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Supplemental material, sj-docx-1-aop-10.1177_10600280221146878 for Economic Evaluation of Three BRAF + MEK Inhibitors for the Treatment of Advanced Unresectable Melanoma With BRAF Mutation From a US Payer Perspective by Shiraz Halloush, Nimer S. Alkhatib, Abdulaali R. Almutairi, Mathias Calamia, Hala Halawah, Mavis Obeng-Kusi, Martin Hoyle, Omar Rashdan, Jim Koeller and Ivo Abraham in Annals of Pharmacotherapy

To assess the cost-efficiency and expanded access of three rituximab biosimilars versus the reference rituximab from the perspective of the Jordanian national health payer. A 1-year cost-efficiency and expanded access model of conversion from reference rituximab (Mabthera) to the approved biosimilars (Truxima, Rixathon, and Tromax) to assess five metrics: total annual cost to treat a hypothetical patient; head-to-head cost comparison; changes in patients’ access to rituximab; number-needed-to-convert (NNC) to provide an additional 10 patients access to a rituximab treatment; and relative amount of Jordanian Dinar (JOD) spent on rituximab options. The model included rituximab doses at 100 mg/10 ml and 500 mg/50 ml and considered both cost-saving and cost-wastage scenarios. Costs of treatments were based on the fiscal year 2022 tender prices received by the Joint Procurement Department (JPD). Rixathon was associated with the lowest average annual cost per patient (JOD2,860) across all six indications among all rituximab comparators, followed by Truxima (JOD4,240), Tromax (JOD4,365) and reference Mabthera (JOD11,431). The highest percentage of patient access to rituximab treatment (321%) was achieved when switching patients from Mabthera to Rixathon in the RA and PV indications. At four patients, Rixathon was associated with the lowest NNC to provide an additional 10 patients access to rituximab treatment. For each JOD1 spent on Rixathon, an additional JOD3.21 must be spent on Mabthera, an additional JOD0.55 on Tromax, and an additional JOD0.53 on Truxima. Rituximab biosimilars were associated with cost savings in all approved indications in Jordan compared to reference rituximab. Rixathon was associated with the lowest annual cost, the highest percentage of expanded patient access for all six indications, and the lowest NNC providing 10 additional patients with access.

Additional file 6 Table S6. Distribution of Most Likely Etiology of L-PRF. Distribution of most likely etiology of L-PRF, by total cohort, pulmonary subset, and extrapulmonary subset.

Additional file 1 Table S1. Distribution of Age (by Decade) Used in Matching Process. Distribution of age (by decade) used in matching of case-control pairs.

Additional file 5 Table S5. Definitions of Comorbidities, Risk Factors, and Outcome Variables. Definitions of comorbidities, risk factors, and outcome variables used in analysis of case-control pairs.

Supplemental material, sj-docx-1-aop-10.1177_10600280221115299 for Effectiveness and Safety of Enoxaparin Versus Unfractionated Heparin as Thromboprophylaxis in Hospitalized COVID-19 Patients: Real-World Evidence by Lina H. AlLehaibi, Mukhtar Alomar, Abdulaziz Almulhim, Sarah Al-Makki, Nazar R. Alrwaili, Shahad Al-Bassam, Semat Alsultan, Jenan Al Saeed, Mohammad Alsheef, Ivo Abraham and Ahmad Alamer in Annals of Pharmacotherapy

Additional file 2 Table S2. Distribution of Hospital Site Used in Matching Process. Distribution of hospital site used in matching of case-control pairs.

Additional file 4 Table S4. Distribution of Surgical Procedure (by Modified Clinical Classification) Used in Matching Process. Distribution of surgical procedure (by modified clinical classification group) used in matching of case-control pairs.

Additional file 3 Table S3. Distribution of Surgical Procedure (by Body Organ or System) Used in Matching Process. Distribution of surgical procedure (by body organ or system) used in matching of case-control pairs.

