Clinical research in hospital pharmacy during the fight against COVID-19.

The health crisis resulting from the rapid spread of SARS-CoV-2 worlwide, added to the low evidence of currently used treatments has led to the development of a large number of clinical trials (CT) and observational studies. Likewise,  important measures have been adopted in healthcare and research centers  aimed at halting the pandemic as soon as possible. The objective of this study is  to gather the main aspects of the clinical research studies undertaken by the  Departments of Hospital Pharmacy (DHP) of Spain during the COVID-19 crisis. The decision of the Spanish Society of Hospital Pharmacy (SEFH) to sponsor CTs made it possible that 13% of DHP had been led at least one CT.  The Spanish Agency for Medicines and Medical Devices (AEMPS), in coordination  with Institutional Review Boards, has adopted a fast-track review procedure to  accelerate authorizations for CTs related to the treatment or prevention of  COVID-19. There have also been numerous public and private calls for financing  research projects aimed at contributing to the fight against this virus. Despite  the pandemic, actions have been taken to continue ongoing CTs and studies  while the safety and well-being of patients are guaranteed. More specifically, the AEMPS and the European Medicines Agency (EMA) have issued guidelines that  incorporate changes to CT protocols that will have to be applied until the  pandemic is over. In this health emergency, the scientific community has found  itself in a race against time to generate evidence. It is at this moment that  hospital pharmacists emerge as key players in clinical research and are  contributing to a rational, effective and safe healthcare decision-making.

La presente crisis sanitaria derivada de la rápida expansión del virus SARS-CoV- 2 a nivel mundial, así como la falta de evidencia de los tratamientos empleados  actualmente, ha provocado la aparición de un gran número de ensayos clínicos y estudios observacionales. Del mismo modo, ha ocasionado la puesta en marcha  de importantes medidas en el entorno sanitario e investigador con el fin de  conseguir detener la evolución de la pandemia lo antes posible. El objetivo del  actual trabajo es recopilar aspectos fundamentales relacionados con la  investigación clínica desarrollada por los servicios de farmacia hospitalaria  durante la crisis provocada por la COVID-19. La iniciativa de la Sociedad  Española de Farmacia Hospitalaria de actuar como promotor de ensayos clínicos  ha posibilitado que el 13% de estos servicios de farmacia hospitalaria haya  podido liderar uno. En este sentido, la Agencia Española de Medicamentos y  Productos Sanitarios, junto con los Comités de Ética de Investigación, ha  acelerado los procedimientos de autorización de nuevos ensayos clínicos  destinados a tratar o prevenir la COVID-19. Asimismo, han sido numerosas las  convocatorias públicas y privadas destinadas a la financiación de proyectos de  diversa índole con el fin de contribuir a la lucha contra este virus. A pesar de la  irrupción de la pandemia, también han surgido acciones destinadas a mantener  las actividades de los ensayos clínicos y estudios puestos previamente en  marcha, garantizando la seguridad y bienestar del paciente. Concretamente, la  Agencia Española de Medicamentos y Productos Sanitarios y la Agencia Europea  de Medicamentos han publicado guías que incluyen cambios en los protocolos de los ensayos clínicos que deben mantenerse mientras dure la pandemia. La  emergencia sanitaria actual ha obligado a la comunidad científica a la generación de evidencia a contrarreloj. Por ello, en este momento en el que se requiere del  mayor rigor posible, el farmacéutico de hospital debe alzarse como una figura  clave en la investigación en salud, contribuyendo a que las decisiones sanitarias  sean racionales, eficientes y seguras.

Reweighting Randomized Controlled Trial Evidence to Better Reflect Real Life - A Case Study of the Innovative Medicines Initiative.

Evidence from randomized controlled trials available for timely health technology assessments of new pharmacological treatments and regulatory decision making may not be generalizable to local patient populations, often resulting in decisions being made under uncertainty. In recent years, several reweighting approaches have been explored to address this important question of generalizability to a target population. We present a case study of the Innovative Medicines Initiative to illustrate the inverse propensity score reweighting methodology, which may allow us to estimate the expected treatment benefit if a clinical trial had been run in a broader real-world target population. We learned that identifying treatment effect modifiers, understanding and managing differences between patient characteristic data sets, and balancing the closeness of trial and target patient populations with effective sample size are key to successfully using this methodology and potentially mitigating some of this uncertainty around local decision making.

Personalized breast cancer screening strategies: A systematic review and quality assessment.

