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. 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 Xxxxx 2019) is applicable, allowing missingness to depend upon exogeneous covariates and both earlier and later observed depression scores (Xxxxxx and Xxxxxxx 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. As illustrated by data from the PRISE study, missing data is a common complication to completing analysis. The first step to dealing with missing data is to first determine the nature of the missingness in the data. Three main classifications of missing data are missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).6,7 Utilizing our data we consider Yi as our dependent variable that represents average daily steps for the women at time i=1,2,3,4 and for the purposes of illustration use X as an arbitrary corresponding independent variable. For data that is MCAR the probability that observation Xx is missing is unrelated to Xx observed or missing, however, Xx may depend on a particular X.6,7 The PRISE step log data would be MCAR if say a patient had forgotten their pedometer on a particular day and was unable to report. Analysis tends to be unbiased with MCAR data.6,7 With MAR data missingness depends on observed Yi values, but not missing Yi values. For example, participants with lower ADS may be less likely to report. Particular characteristics of participants may be related to lower ADS. With MAR data we may find that missingness does not depend on Xx after controlling for another variable. In the case of MAR data we find that bias is introduced. Continuing with our above example if younger participants had lower ADS and thus were less likely to report data, summary statistics and models will be biased since not containing much data from younger participants. Younger participants might be more in shape and have higher average daily steps (ADS); without these measurements our summary statistics might be underestimated. 6,7 The last classification of data that we will consider will be MNAR. If data is not MCAR or MAR it is MNAR. MNAR data occurs when missingness depends on both observed and missing variables. With MNAR bias is introduced as well.6,7 Given the type of missing data that a problem has there are various methods that Statisticians have proposed to deal with this issue. The methods that we will look at involve manipulating the data to result in a new dataset that is able to be utilized for analysis. One method is listwise deletion, where cases are omitted with missing data points. One drawback to this method is this method will result in a much lower sample size. It will also still result in biased results with MAR and MNAR. With the PRISE study, there is so much missing data that listwise deletion is not a...
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). Table 1: Participation and Data Collection for Each CBT for Carers Group Delivered Group ID and Date Participants Datasets Analysed Missing Data 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 Xxxxx Xxxxxx Interview pre- and post- intervention + 2 participants, one in each group, did not complete the coping questions. Qualitative Outcome Measures 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 ...
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. [...***...] ---------------- * Confidential treatment requested.
Missing Data. 1. The Database will have no missing data in the following fields: ENTRY FORMS - [ * ]
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. 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. Missing observations caused by dropouts or uncompleted responses might cause a problem in studies of longitudinal data. When the analysis is restricted to complete cases and the missing data depend on previous responses, the generalized estimating equation (GEE) approach, which is commonly used when population-averaged effects are of primary interest, can lead to biased parameter estimates. There are also different approaches to address such problem, i.e. Multiple Imputation (Xxxxxx & Xxxxx, 1987) or Multiple Imputation by Chained Imputation (Xxxxx, Xxxxxxx, & Xxxx, 2011). However, if only a relatively small proportion of the data contain missing values the records containing missing data can be deleted using only the complete cases in the analyses. Further, in the statistical literature missing data are usually classified according to the underlying reasons of being missing. If data is missing completely at random (MCAR), the probability of a value being missing is independent of both the observed data and the unobserved data, e.g. by tossing a dice, comparisons are generally not subject to bias. When, in a function Y= f(X) relating an outcome with exposure X, the probability of a particular value y being missing depends only on the observed data (Y or X), then the missing data is considered to be missing at random. If the missing data can be considered missing at random, the estimates obtained for the quantile regression, are unbiased. If this assumption is false, the missing data are not ignorable and the missing mechanisms should be modelled (Xxxxxx & Xxxxx, 1987). In the work presented here using the large cohort of Swedish women the data used are essentially complete. Inverse-probability weighting (IPW) is more sophisticated method for handling missing data, which make the weaker assumption that the data are missing at random. We will apply this method to determine whether this method changes the conclusions of the original analysis.