Supplemental material, sj-docx-1-taw-10.1177_2042098621991279 for Association of immune-checkpoint inhibitors and the risk of immune-related colitis among elderly patients with advanced melanoma: real-world evidence from the SEER–Medicare database by Abdulaali R. Almutairi, Marion Slack, Brian L. Erstad, Ali McBride and Ivo Abraham in Therapeutic Advances in Drug Safety

Aims: Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The second dimension (δ2) estimates prices on the basis of four willingness-to-pay (WTP) thresholds. We describe this dimension’s methodology and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib. Materials and methods: Eight WTP scenarios based on four levels of real gross domestic product per capita (3xGDP/capita) and two market conditions (monopolistic versus competitive) were assumed. The incremental cost-utility ratio (ICUR) was applied to differently to both markets. In the monopolistic market, assuming no competitors, the cost/QALY ratio for a drug was used; whereas in the competitive market, assuming competitors, the incremental cost-utility ratio (ICUR) was applied. One-way sensitivity analyses were performed and predictive equations were specified to estimate the prices of treatment for the resulting eight WTP scenarios; for which subsequently the average and standard deviation were calculated. A gamma distribution was specified and Monte Carlo Simulation (MCS) was applied to estimate the dimension-specific price based on WTP (DSPWTP). A proof-of-concept exercise with osimertinib in NSCLC was performed for two hypothetical outcome-based contracts: 1-year (2019-2020) and 2-year (2019-2021). The 2018 wholesale acquisition cost (WAC) of $14,616 (30-day prescription) was used to estimate the DSPWTP for each contract. Results: The 1-year estimates averaged $4,654 (SD=$6,462) and the MCS yielded a DSPWTP of $4,547 or -68.89% of the 2018 WAC for a 30-day prescription. The 2-year estimates averaged $4,7667 (SD=$6,480) with the MCS generating a DSPWTP of $4,704 or -67.82% of the WAC. Conclusions: We demonstrated that WTP-based methods that include various WTP thresholds and market conditions generate price estimates across these thresholds and market conditions that can be integrated into our proposed Six Delta platform for outcome-based pricing/contracting.

Aims: Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The fourth dimension (δ4) estimates prices on the basis of assessments of the safety of the drug using an ex ante analysis based on clinical trial data. We describe this dimension’s methodology and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib. Materials and methods: The safety-based pricing dimension utilizes a four-step method: 1) pooling adverse events (AE), standardizing, estimating 95%CIs, and adjusting for time; 2) estimating correction factors and corrected probabilities of AEs; 3) estimating the probability of at least one adverse event (AE) occurring and leading to treatment discontinuation; and 4) estimating ranges for payback percentages and performing Monte Carlo Simulation to estimate a DSPSafety. A proof-of-concept exercise with osimertinib in NSCLC was performed for two hypothetical outcome-based contracts: 1-year (2019-2020) and 2-year (2019-2021). We estimated the DSPSafety based on the grade 3/4 AEs observed for osimertinib and standard of care. The 2018 wholesale acquisition cost (WAC) of osimertinib at $14,616 for a 30-day prescription was used. Results: AEs3/4 were retrieved from the FLAURA trial. In the 1-year contract, the DSPSafety of osimertinib was estimated at $14,627 (or +0.08% the 2018 WAC) for a 30-day prescription. In the 2-year contract, the DSPSafety of osimertinib was estimated at $14,516 (or −0.68% the 2018 WAC) for a 30-day prescription. Conclusions: We demonstrated that ex ante pricing methods-based paybacks for safety issues leading to treatment discontinuation can be integrated into our proposed Six Delta platform for outcome-based pricing/contracting.

Aims: Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The first dimension (δ1) estimates prices on the basis of cost-effectiveness (CEA) and cost-utility analysis (CUA). We describe this dimension’s methodology and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib. Materials and methods: CEA and CUA were performed using established methods. Probabilistic sensitivity analyses (PSA) were performed to generate cost-effectiveness acceptability curves (CEAC), specifically the PSA incremental cost-effectiveness (PSA ICER) and incremental cost-utility ratio generated CEACs (PSA ICUR). Price of treatment was estimated at three certainty levels (0%, turning point%, 100%). The marketed drug price at turning point was used to estimate prices at 0% and 100% certainty levels, as per PSA ICER and PSA ICUR-generated CEACs. The resulting prices were pooled, inflated, and simulated by Monte Carlo Simulation (MCS) methods to estimate the dimension-specific price based on CEA and CUA (DSPCEA/CUA). A proof-of-concept exercise with osimertinib in NSCLC was performed for two hypothetical outcome-based contracts: 1-year (2019-2020) and 2-years (2019-2021). Results: Turning points were estimated at the 50% certainty level in both PSA ICER and ICUR-generated CEACS. At these points, the wholesale acquisition cost for osimertinib was $14,616 (30-day prescription); inflated by 0.44% for 1-year and by 0.72% for 2-year contracts. Additional prices at 0% and 100% certainty levels were quantified based on the PSA ICER and ICUR-generated CEACs. The MCS yielded a DSPCEA/CUA of $16,391 for the 1-year contract and a DSPCEA/CUA at $16,677 for the 2-year contract for a 30-day prescription. Conclusions: We demonstrated that conventional CEA and CUA methods generate price estimates at varying levels of certainty that can be integrated into our proposed Six Delta platform for outcome-based pricing/contracting.

