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Background Historically, a priori power and sample size calculations have not been routinely performed cost-effectiveness analyses (CEA), partly because the absence of published cost and effectiveness correlation and variance data, which are essential for power and sample size calculations. Importantly, the empirical correlation between cost and effectiveness has not been examined with respect to the estimation of value-for-money in clinical literature. Therefore, it is not well established if cost-effectiveness studies embedded within randomized-controlled-trials (RCTs) are under- or over-powered to detect changes in value-for-money. However, recently guidelines (such as those from ISPOR) and funding agencies have suggested sample size and power calculations should be considered in CEAs embedded in clinical trials. Methods We examined all RCTs conducted by the Canadian Cancer Trials Group with an embedded cost-effectiveness analysis. Variance and correlation of effectiveness and costs were derived from original-trial data. The incremental net benefit method was used to calculate the power of the cost-effectiveness analysis, with exploration of alternative correlation and willingness-to-pay values. Results We identified four trials for inclusion. We observed that a hypothetical scenario of correlation coefficient of zero between cost and effectiveness led to a conservative estimate of sample size. The cost-effectiveness analysis was under-powered to detect changes in value-for-money in two trials, at willingness-to-pay of $100,000. Based on our observations, we present six considerations for future economic evaluations, and an online program to help analysts include a priori sample size and power calculations in future clinical trials. Conclusion The correlation between cost and effectiveness had a potentially meaningful impact on the power and variance of value-for-money estimates in the examined cost-effectiveness analyses. Therefore, the six considerations and online program, may facilitate a priori power calculations in embedded cost-effectiveness analyses in future clinical trials. Highlights • Analysts may use the online program presented in the present study to examine the a priori power of cost-effectiveness analyses. • Analysts may potentially apply the considerations presented in this paper in the planning stage of future cost-effectiveness analyses.

Early-stage Hodgkin lymphoma has become one of the most curable hematologic malignancies. Depending upon the disease location, possible toxicities, and patient preference, chemotherapy alone with ABVD remains an accepted treatment modality for this disease. There remains a paucity of data regarding the longitudinal trajectory of health-related quality of life (HRQoL) in patients treated for HL. The impact of disease and treatment on HRQoL is increasingly important to understand as the number of long-term survivors increases. We report the longitudinal HRQoL using data prospectively collected from diagnosis up to 10 years post-treatment in the ABVD arm of the HD.6 randomized controlled trial for early-stage HL patients ( N =169). We analyzed HRQoL using the EORTC QLQ-C30 collected at baseline, 3 months, 6 months, and 12 months after completion of chemotherapy and yearly up to year 10. Clinically and statistically significant improvements were noted for specific domains including emotional (3 months post-treatment), social (12 months post-treatment) and financial functioning (2 years post-treatment), and the specific symptom of fatigue (6 months post-treatment) during the follow-up period. To our knowledge, this is the first prospective, longitudinal analysis of HRQoL specifically among patients with early-stage HL treated with ABVD therapy alone. Although improvements were noted, sustained clinically and statistically significant improvements were noted only in select symptoms emphasizing the need to better understand and optimize HRQoL among this patient group.

BackgroundEconomic analyses based on clinical trial data are costly and time consuming, and alternative methods for performing economic analyses should be explored.Objective and methodsIn this perspective, we examine the emerging role of administrative data for economic analyses in cancer.ResultsCompared with routinely collected clinical trial data, routinely collected administrative data have several strengths including high capture rates for healthcare encounters, less resource utilisation, low rates of misclassification, long follow-up periods and the opportunity to collect data points not traditionally captured in clinical trials. However, there are also limitations including the need for accurate data linkage across multiple databases and systems, the costs and time associated with data linkage, the potential time lag between trial data collection and the availability of administrative data, and limited data on quality of life, toxicity and indirect costs. In this perspective, we identify important barriers and potential solutions to performing economic analyses for oncology using administrative data, and outline strategies to increase research in this field.ConclusionThe use of routinely collected administrative data sets for economic analyses of clinical trials presents a unique opportunity that could complement and validate economic analyses based on trial-level data.

