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Time-to-event endpoints for patient-reported outcomes, such as time to deterioration of symptoms or function, are frequently used in cancer clinical trials. Although time-to-deterioration endpoints might seem familiar to cancer researchers for being similar to survival or disease-progression endpoints, there are unique considerations associated with their use. The complexity of time-to-deterioration endpoints should be weighed against the information that they add to the tumour, survival, and safety data used to inform the risks and benefits of an investigational drug. Here we use the estimand framework to show how analytical decisions answer different clinical questions of interest, some of which might be uninformative. Challenges including the consideration of intercurrent events, the difficulty in maintaining adequate completion rates, and considerable patient and trial burden from long-term, serial, patient-reported outcome measurements render time to deterioration a problematic approach for widespread use. For trials in which a comparative benefit in symptoms or function is an objective, an analysis at pre-specified relevant timepoints could be a better approach. [ABSTRACT FROM AUTHOR]

Patient-reported outcome (PRO) measures describe how a patient feels or functions and are increasingly being used in benefit-risk assessments in the development of cancer drugs. However, PRO research objectives are often ill-defined in clinical cancer trials, which can lead to misleading conclusions about patient experiences. The estimand framework is a structured approach to aligning a clinical trial objective with the study design, including endpoints and analysis. The estimand framework uses a multidisciplinary approach and can improve design, analysis, and interpretation of PRO results. On the basis of the International Council for Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use E9(R1) addendum, we provide an overview of the estimand framework intended for a multistakeholder audience. We apply the estimand framework to a hypothetical trial for breast cancer, using physical function to develop specific PRO research objectives. This Policy Review is not an endorsement of a specific study design or outcome; rather, it is meant to show the application of principles of the estimand framework to research study design and add to ongoing discussion. Use of the estimand framework to review medical products and label PROs in oncology can improve communication between stakeholders and ultimately provide a clearer interpretation of patient experience in the development of oncological drugs. [ABSTRACT FROM AUTHOR]

With the advent of patient-focused drug development, the US Food and Drug Administration (FDA) has redoubled its efforts to review patient-reported outcome (PRO) data in cancer trials submitted as part of a drug's marketing application. This Review aims to characterise the statistical analysis of PRO data from pivotal lung cancer trials submitted to support FDA drug approval between January, 2008, and December, 2017. For each trial and PRO instrument identified, we evaluated prespecified PRO concepts, statistical analysis, missing data and sensitivity analysis, instrument completion, and clinical relevance. Of the 37 pivotal lung cancer trials used to support FDA drug approval, 25 (68%) trials included PRO measures. The most common prespecified PRO concepts were cough, dyspnoea, and chest pain. At the trial level, the most common statistical analyses were descriptive (24 trials [96%]), followed by time-to-event analyses (19 trials [76%]), longitudinal analyses (12 trials [48%]), and basic inferential tests or general linear models (10 trials [40%]). Our findings indicate a wide variation in the analytic techniques and data presentation methods used, with very few trials reporting clear PRO research objectives and sensitivity analyses for PRO results. Our work further supports the need for focused research objectives to justify and to guide the analytic strategy of PROs to facilitate the interpretation of patient experience. [ABSTRACT FROM AUTHOR]

We extend the pattern-mixture approach to handle missing continuous outcome data in longitudinal cluster randomized trials, which randomize groups of individuals to treatment arms, rather than the individuals themselves. Individuals who drop out at the same time point are grouped into the same dropout pattern. We approach extrapolation of the pattern-mixture model by applying multilevel multiple imputation, which imputes missing values while appropriately accounting for the hierarchical data structure found in cluster randomized trials. To assess parameters of interest under various missing data assumptions, imputed values are multiplied by a sensitivity parameter, k, which increases or decreases imputed values. Using simulated data, we show that estimates of parameters of interest can vary widely under differing missing data assumptions. We conduct a sensitivity analysis using real data from a cluster randomized trial by increasing k until the treatment effect inference changes. By performing a sensitivity analysis for missing data, researchers can assess whether certain missing data assumptions are reasonable for their cluster randomized trial. [ABSTRACT FROM AUTHOR]

