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Analyzing clinical trial outcomes based on incomplete daily diary reports.

ABSTRACT: A case study is presented assessing the impact of missing data on the analysis of daily diary data from a study evaluating the effect of a drug for the treatment of insomnia. The primary analysis averaged daily diary values for each patient into a weekly variable. Following the commonly used approach, missing daily values within a week were ignored provided there was a minimum number of diary reports (i.e., at least 4). A longitudinal model was then fit with treatment, time, and patient-specific effects. A treatment effect at a pre-specified landmark time was obtained from the model. Weekly values following dropout were regarded as missing, but intermittent daily missing values were obscured. Graphical summaries and tables are presented to characterize the complex missing data patterns. We use multiple imputation for daily diary data to create completed data sets so that exactly 7 daily diary values contribute to each weekly patient average. Standard analysis methods are then applied for landmark analysis of the completed data sets, and the resulting estimates are combined using the standard multiple imputation approach. The observed data are subject to digit heaping and patterned responses (e.g., identical values for several consecutive days), which makes accurate modeling of the response data difficult. Sensitivity analyses under different modeling assumptions for the data were performed, along with pattern mixture models assessing the sensitivity to the missing at random assumption. The emphasis is on graphical displays and computational methods that can be implemented with general-purpose software. Copyright © 2016 John Wiley & Sons, Ltd.

SUBMITTER: Thomas N

PROVIDER: S-EPMC4931978 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Analyzing clinical trial outcomes based on incomplete daily diary reports.

Thomas Neal N Harel Ofer O Little Roderick J A RJ

Statistics in medicine 20160216 17

A case study is presented assessing the impact of missing data on the analysis of daily diary data from a study evaluating the effect of a drug for the treatment of insomnia. The primary analysis averaged daily diary values for each patient into a weekly variable. Following the commonly used approach, missing daily values within a week were ignored provided there was a minimum number of diary reports (i.e., at least 4). A longitudinal model was then fit with treatment, time, and patient-specific ...[more]

PMID: 26888661

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Project description:BackgroundMedication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medication event reports is ineffective and requires heavy workloads for clinicians. An automated pipeline is proposed to help clinicians deal with the accumulated reports, extract valuable information and generate feedback from the reports. Thus, the strategy of medication event prevention can be further developed based on the lessons learned.MethodsIn order to build the automated pipeline, four classic machine learning classifiers (i.e., support vector machine, Naïve Bayes, random forest, and multi-layer perceptron) were compared to identify the event originating stages, event types, and event causes from the medication event reports. The precision, recall and F-1 measure were calculated to assess the performance of the classifiers. Further, a strategy to measure the similarity of medication event reports in our pipeline was established and evaluated by human subjects through a questionnaire.ResultsWe developed three classifiers to identify the medication event originating stages, event types and causes, respectively. For the event originating stages, a support vector machine classifier obtains the best performance with an F-1 measure of 0.792. For the event types, a support vector machine classifier exhibits the best performance with an F-1 measure of 0.758. And for the event causes, a random forest classifier reaches an F-1 measure of 0.925. The questionnaire results show that the similarity measurement is consistent with the domain experts in the task of identifying similar reports.ConclusionWe developed and evaluated an automated pipeline that could identify three attributes from the medication event reports and calculate the similarity scores between the reports based on the attributes. The pipeline is expected to improve the efficiency of analyzing the medication event reports and to learn from the reports in a timely manner.

Project description:BACKGROUND: Access to unpublished clinical study reports (CSRs) is currently being discussed as a means to allow unbiased evaluation of clinical research. The Institute for Quality and Efficiency in Health Care (IQWiG) routinely requests CSRs from manufacturers for its drug assessments. Our objective was to determine the information gain from CSRs compared to publicly available sources (journal publications and registry reports) for patient-relevant outcomes included in IQWiG health technology assessments (HTAs) of drugs. METHODS AND FINDINGS: We used a sample of 101 trials with full CSRs received for 16 HTAs of drugs completed by IQWiG between 15 January 2006 and 14 February 2011, and analyzed the CSRs and the publicly available sources of these trials. For each document type we assessed the completeness of information on all patient-relevant outcomes included in the HTAs (benefit outcomes, e.g., mortality, symptoms, and health-related quality of life; harm outcomes, e.g., adverse events). We dichotomized the outcomes as "completely reported" or "incompletely reported." For each document type, we calculated the proportion of outcomes with complete information per outcome category and overall. We analyzed 101 trials with CSRs; 86 had at least one publicly available source, 65 at least one journal publication, and 50 a registry report. The trials included 1,080 patient-relevant outcomes. The CSRs provided complete information on a considerably higher proportion of outcomes (86%) than the combined publicly available sources (39%). With the exception of health-related quality of life (57%), CSRs provided complete information on 78% to 100% of the various benefit outcomes (combined publicly available sources: 20% to 53%). CSRs also provided considerably more information on harms. The differences in completeness of information for patient-relevant outcomes between CSRs and journal publications or registry reports (or a combination of both) were statistically significant for all types of outcomes. The main limitation of our study is that our sample is not representative because only CSRs provided voluntarily by pharmaceutical companies upon request could be assessed. In addition, the sample covered only a limited number of therapeutic areas and was restricted to randomized controlled trials investigating drugs. CONCLUSIONS: In contrast to CSRs, publicly available sources provide insufficient information on patient-relevant outcomes of clinical trials. CSRs should therefore be made publicly available. Please see later in the article for the Editors' Summary.

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Analyzing clinical trial outcomes based on incomplete daily diary reports.

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Analyzing clinical trial outcomes based on incomplete daily diary reports.

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