Missing Data Sample Clauses

Missing Data. [...***...]

Missing Data. There was very little missing data across all self-report questionnaire measures (less than 1%). Case mean substitution technique was used when data were missing on less than 30% of items (Xxx-Xxxxxxxxxx & El-Xxxxx, 2005). This method ascribes the participant’s mean score based upon items that are present for that participant (Xxxxxxx, 1986). When missing data for a subscale of a measure was less than 30% for a particular participant on a specific subscale, it was replaced with the mean for that particular participants subscale. For Posttraumatic Cognitions Inventory (PTCI) and RAND 36-item Health Survey Questionnaire (RAND-36) subscale scores, the mean of all available items was taken even when missing items exceeded 30%, as per scoring instructions. This occurred only in three instances: for one participant within the social functioning (one out of two items missing) and the general health (four out of five items missing) subscales of the RAND 36; and for another participant on the Self-Blame subscale on the PCTI (one out of two items missing). If missing data exceeded 30% within a subscale where items were summed to obtain subscale scores, then the particular case was excluded for analyses for which the missing data was required. One carer did not complete any items on the Hospital Anxiety and Depression Scale (HADS) or on three of the RAND-36 subscales (energy and fatigue, emotional well-being, and pain); therefore this participant was excluded from specific analyses where these data were required. The participant who did not complete any of the interviews was excluded from specific analyses where interview data was required.

Missing Data. The modified Xxxxxx Scale assigns a worst outcome score, 6, to deceased individuals, obviating the need for separate adjustments to the primary analysis to handle death as an outcome. For the BI, NIHSS, GOS, and SIS, missing 90-day endpoint values will be replaced with the worst case value if the patient died, e.g. BI = 0, INIHSS=42, GOS = 5. If the patient did not die, patients with data from a visit after day 7 but missing data on day 90 will be analyzed employing the last observation carried forward (LOCF). Patients with no data available from any visit after day 7 will have will have worst-case values assigned for the day 90 datapoint, e.g. BI = 0, NIHSS = 42, GOS = 5.

Missing Data. As is characteristic of RW data, the availability and completeness of relevant data may be limited. Missing data for the primary analysis will not be imputed, and the data will be analysed as recorded in the study; the mixed model for repeated measurements offers a simple alternative to handle missing data without requiring imputation. However, a sensitivity analysis may be performed using a method such as multiple imputation using the missing at random assumption to assess the impact of missing data on the primary objective. Missing data methods will be further defined in the SAP.

Missing Data. In the methods and results section: (i) Is the occurrence or absence of missing values discussed in any way? Yes, the authors use missingness of medical records as an exclusion criterion in the Study design & participants subsection of the Methods section. (ii) Are the numbers of missing values stated for each variable that is later on used in the primary analysis including the outcome? Answer yes to this question if no missing values can occur. No, the authors indicate in Fig.1 that 11 patients had incomplete medical records. This is a small number compared to the size of the entire sample, but it does nevertheless not give the crucial information if the missingness was in the outcome or any of the explanatory variables. This rating may seem harsh since 11 patients are only 0.1% of the sample (if considering the 772 eligible patients plus the 11 with missing information but not those referred from other hospitals), but there is no clear cut-point for the fraction of missing values that may safely be neglected without discussion.

Missing Data. Missing data may indicate a malfunction with the recording equipment or a faulty sensor. This form of fault is inevitable during a study of this nature. Data should therefore undergo first-stage processing at the earliest opportunity following download in order that any malfunctioning equipment is identified as soon as possible to allow for repair work to be scheduled. Steps should be taken to record (in the Laboratory Log) what issue was experienced, how much data was lost and what the actions to resolve the technical issue were. Datasets with missing data will, where possible, be processed as normal but with a note of what is missing. The decision on whether to use ‘faulty’ data in the analysis is to be made by the individual partner, with a short explanation being submitted to the WP2.3 leaders. Accurate maintenance of riding logs on the part of the participant will act as a further aid in identifying missing data. Participants will be reminded to keep an accurate diary during each data download meeting. If it is found that missing data is due to rider error, the participant will be advised on how to remedy their mistake; but given the equipment used this is unlikely.

Missing Data. 1. The Database will have no missing data in the following fields: ENTRY FORMS - [ * ]

Missing Data. Missing data was scrutinized for patterns (Xxxx & Xxxx, 1991) and then were treated based on level of missingness, patterns of missingness, and potential associations between the missing values and the outcome and other study variables of interest (Xxxxxxxx et al., 2006). Multiple imputation (Xxxxx, 1987) has been suggested by the FFCW research team as an acceptable method for dealing with missing data in the FFCW dataset (Xxxxxxxx, 2017) and was considered as an option. However, we instead handled missing data using full information maximum likelihood (FIML) because missing data on predictor variables was limited (range 0% -4%) (Xxxxxxx, 2012).

Missing Data. Years 2001 and 2002 were excluded from this analysis due to low country response numbers compared to the remaining nine years included in the dataset. Observations with a negative or zero value for number of people prosecuted were also excluded from the analysis—countries with zeros recorded tended to have missing values for other self-reported variables and seem to have been coded as an alternate to missing, while many were missing across the board (including country and year) with a zero value for number prosecuted. For the nine-year window, there were 45 countries with more than 5 years of missing data for the number of people prosecuted for human trafficking offenses, most of which are from the Tier 2/Tier 2 Watch List category and 21 of which are located in Africa. This suggests that missing data could be informative to the research question and is a limitation to this analysis. Future work should explore methods to most appropriately address the missing data issue.

Missing Data. No imputations of missing data will be performed. However, the following rules will be applied to ensure that all patients can be included in the final analysis: • Patients who are withdrawn from the study prior to Week 8 because of safety concerns or poor efficacy will be classified as non-responders from the time of their withdrawal in all analyses of response status, and their data will be censored at time of withdrawal in all time-to-event analyses. For continuous endpoints in such patients, all analyses for time points beyond the point of withdrawal will exclude missing data for these patients. • Patients who do not reach Week 8 because of early transplant will be classified as responders beyond their time of withdrawal in all analyses of response status, and their data will be censored at time of withdrawal.