Missing Data Clause Samples

Missing Data. Missing data was scrutinized for patterns (▇▇▇▇ & ▇▇▇▇, 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 (▇▇▇▇▇▇▇▇ et al., 2006). Multiple imputation (▇▇▇▇▇, 1987) has been suggested by the FFCW research team as an acceptable method for dealing with missing data in the FFCW dataset (▇▇▇▇▇▇▇▇, 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%) (▇▇▇▇▇▇▇, 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. Importantly, the survivor function for an individual can be calculated for any time within the study period, including those where they have missing data. The growth curve model tackles both sporadic missing observations and the right censoring by assuming that the ignorable missing data property of maximum likelihood estimation (Little and ▇▇▇▇▇ 2019) is applicable, allowing missingness to depend upon exogeneous covariates and both earlier and later observed depression scores (▇▇▇▇▇▇ and ▇▇▇▇▇▇▇ 1994) . Furthermore, predictors of missing data can be included in the growth curve model to give unbiased estimates used in the calculation of survival. However, data that are missing not at random require more complex procedures.

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. 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. 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. Thirteen cases were not included in the analysis due to neither the wellbeing or carer burden data being complete i.e. no pre measures, no post-measures or in 3 cases no outcome measures at all – see Table 1. Reasons for incomplete datasets include dropping out of the programme for personal reasons, a failure to complete and return measures, and in one case a participant declined to complete the post-group outcome measures as she said it made her feel worse to answer the questions. In addition, for 4 cases wellbeing data was available but not carer burden and in 5 cases carer burden was available but no measure of wellbeing was administered (this was the first group delivered in Dec 2005). Group 1: 2005 8 5 N = 3 no post measures Group 2: Summer 2006 4 4* Group 3: Oct 2006 6 4 N = 2 no post measures Group 4: Spring 2007 3 3 - Group 5: Oct 2007 7 6+ N = 1 declined to complete measure Group 6: Spring 2008 5 4 N = 1 no post measures Group 7: Autumn 2008 4 4 Group 8: Autumn 2009 7 6+ N = 1 no post measures Group 9: Spring 2010 1 1 - Group 10: Autumn 2011 5 4* N = 1 did not complete group Group 11: Jan 2012 7 4 N = 2 did not complete group N = 1 no post measures Group 12: Summer 2012 4 3 N = 1 no post measures Group 13 Winter 2012 6 6 - Total: N = 67 N = 54 N = 13 * 2 participants, one in each group, did not complete the ▇▇▇▇▇ ▇▇▇▇▇▇ Interview pre- and post- intervention + 2 participants, one in each group, did not complete the coping questions. Anonymised evaluation questionnaires were also administered and data was available for 42 participants. The majority (n=34) completed a 14 item questionnaire which probed practical issues alongside questions relating to outcomes. Six questions relating to content were coded: i) number of sessions ii) were expectations met iii) what did the participant learn? iv) what did they like most? v) what did they like least? and vi) would they recommend the group? The remaining questions focussed on how the participant heard about the group, information provided before the group, timing of group, number of participants in the group, length of sessions, small or large group exercises and an open question. These data were coded into recurring themes. 8 participants completed one of two versions of a different questionnaire with slightly amended questions and a Likert scale for responding. As these datasets were in the minority, data was extracted in accordance with the 6 questions relating to content detailed above.

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.

Missing Data. The modified ▇▇▇▇▇▇ 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.