Hello, if you have any need, please feel free to consult us, this is my wechat: wx91due
7PAVMALM – Multilevel and Longitudinal Modelling
Summative coursework assignment
Assignment summary
You have been provided with a labelled dataset containing a subset from an original study. Your task is to undertake a theoretically and empirically meaningful analysis that requires the fitting of a series of multilevel and longitudinal models.
Background to the SOCRATES dataset:
The SOCrATES Trial – a Study of Cognitive Alignment Therapy in Early Schizophrenia.
This randomised controlled trial originally compared a 5-week CBT programme plus routine care to supportive counselling plus routine care to routine care alone (control) in a multi-centre trial randomising 315 people with DSM–IV diagnosed schizophrenia and related disorders in their first or second acute admission (Lewis et al, 2002; https://doi.org/10.1192/bjp.181.43.s91). There were 6 post-randomisation exclusions, so the final dataset contains 309 people.
The primary outcome of the trial is the Positive and Negative Syndrome Scale (PANSS), a continous measure of symptom severity ranging from 30-210, where a higher score indicates more severe symptoms. A secondary outcome is a binary remission indicator, where a score of <70 on the PANSS indicates remission and a score of ≥70 indicates no remission.
For the purposes of this assignment, the two intervention arms have been combined, and are to be compared to the routine care alone arm.
The variables and their labels that are included in the dataset are:
idnumber Patient no#
interven Intervention or control
centre Centre
sex Sex
episode Admission episode yearsofe Years of education
substmis Substance misuse Duration untreated psychosis:
dup weeks
logdup log10dup
ageentr Age at entry to study: years
panss0 Baseline panss total
panss1 Six week panns total
panss3 3 mnth panss total
panss9 9 mnth panss total
panss18 18 mnth panss total
therapis
Therapist identifier
panss0remis PANSS remission at baseline
panss1remis PANSS remission at 6 weeks
panss3remis PANSS remission at 3 months
panss9remis PANSS remission at 9 months
panss18remis PANSS remission at 18 monthsAssignment details
For this assessment, consider the questions presented below and try and answer them giving each response to each question separately. You should discuss what you found and not simply reprint output from Stata. Remember to justify your choice of statistical models and approach to the analysis.
You should include the ASMHI assignment coversheet with your student number. Do not put your name anywhere on the submission to enable anonymous marking.
At the end of the report, include your labelled do file (you can copy and paste it in word) with the commands you have used to carry out the analyses. Alternatively, you can include a clean log file (i.e. no errors, final version).
Your answers may include a combination of text, tables and/or figures. Choose the most appropriate way to present the findings. It should be a maximum 3000 words (excluding tables/ figures/do file).
This assignment represents 70% of the total mark for 7PAVMALM.Questions
1. Summarize the patterns of missing data in the PANSS measure over time.
2. Summarise the binary outcome variable (PANSS remission) for the intervention arm and routine care alone arm over time.
Note: you could use graphs or tables to display these.
3. Use a graphical display to show the longitudinal profiles of the mean PANSS measure separately for each of the combined intervention arm and routine care alone arm.
4. Using an appropriate generalised linear mixed model, estimate the treatment effect of the combined intervention arm to routine care alone on PANSS scores at 18 months. Describe in statistical terms your final model and interpret the findings.
You should consider the following:
• An appropriate longitudinal model, based on scaling of the time variable
• An appropriate random effect structure, based on model comparisons
• Choice of baseline variables to include in the model
• Validity of underlying statistical assumptions
• Graphical displays to summarise the findings from the modelling
5. Using an appropriate generalised linear mixed model, estimate the treatment effect of the combined intervention arm to routine care alone on the PANSS remission outcome at 9 months. Describe in statistical terms your final model and interpret the findings.
You should consider the following:
• An appropriate longitudinal model, based on scaling of the time variable
• The appropriate metric to report the treatment effect
• An appropriate random effect structure, based on model comparisons
• Choice of any baseline variables to include in the model
• Validity of underlying statistical assumptions
• Graphical displays to summarise the findings from the modelling6. Using a Generalised Estimating Equations approach, estimate the treatment effect of the combined intervention arm to routine care alone on the PANSS remission outcome assuming a common treatment effect across all timepoints. You should check the robustness of your results by performing suitable sensitivity analyses, and describe in statistical terms the methods chosen for analyses and any alternative choices that could have been considered.
You should consider the following:
• An appropriate correlation matrix, based on scaling of the time variable
• Choice of any baseline variables to include in the model
• Validity of underlying statistical assumptions
7. Summarise your findings from questions 4, 5, and 6 and compare the results from the different models. You may wish to highlight the difference between marginal and conditional effects, and the missing data mechanism assumptions made by the statistical methods you have used.
8. Describe what other sources of clustering might be present in the dataset. Consider whether these can be most appropriately handled using fixed or random effects, and give an example Stata syntax that could be used to fit these models. Explain the parameters in your model, and suggest why these models might not be suitable for this dataset.
Note: you do not have to show results of the models.
9. Restricting the sample to those participants with monotonic missingness patterns, examine if there is a difference in the pattern of dropout from the study between the combined intervention arm and routine care alone using appropriate graphical and non-parametric tests.
Note: you may need to create a time-to-dropout variable for each individual, treating any dropouts as occurring at the end of the respective time window. If participants did not dropout then they can be considered as right-censored.
10. Use a joint modelling approach to assess if dropout might have influenced theestimates of the treatment effects from question 4. Explain your findings.
11. Include your STATA do or log file. This should follow good programming standards (header/commented throughout). The log file should be error free and the do file should be able to replicate your findings.