BACKGROUND: The effectiveness of breast cancer screening is still under debate. Our objective was to systematically review studies assessing personalized breast cancer screening strategies based on women's individual risk and to conduct a risk of bias assessment. METHODS: We followed the standard methods of The Cochrane Collaboration and PRISMA declaration and searched the MEDLINE, EMBASE and Clinical Trials databases for studies published in English. The quality of the studies was assessed using the ISPOR-AMCP-NPC Questionnaire and The Cochrane Risk of Bias Tool. Two independent reviewers screened full texts and evaluated the risk of bias. RESULTS: Out of the 1533 initially retrieved citations, we included 13 studies. Three studies were randomized controlled trials, while nine were mathematical modeling studies, and one was an observational pilot study. The trials are in the recruitment phase and have not yet reported their results. All three trials used breast density and age to define risk groups, and two of them included family history, previous biopsies, and genetic information. Among the mathematical modeling studies, the main risk factors used to define risk groups were breast density, age, family history, and previous biopsies. Six studies used genetic information to define risk groups. The most common outcome measures were the gain in quality-adjusted life years (QALY), absolute costs, and incremental cost-effectiveness ratio (ICER), while the main outcome in the observational study was the detection rate. In all models, personalized screening strategies were shown to be effective. The randomized trials were of good quality. The modeling studies showed moderate risk of bias but there was wide variability across studies. The observational study showed a low risk of bias but its utility was moderate due to its pilot design and its relatively small scale. CONCLUSIONS: There is some evidence of the effectiveness of screening personalization in terms of QUALYs and ICER from the modeling studies and the observational study. However, evidence is lacking on feasibility and acceptance by the target population. REVIEW REGISTRATION: PROSPERO: CRD42018110483.

Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence.

Importance: Although randomized clinical trials are considered to be the criterion standard for generating clinical evidence, the use of real-world evidence to evaluate the efficacy and safety of medical interventions is gaining interest. Whether observational data can be used to address the same clinical questions being answered by traditional clinical trials is still unclear. Objective: To identify the number of clinical trials published in high-impact journals in 2017 that could be feasibly replicated using observational data from insurance claims and/or electronic health records (EHRs). Design, Setting, and Participants: In this cross-sectional analysis, PubMed was searched to identify all US-based clinical trials, regardless of randomization, published between January 1, 2017, and December 31, 2017, in the top 7 highest-impact general medical journals of 2017. Trials were excluded if they did not involve human participants, did not use end points that represented clinical outcomes among patients, were not characterized as clinical trials, and had no recruitment sites in the United States. Main Outcomes and Measures: The primary outcomes were the number and percentage of trials for which the intervention, indication, trial inclusion and exclusion criteria, and primary end points could be ascertained from insurance claims and/or EHR data. Results: Of the 220 US-based trials analyzed, 33 (15.0%) could be replicated using observational data because their intervention, indication, inclusion and exclusion criteria, and primary end points could be routinely ascertained from insurance claims and/or EHR data. Of the 220 trials, 86 (39.1%) had an intervention that could be ascertained from insurance claims and/or EHR data. Among the 86 trials, 62 (72.1%) had an indication that could be ascertained. Forty-five (72.6%) of 62 trials had at least 80% of inclusion and exclusion criteria data that could be ascertained. Of these 45 studies, 33 (73.3%) had at least 1 primary end point that could be ascertained. Conclusions and Relevance: This study found that only 15% of the US-based clinical trials published in high-impact journals in 2017 could be feasibly replicated through analysis of administrative claims or EHR data. This finding suggests the potential for real-world evidence to complement clinical trials, both by examining the concordance between randomized experiments and observational studies and by comparing the generalizability of the trial population with the real-world population of interest.

Performance evaluation of regression splines for propensity score adjustment in post-market safety analysis with multiple treatments.