Aims: Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The fifth dimension (δ5) estimates prices on the basis of the risk of efficacy failure of a drug. We describe this dimension’s methodology and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib. Materials and methods: The risk of efficacy failure pricing dimension utilizes a seven-step method: 1) defining risk; 2) extracting data; 3) predicting models; 4) performing Monte Carlo Simulation (MCS) to estimate risk of efficacy failure; 5) estimating ranges for a payback; 6) adjusting for medical inflation; and 7) performing Monte Carlo Simulation (MCS) to estimate the DSPRisk of efficacy failure. A proof-of-concept exercise with osimertinib in NSCLC was performed for two hypothetical outcome-based contracts: 1-year (2019-2020) and 2-year (2019-2021). We estimated the risk of efficacy failure for osimertinib in terms of overall and progression-free survival versus standard of care. We used the estimated risk to estimate the price reduction on the wholesale acquisition cost (WAC) for the two hypothetical contracts: a 1-year (2019–2020) and 2-year contract (2019–2021). From this we estimated the DSPRisk of efficacy failure. Results: Based on the risk of OS and PFS efficacy failure for osimertinib in OS and PFS, in the 1-year contract, the DSPRisk of efficacy failure was estimated at $12,652 (or −13.44% the 2018 WAC) for a 30-day prescription. For the 2-year contract (2019-2021), the DSPRisk of efficacy failure was estimated at $13,019 (or −10.93% the 2018 WAC). Conclusions: We demonstrated that pricing methods based on risk of efficacy failure methods can be integrated into our proposed Six Delta platform for outcome-based pricing/contracting.

Aims: Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The third dimension (δ3) estimates prices on the basis of international drug price referencing methods. We describe this dimension’s methodology and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib. Materials and methods: The reference-based pricing dimension utilizes a six-step method: 1) selecting foreign countries based on a set of four criteria (drug is available in the foreign country, price information is available in the foreign country, foreign countries are members within the organization for Economic Co-operation and Development, pricing methods in the foreign countries involve value assessment); 2) adjusting for exchange rates; 3) generating reference price (RP) scenarios; 4) adjusting with the medical inflation rate; 5) pooling all generated RP scenarios and calculating average and standard deviation (SD); 6) and Monte Carlo Simulation (MCS) to estimate the dimension-specific DSPReference. A proof-of-concept exercise with osimertinib in NSCLC was performed for two hypothetical outcome-based contracts: 1-year (2019-2020) and 2-year (2019-2021). Results: The United Kingdom and Canada met the four criteria. For the osimertinib 1-year contract price, the average of eight RP scenarios, adjusted for inflation by 0.44%, was $8,892 (SD=$2,606) for a 30-day prescription. MCS yielded a DSPReference estimate of $9,395 or -35.72% of the wholesale acquisition cost (WAC) of $14,616. For the 2-year contract, the average, adjusted for inflation by 0.72%, was $8,928 (SD=$2,610). MCS yielded a DSPReference estimate of $9,442 or -35.40% of the WAC of $14,616. Conclusions: We demonstrated that international price referencing methods can be integrated into our proposed Six Delta platform for outcome-based pricing/contracting.

