Screening for cognitive symptoms among cancer patients during chemotherapy: Sensitivity and specificity of a single item self-report cognitive change score.

OBJECTIVES: Cognitive symptoms are commonly reported among cancer patients and survivors, yet guidance on when self-reported cognitive symptoms warrant follow-up is lacking. We sought to establish cut-off scores for identifying patients with perceived low cognitive functioning on widely used self-report measures of cognition and a novel single item Cognitive Change Score. METHODS: Adult patients diagnosed with invasive cancer who had completed at least one cycle of chemotherapy completed a questionnaire containing the EORTC-Cognitive Function (CF) subscale, Functional Assessment of Cancer Therapy-Cognitive Function (FACT-COG) Perceived Cognitive Impairment (PCI) and our Cognitive Change Score (CCS). We used receiver operating characteristic analyses to establish the discriminative ability of these measures against the Patient's Assessment of Own Functioning Inventory (PAOFI) as our reference standard. We chose cut-off scores on each measure that maximised both sensitivity and specificity for identifying patients with self-reported low CF. RESULTS: We recruited 294 participants (55.8% women, mean age 56.6 years) with mixed cancer diagnoses (25.5 months since diagnosis). On the CCS, 77.6% reported some cognitive change since starting chemotherapy. On the PAOFI 36% had low CF. The following cut-off scores identified cases of low CF: ≥28.5 on the CCS (75.5% sensitivity, 67.6% specificity); ≤75.0 on the European Organisation for Research and Treatment of Cancer, QLQ-C30 Cognitive Functioning scale (90.9% sensitivity, 57.1% specificity); ≤55.1 on the FACT-COG PCI-18 (84.8% sensitivity, 76.2% specificity), and ≤59.5 on the FACT-COG PCI-20 (78.8% sensitivity, 84.1% specificity). CONCLUSIONS: We found a single item question asking about cognitive change has acceptable discrimination between patients with self-reported normal and low CF when compared to other more comprehensive self-report measures of cognitive symptoms. Further validation work is required.

Feasibility and acceptability of a beverage intervention for Hispanic adults: results from a pilot randomized controlled trial.

OBJECTIVE: To assess the feasibility and acceptability of a beverage intervention in Hispanic adults. DESIGN: Eligible individuals identified as Hispanic, were 18-64 years old and had BMI 30·0-50·0 kg/m2. Participants were randomized 2:2:1 to one of three beverages: Mediterranean lemonade (ML), green tea (GT) or flavoured water control (FW). After a 2-week washout period, participants were asked to consume 32 oz (946 ml) of study beverage daily for 6 weeks and avoid other sources of tea, citrus, juice and sweetened beverages; water was permissible. Fasting blood samples were collected at baseline and 8 weeks to assess primary and secondary efficacy outcomes. SETTING: Tucson, AZ, USA.ParticipantsFifty-two participants were recruited over 6 months; fifty were randomized (twenty-one ML, nineteen GT, ten FW). Study population mean (sd) age 44·6 (sd 10·2) years, BMI 35·9 (4·6) kg/m2; 78 % female. RESULTS: Forty-four (88 %) completed the 8-week assessment. Self-reported adherence was high. No significant change (95 % CI) in total cholesterol (mg/dl) from baseline was shown -1·7 (-14·2, 10·9), -3·9 (-17·2, 9·4) and -13·2 (-30·2, 3·8) for ML, GT and FW, respectively. Mean change in HDL-cholesterol (mg/dl) -2·3 (-5·3, 0·7; ML), -1·0 (-4·2, 2·2; GT), -3·9 (-8·0, 0·2; FW) and LDL-cholesterol (mg/dl) 0·2 (-11·3, 11·8; ML), 0·5 (-11·4, 12·4; GT), -9·8 (-25·0, 5·4; FW) were also non-significant. Fasting glucose (mg/dl) increased significantly by 5·2 (2·6, 7·9; ML) and 3·3 (0·58, 6·4; GT). No significant change in HbA1c was demonstrated. Due to the small sample size, potential confounders and effect modifiers were not investigated. CONCLUSIONS: Recruitment and retention figures indicate that a larger-scale trial is feasible; however, favourable changes in cardiometabolic biomarkers were not demonstrated.

Missing data handling in non-inferiority and equivalence trials: A systematic review.

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.

Examining Miliary Disease Etiology in a Coccidioides-Endemic Center: A Retrospective Cohort Study.

Background: A miliary pattern on chest imaging is often attributed to tuberculosis (TB) infection. However, a myriad of conditions can cause a miliary pattern, many of which are imminently life-threatening. Research Question: The primary aim of our study is to elucidate the potential causes of miliary chest imaging patterns to improve workup and empiric therapy selection. The secondary aims are to discern the predictors of miliary disease etiology and to determine whether appropriate empiric antimicrobial therapies were given. Study Design and Methods: In this retrospective cohort study, we searched a radiology database for patients with chest imaging studies described by the word "miliary". Subjects were excluded if they were under 18 years of age and if there were insufficient objective data to support a miliary disease etiology. A radiologist independently reviewed all imaging studies, and studies that did not appear to have a true miliary pattern were excluded. The collected data include patient demographics, immunocompromising risk factors, conditions associated with miliary disease, ß-D-glucan levels, serum eosinophil count, and empiric therapies received. Results: From our 41-patient cohort, 22 patients (53.7%) were clinically diagnosed with coccidioidomycosis, 8 (19.5%) with TB, 7 (17.1%) with metastatic solid cancer, 1 (2.4%) with lymphoma, 1 (2.4%) with other (Mycobacterium simiae), and 3 (7.3%) with unknown diseases (the sum equals 42 patients because one individual was diagnosed with both coccidioidomycosis and TB). All six patients with greater than 500 eosinophils/µL were diagnosed with coccidioidomycosis. Of the 22 patients diagnosed with coccidioidomycosis, 20 (90.91%) were empirically treated with an antifungal regimen. Of the eight patients with TB, six were empirically treated for TB. Interpretation: Based on our data from a Coccidioides-endemic region with close proximity to tuberculosis-endemic areas, the leading cause of miliary disease is coccidioidomycosis, although TB and cancer are also common etiologies. Serum eosinophilia and elevated ß-D-glucan levels were strongly predictive of coccidioidomycosis in our patient cohort with a miliary chest imaging pattern.