Observational studies provide a core resource in assessing post-market drug safety and effectiveness. Propensity scores are a predominant method for confounding adjustment to achieve unbiased estimation of average treatment effects in observational data. However, the use of propensity score methods has been limited to comparing two treatment groups, while medical situations frequently present with multiple treatment options. Inverse probability of treatment weighting (IPTW) is a popular propensity score adjustment method, but its performance degrades with decreased positivity leading to extreme weights, a problem that can be amplified with multiple treatment groups. Meanwhile, regression on a spline of the propensity score has shown favorable performance compared to other propensity score methods in recent studies involving two treatments. This project utilizes a simulation study to compare IPTW and propensity score splines as adjustment methods in a three-treatment setting. We test a variety of spline methods, including natural cubic splines with varying numbers of interior knots, and thin-plate regression splines. We vary several parameters across simulations, including the degree of propensity score overlap among treatment groups, treatment prevalence, outcome prevalence, and true marginal relative risk. We assess methods based on their bias, root mean squared error, and coverage of the true marginal relative risk across simulations. We find that all methods perform similarly well when there is good propensity score distribution overlap. However, with even moderate decrease in overlap or low outcome prevalence, IPTW produces more biased estimates and higher variance than propensity score splines. Low treatment prevalence or unequal treatment prevalences across groups also worsens IPTW performance. Overall, a natural cubic spline with a relatively small number of interior knots provides good performance across a range of simulations.

Avoiding pitfalls when combining multiple imputation and propensity scores.

Overcoming bias due to confounding and missing data is challenging when analyzing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, the Within approach uses propensity scores individually to obtain exposure effect estimates in each imputation, which are combined to produce an overall estimate. These approaches were compared in a series of Monte Carlo simulations and applied to data from the British Society for Rheumatology Biologics Register. Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals. Researchers are encouraged to implement the Within approach when conducting propensity score analyses with incomplete data.

Centre effects and case-mix in early rheumatoid arthritis observational cohorts: a narrative review.

OBJECTIVES: Observational cohort studies in early RA are a key source of evidence, despite inconsistencies in methodological approaches. This narrative review assesses the spectrum of methodologies used in addressing centre-level effect and case-mix adjustment in early RA observational cohort studies. METHODS: An electronic search was undertaken to identify observational prospective cohorts of >100 patients recruited from two or more centres, within 2 years of an RA or early inflammatory arthritis diagnosis. References and author publication lists of all studies from eligible cohorts were assessed for additional cohorts. RESULTS: Thirty-four unique cohorts were identified from 204 studies. Seven percent of studies considered centre in their analyses, most commonly as a fixed effect in regression modelling. Reporting of case-mix variables in analyses varied widely. The number of variables considered in case-mix adjustment was higher following publication of the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement in 2007. CONCLUSION: Centre effect is unreported or inadequately accounted for in the majority of RA observational cohorts, potentially leading to spurious inferences and obstructing comparisons between studies. Inadequate case-mix adjustment precludes meaningful comparisons between centres. Appropriate methodology to account for centre and case-mix adjustment should be considered at the outset of analyses.

Use of propensity score and disease risk score for multiple treatments with time-to-event outcome: a simulation study.

Propensity score (PS) and disease risk score (DRS) are often used in pharmacoepidemiologic safety studies. Methods of applying these two balancing scores are extensively studied in binary treatment settings. However, the use of PS and DRS is not well understood in the case of non-ordinal multiple treatments. Some PS methods of multiple treatments have been implemented since the theoretical establishment. Nevertheless, most of the work applies to continuous or binary outcomes. Little work has been done for time-to-event outcomes. In this study, we extend the application of the PS and DRS methods to time-to-event outcomes in multiple treatment settings. The analytical approaches include weighing, matching, stratification, and regression. Simulation studies with rare event rates are conducted to evaluate the performances of different methods. Different treatment-covariates and outcome-covariates strength of associations are considered. Additionally, the impacts of imbalanced designs and large or limited PS overlaps are investigated on various analytical approaches. We found that the inverse probability treatment weighting with bootstrap variance estimator, the generalized PS matching, and the Cox regression estimated DRS in full cohort generally performed well in multiple treatment settings. This study aims to provide additional guidance for researchers on PS and DRS analyses in pharmacoepidemiologic observational studies.

Comparison of balancing scores using the ANCOVA approach for estimating average treatment effect: a simulation study.

In this article, we conducted a simulation study to evaluate the performance of five balancing scores using the Analysis of Covariance (ANCOVA) approach, for adjusting bias in estimating average treatment effects (ATE) in observational studies. The five balancing scores which we used as the covariate(s) in the ANCOVA model were (1) propensity score (P), (2) prognostic score (G), (3) propensity score estimated by prognostic score (PG), (4) prognostic score estimated by propensity score (GP), and (5) both propensity and prognostic scores (P&G). The results of the five balancing scores using the ANCOVA approach were compared to the results of the classic regression approach, which included all observed covariates as the predictors. Simulation results showed that balancing scores P, GP, and (P&G) had the smallest bias and mean squared error (MSE) when the outcome variable and the observed covariates were linearly associated, and PG had the smallest or close to the smallest bias and MSE when the associations were nonlinear, nonadditive and nonlinear & nonadditive.

