and had either >$1 billion in settlements or >1,000 injured patients. Data sources included journals, Congressional transcripts, and news reports. We reviewed data on: 1) timing of ADR reports, Boxed warnings, and product withdrawals, and 2) patient, clinician, and manufacturer impacts. Binomial analysis was used to compare sales pre- and post-FDA Advisory Committee meetings. FINDINGS: Twenty very serious ADRs involved fifteen drugs and one device. Legal settlements totaled $38.4 billion for 753,900 injured persons. Eleven of 18 clinicians (61%) reported harms, including verbal threats from manufacturer (five) and loss of a faculty position (one). Annual sales decreased 94% from $29.1 billion pre-FDA meeting to $4.9 billion afterwards (p<0.0018). Manufacturers of four drugs paid $1.7 billion total in criminal fines for failing to inform the FDA and physicians about very serious ADRs. Following FDA approval, the median time to ADR reporting was 7.5 years (Interquartile range 3,13 years). Twelve drugs received Box warnings and one drug received a warning (median, 7.5 years following ADR reporting (IQR 5,11 years). Six drugs and 1 device were withdrawn from marketing (median, 5 years after ADR reporting (IQR 4,6 years)). INTERPRETATION: Because very serious ADRs impacts are so large, policy makers should consider developing independently funded pharmacovigilance centers of excellence to assist with clinician investigations. FUNDING: This work received support from the National Cancer Institute (1R01 CA102713 (CLB), https://www.nih.gov/about-nih/what-we-do/nih-almanac/national-cancer-institute-nci; and two Pilot Project grants from the American Cancer Society's Institutional Grant Award to the University of South Carolina (IRG-13-043-01) https://www.cancer.org/ (SH; BS).

The First 2 Years of Biosimilar Epoetin for Cancer and Chemotherapy-Induced Anemia in the U.S.: A Review from the Southern Network on Adverse Reactions.

Biosimilars are biologic drug products that are highly similar to reference products in analytic features, pharmacokinetics and pharmacodynamics, immunogenicity, safety, and efficacy. Biosimilar epoetin received Food and Drug Administration (FDA) approval in 2018. The manufacturer received an FDA nonapproval letter in 2017, despite receiving a favorable review by FDA's Oncologic Drugs Advisory Committee (ODAC) and an FDA nonapproval letter in 2015 for an earlier formulation. We discuss the 2018 FDA approval, the 2017 FDA ODAC Committee review, and the FDA complete response letters in 2015 and 2017; review concepts of litigation, naming, labeling, substitution, interchangeability, and pharmacovigilance; review European and U.S. oncology experiences with biosimilar epoetin; and review the safety of erythropoiesis-stimulating agents. In 2020, policy statements from AETNA, United Health Care, and Humana indicated that new epoetin oncology starts must be for biosimilar epoetin unless medical need for other epoetins is documented. Empirical studies report that as of 2012, reference epoetin use decreased from 40%-60% of all patients with cancer with chemotherapy-induced anemia to <5% of such patients because of safety concerns. Between 2018 and 2020, biosimilar epoetin use varied, increasing to 81% among one private insurer's patients covered by Medicare whose cancer care is administered with Oncology Analytics and to 41% with the same private insurer's patients with cancer covered by commercial health insurance and administered by the private insurer, to 0% in several Veterans Administration Hospitals, increasing to 100% in one large county hospital in California, and with yet-to-be-reported data from most oncology settings. We conclude that biosimilar epoetin appears to have overcome some barriers since 2015, although current uptake in the U.S. is variable. Pricing and safety considerations for all erythropoiesis-stimulating agents are primary determinants of biosimilar epoetin oncology uptake. IMPLICATIONS FOR PRACTICE: Few oncologists understand substitution and interchangeability of biosimilars with reference drugs. Epoetin biosimilar is new to the market, and physician and patient understanding is limited. The development of epoetin biosimilar is not familiar to oncologists.

Antimicrobial never events: Objective application of a framework to assess vancomycin appropriateness.