Aims: Six Delta is a six-dimensional independent platform for outcome-based pricing/contracting. The sixth dimension (δ6) estimates prices on the basis of adherence to the prescribed regimen, whereby manufacturers provide payers with adherence-enhancing programs and whereby payers implement these programs and provide adherence data to the manufacturer. We describe this dimension’s methodology and present a proof-of-concept application to the treatment of non-small cell lung cancer (NSCLC) with EGFR mutation with osimertinib. Materials and methods: We propose two paybacks based on adherence: in-advance (based on clinical trial data) and in-arrear (based on real-world data). The risk of efficacy failure pricing dimension utilizes a seven-step method: 1) defining efficacy endpoints; 2) extracting data; 3) predicting models; 4) estimating in-advance and in-arrear paybacks; 5) suggesting ranges for in-advance and in-arrear paybacks; 6) adjusting for medical inflation; and 7) performing Monte Carlo Simulation (MCS) to estimate the DSPAdherence. A proof-of-concept exercise with osimertinib in NSCLC was performed for two hypothetical outcome-based contracts: 1-year (2019-2020) and 2-year (2019-2021). The 2018 wholesale acquisition cost (WAC) for a 30-day prescription was used and inflated as needed. Herein, the DSPAdherence is estimated exclusively in terms of in-advance payback because real-world data about osimertinib are not yet available and thus the in-arrear payback cannot yet be estimated. Results: For the 1-year contract, the average price for osimertinib was $13,798 (SD=$1,265) and the DSPAdherence was $13,785 (or -5.69% of the 2018 WAC) for a 30-day prescription. For the 2-year contract, the average price was $12,555 (SD=$2,847) and the DSPAdherence was $12,582 (or -13.92% of the 2018 WAC). Conclusions: We demonstrated that adherence-based pricing methods can be integrated into our proposed Six Delta platform for outcome-based pricing/contracting. The proof-of-concept exercise needs to be expanded with the in-arrear pricing method based on real world data to be secured.

Supplementary List of all Ethical Review Committees for MONITOR-CKD5. (DOCX 99 kb)

Search Strategy. Figure S1. Forest Plots for Bacteriologic Failure and Adverse Events in All Included Studies. Figure S2. Forest Plots for Subgroup Analyses of Clinical Failure. Figure S3. Funnel Plot of Included Studies for Clinical Failure. Table S1. PRISMA Check List. Table S2. Quality Assessment of Included Studies. Table S3. Characteristics of Excluded Full-Text Articles. (DOC 428Â kb)

Funnel plot. Figure S2. Subgroup analysis of OS rate from available data. Figure S3. Subgroup analysis of ORR from available data. Figure S4. Comparison of OS rates between HMA vs. LDAC in AML patients. Table S1. Search detail in PubMed, EMBASE, and Cochrane database of systematic review. Table S2. Additional characteristics of randomized trials. Table S3. Risk of bias assessment of studies according to Cochrane risk bias assessment tool. Table S4. Characteristics of trials (ad hoc studies) comparing HMAs and LDAC in AML patients. (DOC 1145Â kb)

AIM: To determine imatinib nonadherence rates in patients with gastrointestinal tumors (GIST) over 90 days. PATIENTS AND METHODS: A prospective 90-day observational, open-label, multicenter study was carried out of 28 evaluable GIST patients on imatinib. Nonadherence behavior was measured using a 4-item patient interview. Clinicians, patients, and collaterals rated perceived patient adherence on a 0-100 VAS scale. RESULTS: Nonadherence rates in the 4 weeks prior to baseline and follow-up were 29% (95% CI=26-32) and 24% (95% CI=21-27, p>0.05). Mean VAS ratings of perceived adherence ranged from 95.2 ± 10.2 to 97.3 ± 4.8 (p>0.05 for time and source of rating). Correlations between perceptions of and actual adherence behavior were negative. CONCLUSION: In this first study on imatinib nonadherence in GIST patients, rates were similar to those observed in patients with chronic myeloid leukemia, higher than clinically expected and exceeding meta-analytic estimates for cancer. Nonadherence rates were consistent across the 90-day period. Nonadherence behavior should be assessed by clinicians.

Additional file 2: Table S1. Ranking algorithm. Eight CART iterations, algorithm conditions imposed, and number of biomarkers used. Table S2. Logistic regression analysis of mortality prediction capacity of the eight ARDS biomarkers-Day Zero. Table S3. Logistic regression analysis of mortality prediction capacity of the eight ARDS biomarkers-Day Seven. Table S4. Logistic regression analysis to assess the mortality prediction for change in measurements for the eight biomarkers. Table S5. Number of individuals per latent class. Table S6. Ranking of ARDS biomarker importance in predicting mortality.

Additional file 2: Table S1. Ranking algorithm. Eight CART iterations, algorithm conditions imposed, and number of biomarkers used. Table S2. Logistic regression analysis of mortality prediction capacity of the eight ARDS biomarkers-Day Zero. Table S3. Logistic regression analysis of mortality prediction capacity of the eight ARDS biomarkers-Day Seven. Table S4. Logistic regression analysis to assess the mortality prediction for change in measurements for the eight biomarkers. Table S5. Number of individuals per latent class. Table S6. Ranking of ARDS biomarker importance in predicting mortality.