Background When cancer treatments have similar oncologic outcomes, the number of days with in-person healthcare contact (“”contact days’’) can help contextualize expected time use with each treatment. We assessed contact days in a completed randomized clinical trial. Patients and Methods We conducted a secondary analysis of the CCTG LY.12 RCT that evaluated 2-3 cycles of gemcitabine, dexamethasone, and cisplatin (GDP) vs. dexamethasone, cytarabine, and cisplatin (DHAP) in 619 patients with relapsed/refractory lymphoma prior to stem cell transplant. Primary analyses reported similar response rates and survival. We calculated patient-level “contact days” by analyzing trial forms. The study period was from assignment to progression or transplant. Days without healthcare contact were considered “home days’’. We compared measures of contact days across arms. Results The study period was longer in the GDP arm (median 50, vs. 47 days, P = .007). Contact days were comparable in both arms (median 18 vs 19, P = 0.79), but home days were higher in the GDP arm (median 33 vs 28, P < .001). The proportion of contact days was lower in the GDP arm (34%, vs. 38%, P = .009). The GDP arm experienced more contact days related to planned outpatient chemotherapy (median, 10 vs. 8 days), but the DHAP arm experienced many more inpatient contact days (median, 11 vs. 0 days). Conclusions Measures of time use, such as contact days, can be extracted from RCTs. In LY.12, despite comparable oncologic outcomes, GDP was associated with fewer contact days. Such information can guide decision-making for patients with hematological cancers, who already face significant healthcare contact. When cancer treatments have similar oncologic outcomes, the number of days with in-person healthcare contact (“contact days’’) can help contextualize expected time use with each treatment. This secondary analysis of the CCTG LY.12 trial analyzed trial forms to assess days with and without health care contact.

Abstract Background Given the intensive resources required to conduct economic analyses in clinical trials, a key need is identifying scalable measures of costs. Contact days—days with health care contact outside the home—may represent such a practical measure. Methods We conducted a secondary analysis of a trial that evaluated two pre-transplant chemotherapy regimens for lymphoma. We used trial resource use and patient-reported data to calculate contact days, direct costs, and indirect costs such as lost productivity. We assessed the association between the number of contact days and cost outcomes using linear regression models and Pearson correlation coefficients. Results Contact days were moderately correlated with direct costs (r = .47, $762/ contact day, P < .0001), and strongly correlated with direct costs in the DHAP arm (r = 0.60, $727/ contact day, P < .0001). Contact days were very weakly correlated with pooled indirect costs (r 0.19, $60/ contact day, P = .0003). Among the 3 indirect cost outcomes, the relationship was strongest with paid caregiving hours (r = 0.33, 1.8 hours/ contact day, P < .0001) and weakest for unpaid hours provided by informal care partners (r = .06, .7 hours/ contact day, P = .247). Results were robust when zeroing out costs of hospitalization in the arm receiving inpatient chemotherapy and when evaluating indirect costs among patients working full-time. Conclusions Contact days have the potential as a surrogate measure of direct health system costs, which deserves further exploration. The weak correlation with indirect cost outcomes suggests that the extent of true patient and care partner burdens extends beyond just the number of contact days. Trial registration: ClinicalTrials.gov Identifier: NCT00078949