To address appropriateness of antibiotic use, we implemented an electronic framework to evaluate antibiotic "never events" (NEs) at 2 medical centers. Patient-level vancomycin administration records were classified as NEs or non-NEs. The objective framework allowed capture of true-positive vancomycin NEs in one-third of patients identified by the electronic strategy.

Improving oncology biosimilar launches in the EU, the USA, and Japan: an updated Policy Review from the Southern Network on Adverse Reactions.

The EU, the USA, and Japan account for the majority of biological pharmacotherapy use worldwide. Biosimilar regulatory approval pathways were authorised in the EU (2006), in Japan (2009), and in the USA (2015), to facilitate approval of biological drugs that are highly similar to reference products and to encourage market competition. Between 2007 and 2020, 33 biosimilars for oncology were approved by the European Medicines Agency (EMA), 16 by the US Food and Drug Administration (FDA), and ten by the Japan Pharmaceuticals and Medical Devices Agency (PMDA). Some of these approved applications were initially rejected because of manufacturing concerns (four of 36 [11%] with the EMA, seven of 16 [44%] with the FDA, none of ten for the PMDA). Median times from initial regulatory submission before approval of oncology biosimilars were 1·5 years (EMA), 1·3 years (FDA), and 0·9 years (PMDA). Pharmacists can substitute biosimilars for reference biologics in some EU countries, but not in the USA or Japan. US regulation prohibits substitution, unless the biosimilar has been approved as interchangeable, a designation not yet achieved for any biosimilar in the USA. Japan does not permit biosimilar substitution, as prescribers must include the product name on each prescription and that specific product must be given to the patient. Policy Reviews published in 2014 and 2016 in The Lancet Oncology focused on premarket and postmarket policies for oncology biosimilars before most of these drugs received regulatory approval. In this Policy Review from the Southern Network on Adverse Reactions, we identify factors preventing the effective launch of oncology biosimilars. Introduction to the market has been more challenging with therapeutic than for supportive care oncology biosimilars. Addressing region-specific competition barriers and educational needs would improve the regulatory approval process and market launches for these biologics, therefore expanding patient access to these products in the EU, the USA, and Japan.

End of an era of administering erythropoiesis stimulating agents among Veterans Administration cancer patients with chemotherapy-induced anemia.

Erythropoisis stimulating agent (ESA) use was addressed in Food and Drug Administration (FDA) Oncology Drug Advisory Committee (ODAC) meetings between 2004 and 2008. FDA safety-focused regulatory actions occurred in 2007 and 2008. In 2007, black box warnings advised of early death and venous thromboembolism (VTE) risks with ESAs in oncology. In 2010, a Risk Evaluation Strategies (REMS) was initiated, with cancer patient consent that mortality and VTE risks were noted with ESAs. We report warnings and REMS impacts on ESA utilization among Veterans Administration (VA) cancer patients with chemotherapy-induced anemia (CIA). Data were from Veterans Affairs database (2003-2012). Epoetin and darbepoetin use were primary outcomes. Segmented linear regression was used to estimate changes in ESA use levels and trends, clinical appropriateness, and adverse events (VTEs) among chemotherapy-treated cancer patients. To estimate changes in level of drug prescription rate after policy actions, model-specific indicator variables as covariates based on specific actions were included. ESA use fell by 95% and 90% from 2005, for epoetin and darbepoetin, from 22% and 11%, respectively, to 1% and 1%, respectively, among cancer patients with CIA, respectively (p<0.01). Following REMS in 2010, mean hematocrit levels at ESA initiation decreased from 30% to 21% (p<0.01). Black box warnings preceded decreased ESA use among VA cancer patients with CIA. REMS was followed by reduced hematocrit levels at ESA initiation. Our findings contrast with privately- insured and Medicaid insured cancer patient data on chemotherapy-induced anemia where ESA use decreased to 3% to 7% by 2010-2012. By 2012, the era of ESA administration to VA to cancer patients had ended but the warnings remain relevant and significant. In 2019, oncology/hematology national guidelines (ASCO/ASH) recommend that cancer patients with chemotherapy-induced anemia should receive ESAs or red blood cell transfusions after risk-benefit evaluation.

End of an era for erythropoiesis-stimulating agents in oncology.