mHealth-Supported Gender- and Culturally Sensitive Weight Loss Intervention for Hispanic Men With Overweight and Obesity: Single-Arm Pilot Study.

BACKGROUND: Hispanic men have disproportionate rates of overweight and obesity compared with other racial and ethnic subpopulations. However, few weight loss interventions have been developed specifically for this high-risk group. Furthermore, the use of mobile health (mHealth) technologies to support lifestyle behavior changes in weight loss interventions for Hispanic men is largely untested. OBJECTIVE: This single-arm pilot study examined the feasibility and acceptability of integrating mHealth technology into a 12-week gender- and culturally sensitive weight loss intervention (GCSWLI) for Hispanic men with overweight and obesity. METHODS: A total of 18 Hispanic men (mean age 38, SD 10.9 years; mean BMI 34.3, SD 5.5 kg/m²; 10/18, 56% Spanish monolingual) received a GCSWLI, including weekly in-person individual sessions, a daily calorie goal, and prescription of ≥225 minutes of moderate-intensity physical activity per week. mHealth technology support included tailored SMS text messaging, behavior self-monitoring support using Fitbit Charge 2, and weight tracking using a Fitbit Aria Wi-Fi Smart Scale. Changes in weight from baseline to 12 weeks were estimated using a paired 2-tailed t test. Descriptive analyses characterized the use of Fitbit and smart scales. Semistructured interviews were conducted immediately after intervention to assess the participants' weight loss experiences and perspectives on mHealth technologies. RESULTS: Of 18 participants, 16 (89%) completed the 12-week assessments; the overall attrition rate was 11.1%. The mean weight loss at week 12 was -4.7 kg (95% CI 7.1 to -2.4 kg; P<.001). Participants wore the Fitbit 71.58% (962/1344) of the intervention days and logged their body weight using the smart scale (410/1344, 30.51% of the intervention days). Participants identified barriers to the use of the technology, such as lack of technological literacy and unreliable internet access for the smart scale. CONCLUSIONS: Although clinically significant weight loss was achieved by integrating mHealth technology into the GCSWLI, adherence to the prescribed use of technology was modest. Addressing barriers to the use of such technologies identified in our work may help to refine an mHealth intervention approach for Hispanic men. TRIAL REGISTRATION: ClinicalTrials.gov NCT02783521; https://clinicaltrials.gov/ct2/show/NCT02783521.

The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data.

BACKGROUND: Cluster randomized trials (CRTs) are a design used to test interventions where individual randomization is not appropriate. The mixed model for repeated measures (MMRM) is a popular choice for individually randomized trials with longitudinal continuous outcomes. This model's appeal is due to avoidance of model misspecification and its unbiasedness for data missing completely at random or at random. METHODS: We extended the MMRM to cluster randomized trials by adding a random intercept for the cluster and undertook a simulation experiment to investigate statistical properties when data are missing at random. We simulated cluster randomized trial data where the outcome was continuous and measured at baseline and three post-intervention time points. We varied the number of clusters, the cluster size, the intra-cluster correlation, missingness and the data-generation models. We demonstrate the MMRM-CRT with an example of a cluster randomized trial on cardiovascular disease prevention among diabetics. RESULTS: When simulating a treatment effect at the final time point we found that estimates were unbiased when data were complete and when data were missing at random. Variance components were also largely unbiased. When simulating under the null, we found that type I error was largely nominal, although for a few specific cases it was as high as 0.081. CONCLUSIONS: Although there have been assertions that this model is inappropriate when there are more than two repeated measures on subjects, we found evidence to the contrary. We conclude that the MMRM for CRTs is a good analytic choice for cluster randomized trials with a continuous outcome measured longitudinally. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT02804698.

Analytical approaches and estimands to take account of missing patient-reported data in longitudinal studies.

Patient-reported outcomes, such as quality of life, functioning, and symptoms, are used widely in therapeutic and behavioral trials and are increasingly used in drug development to represent the patient voice. Missing patient reported data is common and can undermine the validity of results reporting by reducing power, biasing estimates, and ultimately reducing confidence in the results. In this paper, we review statistically principled approaches for handling missing patient-reported outcome data and introduce the idea of estimands in the context of behavioral trials. Specifically, we outline a plan that considers missing data at each stage of research: design, data collection, analysis, and reporting. The design stage includes processes to prevent missing data, define the estimand, and specify primary and sensitivity analyses. The analytic strategy considering missing data depends on the estimand. Reviewed approaches include maximum likelihood-based models, multiple imputation, generalized estimating equations, and responder analysis. We outline sensitivity analyses to assess the robustness of the primary analysis results when data are missing. We also describe ad-hoc methods, including approaches to avoid. Last, we demonstrate methods using data from a behavioral intervention, where the primary outcome was self-reported cognition.