Background: Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs.Methods: We systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. We evaluated the primary analysis and determined whether it was at the cluster or individual level.Results: Of the 86 included CRTs, 80 (93%) trials reported some missing outcome data. Of those reporting missing data, the median percent of individuals with a missing outcome was 19% (range 0.5 to 90%). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55%), whereas 18 (22%) used mixed models, six (8%) used single imputation, four (5%) used unweighted generalized estimating equations, and two (2%) used multiple imputation. Fourteen (16%) trials reported a sensitivity analysis for missing data, but most assumed the same missing data mechanism as in the primary analysis. Overall, 67 (78%) trials accounted for clustering in the primary analysis.Conclusions: High rates of missing outcome data are present in the majority of CRTs, yet handling missing data in practice remains suboptimal. Researchers and applied statisticians should carry out appropriate missing data methods, which are valid under plausible assumptions in order to increase statistical power in trials and reduce the possibility of bias. Sensitivity analysis should be performed, with weakened assumptions regarding the missing data mechanism to explore the robustness of results reported in the primary analysis. [ABSTRACT FROM AUTHOR]

Summary: Background: Non‐inferiority (NI) and equivalence clinical trials test whether a new treatment is therapeutically no worse than, or equivalent to, an existing standard of care. Missing data in clinical trials have been shown to reduce statistical power and potentially bias estimates of effect size; however, in NI and equivalence trials, they present additional issues. For instance, they may decrease sensitivity to differences between treatment groups and bias toward the alternative hypothesis of NI (or equivalence). Aims: Our primary aim was to review the extent of and methods for handling missing data (model‐based methods, single imputation, multiple imputation, complete case), the analysis sets used (Intention‐To‐Treat, Per‐Protocol, or both), and whether sensitivity analyses were used to explore departures from assumptions about the missing data. Methods: We conducted a systematic review of NI and equivalence trials published between May 2015 and April 2016 by searching the PubMed database. Articles were reviewed primarily by 2 reviewers, with 6 articles reviewed by both reviewers to establish consensus. Results: Of 109 selected articles, 93% reported some missing data in the primary outcome. Among those, 50% reported complete case analysis, and 28% reported single imputation approaches for handling missing data. Only 32% reported conducting analyses of both intention‐to‐treat and per‐protocol populations. Only 11% conducted any sensitivity analyses to test assumptions with respect to missing data. Conclusion: Missing data are common in NI and equivalence trials, and they are often handled by methods which may bias estimates and lead to incorrect conclusions. [ABSTRACT FROM AUTHOR]

Patient-reported outcomes (PROs) are increasingly used in single-arm cancer studies. We reviewed 60 papers published between 2018 and 2021 of single-arm studies of cancer treatment with PRO data for current practice on design, analysis, reporting, and interpretation. We further examined the studies' handling of potential bias and how they informed decision making. Most studies (58; 97%) analysed PROs without stating a predefined research hypothesis. 13 (22%) of the 60 studies used a PRO as a primary or co-primary endpoint. Definitions of PRO objectives, study population, endpoints, and missing data strategies varied widely. 23 studies (38%) compared the PRO data with external information, most often by using a clinically important difference value; one study used a historical control group. Appropriateness of methods to handle missing data and intercurrent events (including death) were seldom discussed. Most studies (51; 85%) concluded that PRO results supported treatment. Conducting and reporting of PROs in cancer single-arm studies need standards and a critical discussion of statistical methods and possible biases. These findings will guide the Setting International Standards in Analysing Patient-Reported Outcomes and Quality of Life Data in Cancer Clinical Trials–Innovative Medicines Initiative (SISAQOL-IMI) in developing recommendations for the use of PRO-measures in single-arm studies. [ABSTRACT FROM AUTHOR]

Background: The Hospital Anxiety and Depression Scale (HADS) is a widely used questionnaire in health research, but there is little guidance on how to handle missing items. We aimed to investigate approaches to handling item non-response, varying sample size, proportion of subjects with missing items, proportion of missing items per subject, and the missingness mechanism. Methods: We performed a simulation study based on anxiety and depression data among cancer survivors and patients. Item level data were deleted according to random, demographic, and subscale dependent missingness mechanisms. Seven methods for handling missing items were assessed for bias and imprecision. Imputation, imputation conditional on the number of non-missing items, and complete case approaches were used. One thousand datasets were simulated for each parameter combination. Results: All methods were most sensitive when missingness was dependent on the subscale (i.e., higher values of depression leads to higher levels of missingness). The worst performing approach was to analyze only individuals with complete data. The best performing imputation methods depended on whether inference was targeted at the individual or at the population. Conclusions: We recommend the 'half rule' using individual subscale means when using the HADS scores at the individual level (e.g. screening). For population inference, we recommend relaxing the requirement that at least half the items be answered to minimize missing scores. [ABSTRACT FROM AUTHOR]