Erythropoiesis-stimulating agents (ESAs) are available to treat chemotherapy-induced anemia (CIA). In 2007-2008, regulatory notifications advised of venous thromboembolism and mortality risks while the Center for Medicare and Medicaid Services' restricted ESA initiation to patients with hemoglobin <10 g/dl. In 2010, a Risk Evaluation and Mitigation Strategies required consent prior to administration. We evaluated ESA utilization from 2003 to 2012 and obtained private health insurer claims data for persons with lung, colorectal, or breast cancer from 2001 to 2012. ESA use for CIA was determined by an ESA claim after chemotherapy, up to 6 months after treatment. We identified 839,948 commercially insured patients, including 24,785 patients with ESA-treated CIA (3.2%). Darbepoetin use increased 3.9-fold from 2003 to 2007 (12.3% to 48.7%) and then decreased 95% to 2.6% by 2012. Epoetin use decreased 90% from 2003 to 2012 (30.3% to 3.1%). Between 2003 and 2012, mean epoetin dosing decreased 0.8-fold (244,979 in 2003 vs. 196,216 units in 2012), but increased 1.8-fold for darbepoetin-treated CIA (262 in 2003 to 467 µg in 2012). Among CIA patients, transfusions were low (4.5%) in 2002-2007, then increased 2.2-fold between 2008 and 2012. Safety initiatives between 2007 and 2010 facilitated reductions in ESA use combined with changes in coverage. These data show the efficacy of regulatory efforts, publication of adverse events and changes in reimbursement in reducing use of ESAs. Future studies are warranted to optimize deimplementation strategies to improve patient safety.

Caveat Medicus: Clinician experiences in publishing reports of serious oncology-associated adverse drug reactions.

Oncology-associated adverse drug/device reactions can be fatal. Some clinicians who treat single patients with severe oncology-associated toxicities have researched case series and published this information. We investigated motivations and experiences of select individuals leading such efforts. Clinicians treating individual patients who developed oncology-associated serious adverse drug events were asked to participate. Inclusion criteria included having index patient information, reporting case series, and being collaborative with investigators from two National Institutes of Health funded pharmacovigilance networks. Thirty-minute interviews addressed investigational motivation, feedback from pharmaceutical manufacturers, FDA personnel, and academic leadership, and recommendations for improving pharmacovigilance. Responses were analyzed using constant comparative methods of qualitative analysis. Overall, 18 clinicians met inclusion criteria and 14 interviewees are included. Primary motivations were scientific curiosity, expressed by six clinicians. A less common theme was public health related (three clinicians). Six clinicians received feedback characterized as supportive from academic leaders, while four clinicians received feedback characterized as negative. Three clinicians reported that following the case series publication they were invited to speak at academic institutions worldwide. Responses from pharmaceutical manufacturers were characterized as negative by 12 clinicians. One clinician's wife called the post-reporting time the "Maalox month," while another clinician reported that the manufacturer collaboratively offered to identify additional cases of the toxicity. Responses from FDA employees were characterized as collaborative for two clinicians, neutral for five clinicians, unresponsive for negative by six clinicians. Three clinicians endorsed developing improved reporting mechanisms for individual physicians, while 11 clinicians endorsed safety activities that should be undertaken by persons other than a motivated clinician who personally treats a patient with a severe adverse drug/device reaction. Our study provides some of the first reports of clinician motivations and experiences with reporting serious or potentially fatal oncology-associated adverse drug or device reactions. Overall, it appears that negative feedback from pharmaceutical manufacturers and mixed feedback from the academic community and/or the FDA were reported. Big data, registries, Data Safety Monitoring Boards, and pharmacogenetic studies may facilitate improved pharmacovigilance efforts for oncology-associated adverse drug reactions. These initiatives overcome concerns related to complacency, indifference, ignorance, and system-level problems as barriers to documenting and reporting adverse drug events- barriers that have been previously reported for clinician reporting of serious adverse drug reactions.

Regulatory and Clinical Experiences with Biosimilar Filgrastim in the U.S., the European Union, Japan, and Canada.