The use of real-world data in cancer drug development.

Excitement about the dramatic increase in potential successful anticancer medicines in recent years is hampered by the high costs involved as well as the length of time traditional pathways take for regulatory approval. The translation of experimental clinical data into real-world evidence is also problematic. While the randomised controlled trial remains the gold standard for assessing efficacy and safety, there is increasing interest in the use of observational data to enable more rapid, informed and widespread availability and access to important anticancer medicines. Taking real-world evidence into account in regulatory and health technology assessment in a thoughtful and balanced fashion will enrich and justify sound decision-making.

Randomized clinical trials and observational studies in the assessment of drug safety.

Randomized clinical trials are considered as the preferred design to assess the potential causal relationships between drugs or other medical interventions and intended effects. For this reason, randomized clinical trials are generally the basis of development programs in the life cycle of drugs and the cornerstone of evidence-based medicine. Instead, randomized clinical trials are not the design of choice for the detection and assessment of rare, delayed and/or unexpected effects related to drug safety. Moreover, the highly homogeneous populations resulting from restrictive eligibility criteria make randomized clinical trials inappropriate to describe comprehensively the safety profile of drugs. In that context, observational studies have a key added value when evaluating the benefit-risk balance of the drugs. However, observational studies are more prone to bias than randomized clinical trials and they have to be designed, conducted and reported judiciously. In this article, we discuss the strengths and limitations of randomized clinical trials and of observational studies, more particularly regarding their contribution to the knowledge of medicines' safety profile. In addition, we present general recommendations for the sensible use of observational data.

Most response-inducing strategies do not increase participation in observational studies: a systematic review and meta-analysis.

OBJECTIVES: To evaluate response-inducing strategies for observational studies using health-related questionnaires or interviews. STUDY DESIGN AND SETTING: We searched PubMed, EMBASE, CINAHL, PsycINFO, and Web of Science up to December 28, 2017. Studies evaluating the effect of a response-inducing strategy on participation rates of observational studies were included. For each strategy, we estimated pooled response ratios with 95% confidence intervals (CIs) in a Hartung-Knapp/Sidik-Jonkman random effects model with the final participation rate as outcome, stratified for type of participants and method of data collection. RESULTS: The search yielded 168 eligible studies involving 367,616 potential participants and 33 strategies. Among patients, response-inducing strategies for paper-based questionnaires included unconditional monetary incentives (response ratio 1.15; 95% CI 1.09-1.21) and shorter questionnaires (1.04; 1.02-1.06). Among nonpatients, a personalized mode of delivery (1.47; 1.24-1.74), more expensive mailing type (1.25; 1.00-1.56), unconditional monetary incentives (1.24; 1.12-1.38), prenotification (1.12; 1.03-1.22), unconditional scratch lottery tickets (1.09; 1.01-1.18), and shorter questionnaires (1.06; 1.02-1.11) increased response rates to paper-based questionnaires. For Web-based questionnaires and interviews among nonpatients, response rates were increased by conditional lottery tickets (1.17; 1.02-1.34) and conditional monetary incentives (1.39; 1.01-1.91), respectively. CONCLUSION: Although the majority of strategies evaluated were unsuccessful, some may increase response rates to observational studies, particularly among nonpatients.

Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomes.

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures. The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (eg, dose or quantity of medication, income, or years of education). We used Monte Carlo simulations to examine the performance of different methods of using the GPS to estimate the effect of continuous exposures on binary outcomes. We examined covariate adjustment using the GPS and weighting using weights based on the inverse of the GPS. We examined both the use of ordinary least squares to estimate the propensity function and the use of the covariate balancing propensity score algorithm. The use of methods based on the GPS was compared with the use of G-computation. All methods resulted in essentially unbiased estimation of the population dose-response function. However, GPS-based weighting tended to result in estimates that displayed greater variability and had higher mean squared error when the magnitude of confounding was strong. Of the methods based on the GPS, covariate adjustment using the GPS tended to result in estimates with lower variability and mean squared error when the magnitude of confounding was strong. We illustrate the application of these methods by estimating the effect of average neighborhood income on the probability of death within 1 year of hospitalization for an acute myocardial infarction.

Assessing performance of sequential analysis methods for active drug safety surveillance using observational data.