Objectives: Many trials conclude "no clinically meaningful detriment" to health-related quality of life (HRQL) or function between arms, even when notable differential toxicity is observed. Mean change from baseline analyses of function or HRQL can possibly obscure important change in subgroups experiencing symptomatic toxicity. We evaluate the impact of diarrhea, a key treatment arm toxicity, on patient-reported HRQL and functioning in clinical trials submitted to US Food and Drug Administration.Methods: This study used 4 randomized, breast cancer trials (adjuvant to late-line metastatic) as case examples. Diarrhea, physical functioning (PF), and global health status and quality of life (GHS/QoL) from the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-C30 were analyzed at baseline and approximately 3 and 6 months.Results: Generally, patients reporting very much diarrhea at months 3 and 6 had worse PF (9-19 points lower) and GHS/QoL (16-19 points lower) than patients reporting no diarrhea regardless of treatment arm. In the change from baseline analysis, patients reporting very much diarrhea also experienced a greater decrease in PF (6-13 points) and GHS/QoL (6-16 points) versus patients reporting no diarrhea in both arms.Conclusions: In trials with moderate to large differences in symptomatic toxicity by arm, reporting "no meaningful difference in functioning and HRQL between arms" based on mean change from baseline analysis is insufficient and may obscure important impacts on subgroups experiencing symptomatic adverse events. Additional exploratory analyses with simple data visualizations evaluating functioning or HRQL in patient subgroups experiencing expected symptomatic toxicities can further inform the safety and tolerability of an investigational agent. [ABSTRACT FROM AUTHOR]

The US Food and Drug Administration and the Critical Path Institute's Patient-Reported Outcome (PRO) Consortium convened a cosponsored workshop on the use of PRO measures to inform the assessment of safety and tolerability in cancer clinical trials. A broad array of international stakeholders involved in oncology drug development and PRO measurement science provided perspectives on the role of PRO measures to provide complementary clinical data on the symptomatic side effects of anticancer agents. Speakers and panelists explored the utility of information derived from existing and emerging PRO measures, focusing on the PRO version of the National Cancer Institute's Common Terminology Criteria for Adverse Events. Panelists and speakers discussed potential ways to improve the collection, analysis, and presentation of PRO data describing symptomatic adverse events to support drug development and better inform regulatory and treatment decisions. Workshop participants concluded the day with a discussion of possible approaches to the patient-reported assessment of an investigational drug's overall side effect burden as a potential clinical trial end point. The Food and Drug Administration reiterated its commitment to collaborate with international drug development stakeholders to identify rigorous methods to incorporate the patient perspective into the development of cancer therapeutics. [ABSTRACT FROM AUTHOR]

Purpose: Informal caregivers of individuals with cancer may experience substantial burdens. To develop interventions to support these caregivers, it is crucial to quantify and understand the domains of burdens potentially experienced by caregivers and factors contributing to each domain.Methods: Using data from two national surveys, the National Survey of Caregiving (NSOC) linked to the National Health and Aging Trends Survey (NHATS), we identified all participants in the NHATS diagnosed with cancer who had a caregiver participating in the NSOC. Guided by a theoretical model, twenty-two items in the NSOC related to caregiver health, mood and outlook were included in factor analysis to develop scales capturing domains of burden. Multivariable regression analyses examined whether activities performed by caregivers and supports for caregivers were associated with these burden scales.Results: Analysis of responses from 373 caregivers of cancer patients identified three scales: emotional burden; psychological burden; and relationship with the patient. Providing assistance managing medical care was associated with increased emotional and psychological burden, while assistance with non-medical issues increased psychological burden and worsened relationships with patients. Caregiver provision of direct patient care activities was also associated with increased burden but improved relationships with patients. Use of caregiver supports showed mixed associations with burden.Conclusions: Using a nationally-representative sample of cancer patients and their caregivers and brief publicly-available survey questions, we present three scales addressing different aspects of caregiver burden that are responsive to caregiver activities and social supports. This may assist in developing and evaluating intervention to decrease caregiver burden. [ABSTRACT FROM AUTHOR]