•Humoral immune response (measured by anti-S levels) increased in the cohort with subsequent doses of vaccine.•Sero-response from negative to positive anti-S above threshold occurred post-dose 4 and dose 5 in the whole cohort.•Patients who received anti-CD20 and anti-CD38 therapy had lower anti-S levels than the rest of the cohort but these levels increased post-dose 4 and post-dose 5. Immune response to COVID-19 vaccine is diminished in patients with hematologic malignancy. There is limited data regarding response to vaccine doses in these patients. To quantify the humoral immune response engendered by 4th and subsequent doses of SARS-CoV-2 vaccination as measured by anti-Spike (anti-S) antibody levels, based on dried blood spot (DBS) testing, in patients with hematologic malignancies. Anti-S binds to the spike protein of the SARS-CoV-2 virus and is indicative of vaccine immunogenicity. We conducted a prospective study of hematologic malignancies between August 2021 and January 2023 at 12 sites across Canada. Participants were followed longitudinally and submitted finger-prick DBS cards at set intervals associated with vaccination. Samples were processed via high throughput ELISA assay to detect serum antibodies against nucleocapsid (N) and spike (S) proteins. We obtained 3071 samples on 790 unique patients. Of these, 372 unique participants with 1840 samples had anti-S results available post-4th, 5th or 6th COVID-19 vaccine dose and were included for analysis. Three hundred thirty-three patients of the 372 participants submitted a DBS sample post 4th dose. Of these, 257 patients (77.2%) had a positive anti-S antibody. A total of 198 patients had paired samples pre- and post-dose 4, of which 59 (29.7%) had a negative anti-S antibody pre-dose 4. Of these, 20 (33.4%) developed positive anti-S antibody post-dose 4. One hundred forty-nine patients submitted a DBS sample post-dose 5. Of these, 135 patients (90.6%) had positive anti-S antibody. A total of 52 had paired samples pre- and post-dose 5. Six (8.7%) had a negative anti-S antibody pre-dose 5, of which two (33.3%) developed positive anti-S antibody post-dose 5. Of these 372 patients, 123 (34%) reported COVID-19 infection and 4 (1%) had a COVID-19 related hospitalization. There were no reported deaths from COVID-19. This prospective cohort study showed that humoral immune response improved with subsequent doses of COVID-19 vaccines.

Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are severe myeloid disorders associated with significant morbidity and mortality. Because of patient and disease factors, many older adults are treated as outpatients with less-intensive therapy. Optimal supportive care strategies to minimize bleeding and infectious complications in this patient population have not been systematically evaluated. We conducted a survey of Canadian hematologists to explore current practice in the use of tranexamic acid (TXA) and prophylactic antimicrobials in patients with MDS/AML treated with less-intensive therapy, and to evaluate equipoise for future trials. Survey items were generated through a combination of literature review and discussion with content experts. The survey was disseminated to 304 potential respondents with a response rate of 52%. Prophylactic platelet transfusions were used by 95%, while prophylactic TXA was used by 57%; the most frequent reason for not using TXA was uncertainty about benefit or harm. Use of prophylactic antimicrobials varied by chemotherapy regimen. If antimicrobial prophylaxis was used, the most frequently prescribed antibacterials were fluroquinolones (90%) and trimethoprim/sulfamethoxazole (21%); the most commonly used antifungals were fluconazole (66%) and voriconazole (36%). The most common reason for not using prophylactic antimicrobials was insufficient evidence of benefit. Most respondents agreed that clinical trials are needed to define the use of TXA and prophylactic antimicrobials in this patient population. Among survey respondents, there was variation in the use of supportive care strategies to address bleeding and infection risk in older adults with MDS/AML. The results of this survey will help to inform clinical trials to assess the benefits and risks of these prophylactic strategies.

EDITOR-Graffy et al's finding that offering voluntary support to breastfeeding mothers did not significantly affect breastfeeding rates is not surprising. 1 Many will know the chaos caused by a new baby's arrival.

The causal effect of an exposure on an outcome of interest in an observational study cannot be estimated directly if the confounding variables are not controlled. Many approaches are available for estimating the causal effect of an exposure. In this manuscript, we demonstrate the advantages associated with using inverse probability weighting (IPW) and doubly robust estimation of the odds ratio in terms of reduced bias. IPW approach can be used to adjust for confounding variables and provide unbiased estimates of the exposure's causal effect. For cluster-structured data, as is common in animal populations, inverse conditional probability weighting (ICPW) approach can provide a robust estimation of the causal effect. Doubly robust estimation can provide a robust method even when the specification of the model form is uncertain. In this paper, the usage of IPW, ICPW, and doubly robust approaches are illustrated with a subset of data with complete covariates from the Australian-based National Bovine Respiratory Disease Initiative as well as simulated data. We evaluate the causal effect of prior bovine viral diarrhea exposure on bovine respiratory disease in feedlot cattle. The results show that the IPW, ICPW and doubly robust approaches would provide a more accurate estimation of the exposure effect than the traditional outcome regression model, and doubly robust approaches are the most preferable overall.