Evaluation of Methods Handling Missing Diary Datafor Statistical Analysis in Dry Eye StudiesHui-Chun T. Hsu, Dale Usner, R...
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Evaluation of Methods Handling Missing Diary Data for Statistical Analysis in Dry Eye Studies

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Evaluation of Methods Handling Missing Diary Data for Statistical Analysis in Dry Eye Studies

  1. 1. Evaluation of Methods Handling Missing Diary Datafor Statistical Analysis in Dry Eye StudiesHui-Chun T. Hsu, Dale Usner, Richard AbelsonStatistics & Data Corporation, Tempe, AZ.thsu@sdcclinical.com5319/D0080Patient symptom diaries are a commonly used method to collect efficacy data in clinicaltrials such as those for dry eye treatments. Typically, patients are asked to report the severityof several ocular symptoms multiple times per day over the course of a study that may lastweeks or even months. Missing data are common, especially when the patients are askedto complete many items or when diaries are collected over many time points. Along withanalyzing the observed data only, the missing observations can be imputed based onother available data to obtain statistically plausible values. There are several possible waysto handle the missing diary data, each of which will result in a different statistical outcome.The goal of imputing missing diary data is to use available data to estimate the statisticaloutcome that would have been achieved with no missing data.Purpose5000 sets of diary data were randomly created from a multivariate normal distribution for twotreatment groups (active and placebo). For each simulation, a complete two weeks of dailydiary data was generated for 50 subjects per treatment group, assuming a 0.6 treatment meandifference on a scale of 0-5 with a standard deviation of 1 and a correlation of 0.85 betweenconsecutive diary days. Responses were provided at the subject level. Two percent of theobservations were randomly set as missing and ten percent of the subjects were randomlyselected as early withdrawals. Several imputation methods were used to handle the missingdata and compared: (1) last observation carried forward (LOCF); (2) baseline observationcarried forward (BOCF); (3) post-baseline worst observation carried forward (WOCF); (4)subject mean; (5) treatment group mean. Table 1 presents an example of implementing thelisted methods.A mixed model accounting for repeated measures within each subject was used for statisticalanalysis. The percentages of times where the results indicated significant treatment differencesbased on the different imputation methods were compared to the complete simulated dataas well as observed data only with Mixed Model Repeated Measures [(6), MMRM]. Further-more, the concordance and discordance of significance between each imputation methodand the complete simulated data were summarized. Discordance includes two parts: falsepositive (i.e., significant using imputation data but not significant using complete simulateddata), and false negative (i.e., not significant using imputation data but significant usingcomplete simulated data).MethodsCompared to the analysis based on the complete simulated data, treatment group meanimputation (5) yielded an artificially higher percentage of significance, whereas BOCF (2)yielded an artificially lower percentage of significance. Both methods (2) and (5) showedrelatively lower concordance rates compared to the other methods. The discordance inBOCF (2) was primarily a function of a higher false negative rate (5.7%) while the treatmentgroup mean imputation (5) was primarily a function of a higher false positive rate (2.3%). Allother methods had similar percentages of significance, concordance and discordance ratesas the analysis based on the complete simulated data.ResultsAll of the above imputation methods, including analyzing the observed data only with MMRM,arevalidformissingdatahandling.Morecompleximputationmethods(e.g.multipleimputation)can also be used and will be included for comparison in future research. More than oneimputation method is recommended to apply to the clinical research for sensitivity analysis.The methods yielding similar percentages of significance as analysis based on the completesimulated data with high concordance rates are recommended.ConclusionFigure 1. Bar Chart of Significance Rate Comparison (Power)Table 1. Implementation of different imputation methods on the data: assuming subject 1 is in activetreatment group (group mean = 2.4) and subject 2 is in placebo group (group mean = 3)Figure 3. Bar Chart of Discordance ComparisonFigure 2. Bar Chart of Concordance Comparison

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