Biosimilar filgrastims are primarily indicated for chemotherapy-induced neutropenia prevention. They are less expensive formulations of branded filgrastim, and biosimilar filgrastim was the first biosimilar oncology drug administered in European Union (EU) countries, Japan, and the U.S. Fourteen biosimilar filgrastims have been marketed in EU countries, Japan, the U.S., and Canada since 2008, 2012, 2015, and 2016, respectively. We reviewed experiences and policies for biosimilar filgrastim markets in EU countries and Japan, where uptake has been rapid, and in the U.S. and Canada, where experience is rapidly emerging. U.S. regulations for designating biosimilar interchangeability are under development, and such regulations have not been developed in most other countries. Pharmaceutical substitution is allowed for new filgrastim starts in some EU countries and in Canada, but not Japan and the U.S. In EU countries, biosimilar adoption is facilitated with favorable hospital tender offers. U.S. adoption is reportedly 24%, while the second filgrastim biosimilar is priced 30% lower than branded filgrastim and 20% lower than the first biosimilar filgrastim approved by the U.S. Food and Drug Administration. Utilization is about 60% in EU countries, where biosimilar filgrastim is marketed at a 30%-40% discount. In Japan, biosimilar filgrastim utilization is 45%, primarily because of 35% discounts negotiated by Central Insurance and hospital-only markets. Overall, biosimilar filgrastim adoption barriers are small in many EU countries and Japan and are diminishing in Canada in the U.S. Policies facilitating improved U.S. adoption of biosimilar filgrastim, based on positive experiences in EU countries and Japan, including favorable insurance coverage; larger price discount relative to reference filgrastim pricing; closing of the "rebate trap" with transparent pricing information; formal educational efforts of patients, physicians, caregivers, and providers; and allowance of pharmaceutical substitution of biosimilar versus reference filgrastim, should be considered. IMPLICATIONS FOR PRACTICE: We reviewed experiences and policies for biosimilar filgrastims in Europe, Japan, Canada, and the U.S. Postmarketing harmonization of regulatory policies for biosimilar filgrastims has not occurred. Acceptance of biosimilar filgrastims for branded filgrastim, increasing in the U.S. and in Canada, is commonplace in Japan and Europe. In the U.S., some factors, accepted in Europe or Japan, could improve uptake, including acceptance of biosimilars as safe and effective; larger cost savings, decreasing "rebate traps" where pharmaceutical benefit managers support branded filgrastim, decreased use of patent litigation/challenges, and allowing pharmacists to routinely substitute biosimilar for branded filgrastim.

Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis.

RATIONALE, AIMS, AND OBJECTIVES: Interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time, and the intervention is expected to "interrupt" the level and/or trend of the outcome, subsequent to its introduction. The internal validity of this analysis is strengthened considerably if the treated unit is contrasted with a comparable control group. In this paper, we introduce a novel machine learning approach using optimal discriminant analysis (ODA) to evaluate treatment effects in multiple-group ITSA. METHOD: We evaluate the effect of California's Proposition 99 (passed in 1988) for reducing cigarette sales, by comparing California (CA) to Montana (MT)-the best matching control state not exposed to any smoking reduction initiatives. We contrast results from ODA to those of ITSA regression (ITSAREG)-a commonly used approach for evaluating treatment effects in ITSA studies. RESULTS: Both approaches found CA and MT to be comparable on their preintervention time series, and both approaches equally found CA to have statistically lower cigarette sales in the post-intervention period (P < 0.0001). The ODA model achieved very high effect strength of sensitivity (a measure of classification accuracy) of 91.67%, which remained high (75.00%) after conducting leave-one-out analysis to assess generalizability. CONCLUSIONS: The ODA framework achieved results comparable to ITSAREG, bolstering confidence in the intervention effect. In addition, ODA confers several advantages over conventional approaches that may make it a better approach to use in multiple group ITSA studies: insensitivity to skewed data, model-free permutation tests to derive P values, identification of the threshold value which best discriminates intervention and control groups, a chance- and maximum-corrected index of classification accuracy, and cross-validation to assess generalizability.

Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then estimate treatment effects using Cox regression. A machine-learning alternative-classification tree analysis (CTA)-used to generate propensity scores and to estimate treatment effects in time-to-event data may identify complex relationships between covariates not found using conventional regression-based approaches. METHOD: Using empirical data, we identify all statistically valid CTA propensity score models and then use them to compute strata-specific, observation-level propensity score weights that are subsequently applied in outcomes analyses. We compare findings obtained using this framework to the conventional method, by evaluating covariate balance and treatment effect estimates obtained using Cox regression and a weighted CTA outcomes model. RESULTS: All models had some imbalanced covariates. Nevertheless, treatment effect estimates were generally consistent across all weighted models. CONCLUSIONS: In the study sample, given that all approaches elicited similar results, using CTA increased confidence that bias could not be reduced any further. Because the CTA algorithm identifies all statistically valid propensity score models for a sample, it is most likely to identify a correctly specified propensity score model-and therefore should be used either to confirm results using traditional methods, or to reveal biases that may be missed by traditional methods. Moreover, given that the true treatment effect is never known in observational data, CTA should be considered for estimating outcomes because no statistical assumptions are required.

Identifying causal mechanisms in health care interventions using classification tree analysis.

RATIONALE, AIMS, AND OBJECTIVES: Mediation analysis identifies causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that mediates the relationship between the treatment and outcome. This paper introduces classification tree analysis (CTA), a machine-learning procedure, as an alternative to conventional methods for analysing mediation effects. METHOD: Using data from the JOBS II study, we compare CTA to structural equation models (SEMs) by assessing their consistency in revealing mediation effects on 2 outcomes; reemployment (a binary variable) and depressive symptoms (a continuous variable). Because study participants were not randomized sequentially to both treatment and mediator, an additional model was generated including baseline covariates to strengthen the validity of some key identifying assumptions required of all mediation analyses. RESULTS: Using SEM, no statistically significant treatment or mediated effects were found for either outcome. In contrast, CTA found a significant treatment effect for reemployment (P = .047) and a mediated pathway for individuals in the treatment group (P = .014). No CTA model could be generated for the reemployment outcome. When covariates were added to the model, CTA found numerous interactions, while SEM found no effects. CONCLUSIONS: CTA may uncover mediation effects where conventional approaches do not, because CTA does not require any assumptions about the distribution of variables nor of the functional form of the model, and CTA will systematically identify all statistically viable interactions. The versatility of CTA enables the investigator to explore the theorized underlying causal mechanism of an intervention in a much more comprehensive manner than conventional mediation analytic approaches.

Modeling time-to-event (survival) data using classification tree analysis.

RATIONALE, AIMS, AND OBJECTIVES: Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. METHOD: Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. RESULTS: The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. CONCLUSIONS: Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework.

Minimizing imbalances on patient characteristics between treatment groups in randomized trials using classification tree analysis.

RATIONALE, AIMS, AND OBJECTIVES: Randomization ensures that treatment groups do not differ systematically in their characteristics, thereby reducing threats to validity that may otherwise explain differences in outcomes. Large observed imbalances in patient characteristics may indicate that selection bias is being introduced into the treatment allocation process. We introduce classification tree analysis (CTA) as a novel algorithmic approach for identifying potential imbalances in characteristics and their interactions when provisionally assigning each new participant to one or the other treatment group. The participant is then permanently assigned to the treatment group that elicits either no or less imbalance than if assigned to the alternate group. METHOD: Using data on participant characteristics from a clinical trial, we compare 3 different treatment allocation approaches: permuted block randomization (the original allocation method), minimization, and CTA. Treatment allocation performance is assessed by examining balance of all 17 patient characteristics between study groups for each of the allocation techniques. RESULTS: While all 3 treatment allocation techniques achieved excellent balance on main effect variables, Classification tree analysis further identified imbalances on interactions and in the distributions of some of the continuous variables. CONCLUSIONS: Classification tree analysis offers an algorithmic procedure that may be used with any randomization methodology to identify and then minimize linear, nonlinear, and interactive effects that induce covariate imbalance between groups. Investigators should consider using the CTA approach as a real-time complement to randomization for any clinical trial to safeguard the treatment allocation process against bias.

Melanoma complicating treatment with natalizumab for multiple sclerosis: A report from the Southern Network on Adverse Reactions (SONAR).

