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1FINAL programming skills
Creation date：2024-04-11 15:57:59
Belonging category：计算机computer science
Tag 1FINAL
programming skills

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FINAL PROJECT

PAPER REPLICATION
Learning objective
The final project provides you the experience of implementing the methods and programming skills
acquired during this course. After completing the project you should be able to apply SAS and/or Stata
to develop an analytic file, including identifying the data files and variables required, transforming
variables into the measures of interest, and assessing the accuracy of your work; run and present the
results of your analysis; describe the methods used to develop and analyze the data; and assess peer
reviewed papers’ clarity in describing their methods as well as note potential problems, if any, with the
original analysis.
Assignment description
For the final project, you will execute your analytic plan as described in your Midterm and summarize
your methods and results. You may use SAS and/or Stata to develop the data and run the analysis for
the project. The final project submission will include:
- A document with a methods, results, and discussion section (2-4 pages):
o Methods: describe what you did, including how it differed from the paper and/or what
parts of the methods were not clear from the original paper’s methods and how you
handled them. You can refer to the paper for details, rather than spelling them all out
here, but do describe the components and any modifications you made to the
methods.
o Results: these should reflect the paper’s tables, but the numbers should be taken from
the results you produced. They may not be the same as those in the paper—if you’ve
checked your work, this is OK.
o Discussion: describe what the results mean. Evaluate how your results compare to the
paper, including possible reasons why the results are different and how you would
proceed to resolve substantial differences. Or if your results are very similar to those
from the paper’s, what are the limitations of the analysis and how might those be
addressed?
o Please also include the citation for the paper.
- The location of your programs and logs/listings showing the steps you executed from raw data
to final results. Please include the program names and the order in which they were run.
- You should upload the written portion and provide the location and order of your programs,
logs, and listings.
Steps
21. Ensure that you understand the author’s description of the methods.
2. Break down the methods into the measures that will be needed to produce the tables and
figures you will reproduce.
3. Identify the data files and variables you will need. Find the files on the servers.
4. Process the source data to create an analytic file with the measures you identified before. It may
be useful to focus on one measure or group of measures at a time as you pull variables from the
original files and process them into the measures you will analyze.
5. Check your work – run tables to verify that you’ve recoded correctly, look at particular cases if
they don’t make sense, and compare your N’s to the paper. The N’s may not (probably won’t)
match exactly but they should be fairly close. Also check percentages and means that you can
compare to the paper’s results.
6. For survey data sources:
a. Don’t forget to recode missing values to be missing.
b. For binary measures, e.g., yes/no, use 0 for no and 1 for yes.
c. Set a 0/1 flag to indicate who is in your sample (=1) and who is not (=0). Include all
observations, whether in your sample or not and use the weights properly, when
running descriptive statistics or models. See the slide deck on Weighting from Week 11,
and read the background information for the survey if the link is available.
i. In SAS you will include a DOMAIN statement with your sample flag, e.g.,
DOMAIN insamp; as well as the CLUSTER, STRATA, and WEIGHT statements with
the appropriate variables from the survey. If you need to run separate analyses
by other groups, you can use additional variables on the DOMAIN statement,
e.g., if running models by sex, DOMAIN insamp*sex; or DOMAIN insamp
insamp*sex; to run the analysis for the whole sample and by sex.
ii. In Stata you will use svyset to tell Stata what the cluster (or PSU) and strata
variables are, and use the subpop option when you run svy: prefixed descriptive
statistics or models, e.g., svy, subpop(insamp): [stata command]. If you need to
run separate analysis by other groups, you can use the over() option, which
allows you to list multiple variables. All combinations of values of multiple
variables will be run separately. See subpopulation estimation for examples of
subpop and over, alone and in combination.
iii. You’ll only look at the results for those in your sample, e.g., where insamp=1.
7. For OPTUM based papers:
a. If possible, identify your sample first, so when you create your measures you can reduce
the size of your files by only looking at claims, labs, or confinements from people in the
sample.
b. Extract and develop measures from different claims data sources separately ,e.g.,
measures needed from medical claims, from drug claims, from labs, from confinements.
3Then merge results together by patid. You can do this in separate programs to reduce
the run times of each.
c. Don’t forget about enrollment. If a patid is not enrolled for a period of time, their
information is missing, not zero. You may find it useful to run the continuous
enrollment program we ran to prep those data for the CCW conditions package,
particularly if you need to consider enrollment before and after. The pre_ and post_
variables in the yearly files produced provide the number of continuously enrolled
months before and after each month of the year.
d. Take advantage of the CCW conditions package if the paper controls for individual
conditions. Don’t forget to include prior years of data to cover the reference periods.
e. If analyzing costs, you will need to take into account differing reference years for
STD_COST. There is an excel file in the Optum data folder that contains factors to shift
the reference year to 2019. You can also use the factors to change the reference year to
any other year. If you are unsure of how to do this please ask.
8. Plan your work so that you complete a core analysis, then go back and add or tweak variables or
steps that you may have skipped.
9. In the written portion, use your own words to describe what you did. Organize the document to
have sections on methods, results, and discussion. The methods section doesn’t need to include
all the detail laid out in the paper; it can refer to the paper’s methods section, while describing
where you had to modify or infer what the authors did, and how you addressed any issues. The
results section should show the figures and tables that you replicated. In the discussion section,
note differences and similarities to the paper’s results. Look for differences in sign and relative
magnitude of coefficients, odds ratios, percentages, or significance. For example, if you have
confidence intervals, do they overlap with those in the paper?
10. If you are stuck, ask questions, at office hours or through the Final Project Discussion Board.
11. Submit your project through Blackboard.
Grading
Your instructor will use the following rubric to grade this assignment.
Programs 25 points
- 15 for accuracy: Are the paper methods reflected in the
programs, or, if not, is there an explanation for the
change? Do the programs include steps that allow you
to check that your variable derivations worked?
- 5 for efficiency: Are there unnecessary reads and writes
of files, and are variables and observations kept
appropriately (especially for Optum)?
4- 5 for organization: Is the program easy to follow with
comments for clarity when needed, and are variables
and values labelled?
Written summary 15 points (5 pts each methods, results, discussion)
Methods: Are the methods described accurately and in your own
words, that is, is it clear you understand the methods? Is the
citation for the replicated paper included?
Results: Are your results presented so they can easily be
compared to the paper? Are they complete according to your
analytic plan, with an explanation for parts not done?
Discussion: Have you described the “take-aways” of the results?
Do you address the differences between your results and those
in the paper? Are there suggestions for further study or analysis?
Copy and pasted text, largely
verbatim
Subtract up to 5 points from the appropriate written section
Overall 40 points
Case category
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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