The routine use of sequential methods is well established in clinical studies. Recently, there has been increasing interest in applying these methods to prospectively monitor the safety of newly approved drugs through accrual of real-world data. However, the application to marketed drugs using real-world data has been limited and work is needed to determine which sequential approaches are most suited to such data. In this study, the conditional sequential sampling procedure (CSSP), a group sequential method, was compared with a log-linear model with Poisson distribution (LLMP) through a SAS procedure (PROC GENMOD) combined with an alpha-spending function on two large longitudinal US administrative health claims databases. Relative performance in identifying known drug-outcome associations was examined using a set of 50 well-studied drug-outcome pairs. The study finds that neither method correctly identified all pairs but that LLMP often provides better ability and shorter time for identifying the known drug-outcome associations with superior computational performance when compared with CSSP, albeit with more false positives. With the features of flexible confounding control and ease of implementation, LLMP may be a good alternative or complement to CSSP.

The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening.

Observational analyses for causal inference often rely on real world data collected for purposes other than research. A frequent goal of these observational analyses is to use the data to emulate a hypothetical randomized experiment, i.e., the target trial, that mimics the design features of a true experiment, including a clear definition of time zero with synchronization of treatment assignment and determination of eligibility. We review a recent observational analysis that explicitly emulated a target trial of screening colonoscopy using insurance claims from U.S. Medicare. We then compare this explicit emulation with alternative, simpler observational analyses that do not synchronize treatment assignment and eligibility determination at time zero and/or do not allow for repeated eligibility. This empirical comparison suggests that lack of an explicit emulation of the target trial leads to biased estimates, and shows that allowing for repeated eligibility increases the statistical efficiency of the estimates.

Effects of expanding the look-back period to all available data in the assessment of covariates.

BACKGROUND: A fixed baseline period has been a common covariate assessment approach in pharmacoepidemiological studies from claims but may lead to high levels of covariate misclassification. Simulation studies have recommended expanding the look-back approach to all available data (AAD) for binary indicators of diagnoses, procedures, and medications, but there have been few real data analyses using this approach. OBJECTIVE: The objective of the study is to explore the impact on treatment effect estimates and covariate prevalence of expanding the look-back period within five validated studies in the Aetion system, a rapid cycle analytics platform. METHODS: We reran the five studies and assessed covariates using (i) a fixed window approach (usually 180 days before treatment initiation), (ii) AAD prior to treatment initiation, and (iii) AAD with a categorized by recency approach, where the most recent occurrence of a covariate was labeled as recent (occurring within the fixed window) or past (before the start of the fixed window). For each covariate assessment approach, we adjusted for covariates via propensity score matching. RESULTS: All studies had at least one covariate that had an increase in prevalence of 15% or higher from the fixed window to the AAD approach. However, there was little change in treatment effect estimates resulting from differing covariate assessment approaches. For example, in a study of acute coronary syndrome in high-intensity versus low-intensity statin users, the estimated hazard ratio from the fixed window approach was 1.11 (95% confidence interval 0.98, 1.25) versus 1.21 (1.07, 1.37) when using AAD and 1.19 (1.05, 1.35) using categorized by recency. CONCLUSION: Expanding the baseline period to AAD improved covariate sensitivity by capturing data that would otherwise be missed yet did not meaningfully change the overall treatment effect estimates compared with the fixed window approach. Copyright © 2017 John Wiley & Sons, Ltd.

Magnitude and direction of missing confounders had different consequences on treatment effect estimation in propensity score analysis.

OBJECTIVE: Propensity score (PS) analysis allows an unbiased estimate of treatment effects but assumes that all confounders are measured. We assessed the impact of omitting confounders from a PS analysis on clinical decision making. STUDY DESIGN AND SETTING: We conducted Monte Carlo simulations on hypothetical observational studies based on virtual populations and on the population from a large randomized trial (CRASH-2). In both series of simulations, PS analysis was conducted with all confounders and with omitted confounders, which were defined to have different strengths of association with the outcome and treatment exposure. After inverse probability of treatment weighting, we calculated the absolute risk differences and numbers needed to treat (NNT). RESULTS: In both series of simulations, omitting a confounder that was moderately associated with the outcome and exposure led to negligible bias on the NNT scale. The bias induced by omitting strongly positive confounding variables remained less than 15 patients to treat. Major bias and reversed effects were found only when omitting highly prevalent, strongly negative confounders that were similarly associated with the outcome and exposure with odds ratios greater than 4.00 (or <0.25). This omission was accompanied by a substantial decrease in analysis power. CONCLUSION: The omission of strongly negative confounding variables from a PS analysis can lead to incorrect clinical decision making. However, omitting these variables also decreases the analysis power, which may prevent the reporting of significant but misleading effects.

