Patient preferences for reducing bowel adverse events following prostate radiotherapy.

BACKGROUND: The Extended Prostate Cancer Index Composite (EPIC) instrument is a commonly used patient reported outcome (PRO) tool in prostate cancer clinical trials. Summary scores for EPIC subscales are calculated by averaging patient scores for attributes (e.g., side effects), implying equal weighting of the attributes in the absence of evidence showing otherwise. METHODS: We estimated patient preferences for each of the attributes included in the bowel subscale of the EPIC instrument using best-worst (B-W) scaling among a cohort of men with prostate cancer. Patients were presented with multiple tasks in which they were asked to indicate which attribute they would find most and least bothersome at different levels of severity. Analysis utilized both (simple) B-W counts and scores to estimate patient preferences for each attribute as well as attribute levels. RESULTS: A total of 174 respondents from two institutions participated in the survey. Preference estimates for each of the five attributes included in the EPIC-26 bowel subscale showed wide variation preferences: 'losing control of bowel movements' was found to be the most bothersome attribute, with a B-W score of -0.48, followed by bowel urgency which also had negative B-W score (-0.04). Increased frequency of bowel movements was the least bothersome attribute, with a B-W score of +0.33, followed by bloody stools (+0.12), and pelvic/rectal pain (+0.06). Analysis of preference weights for attribute bother levels showed preference estimates be linear. CONCLUSIONS: We provide novel evidence on patient preferences for side effect reduction following prostate radiotherapy. Within the bowel sub-scale of the EPIC-26 short form, we found that bowel incontinence was perceived to be the most bothersome treatment effect, while increased bowel frequency was least bothersome to patients.

Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review.

BACKGROUND: Latent class analysis (LCA) has been increasingly used to explore preference heterogeneity, but the literature has not been systematically explored and hence best practices are not understood. OBJECTIVE: We sought to document all applications of LCA in the stated-preference literature in health and to inform future studies by identifying current norms in published applications. METHODS: We conducted a systematic review of the MEDLINE, EMBASE, EconLit, Web of Science, and PsycINFO databases. We included stated-preference studies that used LCA to explore preference heterogeneity in healthcare or public health. Two co-authors independently evaluated titles, abstracts, and full-text articles. Abstracted key outcomes included segmentation methods, preference elicitation methods, number of attributes and levels, sample size, model selection criteria, number of classes reported, and hypotheses tests. Study data quality and validity were assessed with the Purpose, Respondents, Explanation, Findings, and Significance (PREFS) quality checklist. RESULTS: We identified 2560 titles, 99 of which met the inclusion criteria for the review. Two-thirds of the studies focused on the preferences of patients and the general population. In total, 80% of the studies used discrete choice experiments. Studies used between three and 20 attributes, most commonly four to six. Sample size in LCAs ranged from 47 to 2068, with one-third between 100 and 300. Over 90% of the studies used latent class logit models for segmentation. Bayesian information criterion (BIC), Akaike information criterion (AIC), and log-likelihood (LL) were commonly used for model selection, and class size and interpretability were also considered in some studies. About 80% of studies reported two to three classes. The number of classes reported was not correlated with any study characteristics or study population characteristics (p > 0.05). Only 30% of the studies reported using statistical tests to detect significant variations in preferences between classes. Less than half of the studies reported that individual characteristics were included in the segmentation models, and 30% reported that post-estimation analyses were conducted to examine class characteristics. While a higher percentage of studies discussed clinical implications of the segmentation results, an increasing number of studies proposed policy recommendations based on segmentation results since 2010. CONCLUSIONS: LCA is increasingly used to study preference heterogeneity in health and support decision-making. However, there is little consensus on best practices as its application in health is relatively new. With an increasing demand to study preference heterogeneity, guidance is needed to improve the quality of applications of segmentation methods in health to support policy development and clinical practice.

Effects of deworming on child and maternal health: a literature review and meta-analysis.

BACKGROUND: Soil-transmitted helminth infections are widespread. Many studies have been published on the topic of deworming. The Lives Saved Tool (LiST) is a software package that uses a deterministic mathematical model to estimate the effect of scaling up interventions on maternal and child health outcomes. This review investigates the scope of available evidence for benefits of deworming treatments in order to inform a decision about possible inclusion of deworming as an intervention in LiST. METHODS: We searched PubMed, the Cochrane Library, and Google Scholar. We included studies that reported pre/post data in children younger than 5 years or pregnant women for outcomes related to mortality and growth. We excluded studies that compared different anthelminthic treatments but did not include a placebo or non-treatment group, and those that did not report post-intervention outcomes. We categorized articles by treated population (children younger than 5 years and pregnant women), experimental versus observational, mass drug administration (MDA) versus treatment, and reported outcome. RESULTS: We identified 58 relevant trials; 27 investigated children younger than 5 years and 11 investigated pregnant women; one reported on both children younger than 5 years and pregnant women. We conducted meta-analyses of relevant outcomes in children younger than 5 years. CONCLUSIONS: Deworming did not show consistent benefits for indicators of mortality, anemia, or growth in children younger than five or women of reproductive age. We do not recommend including the effect of deworming in the LiST model.