A 43-year-old female with multiple sclerosis developed urethral melanoma. The only potential risk factor was treatment with natalizumab, a humanized monoclonal antibody against α4 integrins. To investigate the risk-exposure relationship, we reviewed this case, all other published cases, and cases of natalizumab-associated melanoma reported to regulatory agencies. Data sources included the Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) (2004-2014), a FDA Advisory Committee Meeting Report, and peer-reviewed publications. In the United States, the manufacturer maintains an FDA-mandated Tysabri Safety Surveillance Program (part of the Tysabri Outcomes Unified Commitment to Health (TOUCH)) of natalizumab-treated patients. We statistically compared reporting completeness for natalizumab-associated melanoma cases in FAERs for which information was obtained entirely from the TOUCH program versus cases where FAERS information was supplemented by TOUCH program information. FAERS included 137 natalizumab-associated melanoma reports in patients with multiple sclerosis. Median age at melanoma diagnosis was 45 years (range: 21-74 years). Changes in preexisting nevi occurred in 16%, history of cutaneous nevi occurred in 22%, diagnosis within 2 years of beginning natalizumab occurred in 34%, and 74% had primary surgical treatment. Among seven natalizumab-treated MS patients who developed biopsy-confirmed melanoma on treatment and reported in the literature, median age at diagnosis was 41 years (range: 38-48 years); and the melanoma diagnosis occurred following a median of 12 natalizumab doses (range: 1-77 doses). A history of mole or nevi was noted in four patients and a history of prior melanoma was noted in one patient. Completeness scores for reports were significantly lower for FAERS cases reported from the TOUCH program versus FAERS cases supplemented by TOUCH information (median score of 2 vs. 4 items out of 8-possible items, P < 0.0007). Clinicians should monitor existing nevi and maintain suspicion for melanoma developing in natalizumab-treated patients. The TOUCH Safety Surveillance Program, currently focused on progressive multifocal leukoencephalopathy, should be expanded to include information on other serious complications including malignancies, particularly if they are immunologic in nature.

Tale of Two Erythropoiesis-Stimulating Agents: Utilization, Dosing, Litigation, and Costs of Darbepoetin and Epoetin Among South Carolina Medicaid-Covered Patients With Cancer and Chemotherapy-Induced Anemia.

PURPOSE: The US Food and Drug Administration (FDA) has approved epoetin and darbepoetin for chemotherapy-induced anemia (CIA). Approved epoetin and darbepoetin dosing schedules were three times per week and weekly, respectively, although off-label, less frequent scheduling was common. In 2004, 2007, and 2008, a US Food and Drug Administration Advisory Committees warned of risks associated with erythropoiesis-stimulating agents. During this period, lawsuits alleging illegal darbepoetin marketing practices have concluded, resulting in $1.1 billion in fines and settlements and one criminal conviction. No prior study, to our knowledge, has reported on the use of darbepoetin versus epoetin for CIA. METHODS: We evaluated the dosing, utilization, and costs of erythropoiesis-stimulating agents among 3,761 South Carolina Medicaid patients with CIA. RESULTS: Epoetin and darbepoetin utilization rates were 22% and 28% in 2003, 10% and 33% in 2007, and 3% and 7% in 2010, respectively. Mean per-patient per-administration epoetin and darbepoetin doses were 40,983 IU and 191 µg, respectively, in 2003 and 47,753 IU and 369 µg, respectively, in 2010. Mean monthly patient costs for epoetin and darbepoetin were $1,030 and $981, respectively, in 2003 and $932 and $1,352, respectively, in 2010. Epoetin use decreased steadily between 2002 and 2010; darbepoetin use increased steadily between 2003 and 2007 and then decreased steadily thereafter. Per-patient dosing of darbepoetin, but not epoetin, increased steadily between 2003 and 2010, and monthly per-patient epoetin costs decreased 3% while the per-patients costs of darbepoetin increased 30% between 2003 and 2010. CONCLUSION: To our knowledge, our findings are the first data reporting on epoetin versus darbepoetin use for CIA and support recently concluded lawsuits involving allegations of illegal marketing practices of the manufacturer of darbepoetin.