A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding.

Bias from unmeasured confounding is a persistent concern in observational studies, and sensitivity analysis has been proposed as a solution. In the recent years, probabilistic sensitivity analysis using either Monte Carlo sensitivity analysis (MCSA) or Bayesian sensitivity analysis (BSA) has emerged as a practical analytic strategy when there are multiple bias parameters inputs. BSA uses Bayes theorem to formally combine evidence from the prior distribution and the data. In contrast, MCSA samples bias parameters directly from the prior distribution. Intuitively, one would think that BSA and MCSA ought to give similar results. Both methods use similar models and the same (prior) probability distributions for the bias parameters. In this paper, we illustrate the surprising finding that BSA and MCSA can give very different results. Specifically, we demonstrate that MCSA can give inaccurate uncertainty assessments (e.g. 95% intervals) that do not reflect the data's influence on uncertainty about unmeasured confounding. Using a data example from epidemiology and simulation studies, we show that certain combinations of data and prior distributions can result in dramatic prior-to-posterior changes in uncertainty about the bias parameters. This occurs because the application of Bayes theorem in a non-identifiable model can sometimes rule out certain patterns of unmeasured confounding that are not compatible with the data. Consequently, the MCSA approach may give 95% intervals that are either too wide or too narrow and that do not have 95% frequentist coverage probability. Based on our findings, we recommend that analysts use BSA for probabilistic sensitivity analysis. Copyright © 2017 John Wiley & Sons, Ltd.

Establishing Evidence-Based Indications for Proton Therapy: An Overview of Current Clinical Trials.

PURPOSE: To review and assess ongoing proton beam therapy (PBT) clinical trials and to identify major gaps. METHODS AND MATERIALS: Active PBT clinical trials were identified from clinicaltrials.gov and the World Health Organization International Clinical Trials Platform Registry. Data on clinical trial disease site, age group, projected patient enrollment, expected start and end dates, study type, and funding source were extracted. RESULTS: A total of 122 active PBT clinical trials were identified, with target enrollment of >42,000 patients worldwide. Ninety-six trials (79%), with a median planned sample size of 68, were classified as interventional studies. Observational studies accounted for 21% of trials but 71% (n=29,852) of planned patient enrollment. The most common PBT clinical trials focus on gastrointestinal tract tumors (21%, n=26), tumors of the central nervous system (15%, n=18), and prostate cancer (12%, n=15). Five active studies (lung, esophagus, head and neck, prostate, breast) will randomize patients between protons and photons, and 3 will randomize patients between protons and carbon ion therapy. CONCLUSIONS: The PBT clinical trial portfolio is expanding rapidly. Although the majority of ongoing studies are interventional, the majority of patients will be accrued to observational studies. Future efforts should focus on strategies to encourage optimal patient enrollment and retention, with an emphasis on randomized, controlled trials, which will require support from third-party payers. Results of ongoing PBT studies should be evaluated in terms of comparative effectiveness, as well as incremental effectiveness and value offered by PBT in comparison with conventional radiation modalities.

Estimating the effect of treatment on binary outcomes using full matching on the propensity score.

Many non-experimental studies use propensity-score methods to estimate causal effects by balancing treatment and control groups on a set of observed baseline covariates. Full matching on the propensity score has emerged as a particularly effective and flexible method for utilizing all available data, and creating well-balanced treatment and comparison groups. However, full matching has been used infrequently with binary outcomes, and relatively little work has investigated the performance of full matching when estimating effects on binary outcomes. This paper describes methods that can be used for estimating the effect of treatment on binary outcomes when using full matching. It then used Monte Carlo simulations to evaluate the performance of these methods based on full matching (with and without a caliper), and compared their performance with that of nearest neighbour matching (with and without a caliper) and inverse probability of treatment weighting. The simulations varied the prevalence of the treatment and the strength of association between the covariates and treatment assignment. Results indicated that all of the approaches work well when the strength of confounding is relatively weak. With stronger confounding, the relative performance of the methods varies, with nearest neighbour matching with a caliper showing consistently good performance across a wide range of settings. We illustrate the approaches using a study estimating the effect of inpatient smoking cessation counselling on survival following hospitalization for a heart attack.