Part 1: The researchers that conduct the first study are interested in changes of functioning over time. As they are doing their analyses using R they are asking for a dataset that will be easy for them to work with:
1.1 Create a .csv datafile that contains for every participant the baseline (time 1) scores and the change scores for time 1 to time3 for all the RFS susbscales (RFS1A, RFS1B, RFS2A, RFS2B). If there is missing data use LOCF (last observation carried forward) for time 3 and generate an estimated change score based on that. Include a variable that flags if the change score is based on observed time 1 and time 3 data, or if the change score was calculated using LOCF.
1.2 Create a report about the basic structure of the data that includes: The number of unique participants, the number and % of participants that have complete data and the number and % of participants for each of the missing data patterns in the study. Additionally include the number and % of participants whose change scores were computed based on observed data vs. change scores calculated based on LOCF. To provide some initial information to decide if there are any problems with the use of LOCF please provide a table with summary statistics (mean, sd, min, max) for each of the RFS subscales at baseline and their change scores for the complete sample and separated between the complete data cases and the LOCF cases.
Part 2: At the same institute there is an ongoing research project that among other variables measures inflammation markers/cytokines. Most participants in the functioning study are also part of this inflammation study and the two research groups have decided to collaborate on a joint paper linking inflammation status and functioning. The dataset provided by the program coordinator contains data for all participants in the inflammation study whose blood draw was scheduled during the study period of the functioning study.
2.1. Create an analysis dataset including the cytokine measures and the role functioning scores for the test occasion that is closest to the timepoint of the cytokine measurement (I.e., if the cytokine measurement was closest to time 2 then the dataset would include for this person the time 2 RFS scores and their cytokine measures, while if the cytokine measurement was closer to time 1 it would include the time 1 RFS scores.).
2.2. To be able to do leverage analyses to determine if there are systematic differences between the participants in this combined study compared to participants that were not matched between the two
source studies create a dataset that includes for every participant the baseline RFS scores, the cytokine data, what timepoint the cytokine data was matched to, and a variable that identifies if this participant has RFS data, inflammation data, or both available.
2.3. Create a word document with a report that includes a summary of the number of participants in each of the source studies, and the number and %s of them that have both types of data available. Also include a paragraph of information about the matching process that includes the number and % of participants for each of the timepoints that the inflammation was matched to, and histograms and summary statistics for three variables: a) the time in days from the start of the functioning study to the inflammation test timepoint, b) the difference in days between the inflammation test timepoint and the functioning test timepoint, c) the absolute value of the difference in days between the inflammation test timepoint and the functioning test timepoint.
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