Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1 TR002345. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Disclaimer: Additionally, the views expressed in this article are those of the author only and should not be construed to represent in any way those of the United States Government, the United States Department of Defense (DoD), or the United States Department of the Army. The identification of specific products or scientific instrumentation is considered an integral part of the scientific endeavor and does not constitute endorsement or implied endorsement on the part of the author, DoD, or any component agency.

The following protocol follows the guidelines of our institutional human ethics committees.
1. Defining the cohort
<ol>
	<li>Determine and clearly define the inclusion and exclusion criteria of the planned cohort using either 1) a registry or 2) data points that can be extracted from the MDR such as administrative codes for diagnoses or procedures (i.e., all patients with more than two outpatient diagnoses or one inpatient diagnosis of atrial fibrillation).
	<ol>
		<li>If using a registry, i...

<ol>
	<li>Concato, J., Shah, N., Horwitz, R. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Randomized,+controlled+trials,+observational+studies,+and+the+hierarchy+of+research+designs.">Randomized, controlled trials, observational studies, and the hierarchy of research designs.</a> New England Journal of Medicine. 342 (25), 1887-1892 (2000).</li><li>Austin, P. C., Platt, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survivor+treatment+bias,+treatment+selection+bias,+and+propensity+scores+in+observational+research.">Survivor treatment bias, treatment selection bias, and propensity scores in observational research.</a> Journal of Clinical Epidemiology. 63 (2), 136-138 (2010).</li><li>Sturmer, T., Wyss, R., Glynn, R. J., Brookhart, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Propensity+scores+for+confounder+adjustment+when+assessing+the+effects+of+medical+interventions+using+nonexperimental+study+designs.">Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs.</a> Journal of Internal Medicine. 275 (6), 570-580 (2014).</li><li>Schermerhorn, M. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-Term+Outcomes+of+Abdominal+Aortic+Aneurysm+in+the+Medicare+Population.">Long-Term Outcomes of Abdominal Aortic Aneurysm in the Medicare Population.</a> New England Journal of Medicine. 373 (4), 328-338 (2015).</li><li>Williams, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Propensity-Matched+Analysis+Between+Standard+Versus+Tapered+Oral+Vancomycin+Courses+for+the+Management+of+Recurrent+Clostridium+difficile+Infection.">A Propensity-Matched Analysis Between Standard Versus Tapered Oral Vancomycin Courses for the Management of Recurrent Clostridium difficile Infection.</a> Open Forum Infectious Diseases. 4 (4), (2017).</li><li>Mitchell, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+Statins+on+Cardiovascular+Outcomes+Following+Coronary+Artery+Calcium+Scoring.">Impact of Statins on Cardiovascular Outcomes Following Coronary Artery Calcium Scoring.</a> Journal of the American College of Cardiology. 72 (25), 3233-3242 (2018).</li><li>Rush, T., McGeary, M., Sicignano, N., Buryk, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+plateau+in+new+onset+type+1+diabetes:+Incidence+of+pediatric+diabetes+in+the+United+States+Military+Health+System.">A plateau in new onset type 1 diabetes: Incidence of pediatric diabetes in the United States Military Health System.</a> Pediatric Diabetes. 19 (5), 917-922 (2018).</li><li>Rhon, D. I., Greenlee, T. A., Marchant, B. G., Sissel, C. D., Cook, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comorbidities+in+the+first+2+years+after+arthroscopic+hip+surgery:+substantial+increases+in+mental+health+disorders,+chronic+pain,+substance+abuse+and+cardiometabolic+conditions.">Comorbidities in the first 2 years after arthroscopic hip surgery: substantial increases in mental health disorders, chronic pain, substance abuse and cardiometabolic conditions.</a> British Journal of Sports Medicine. , (2018).</li><li>Mitchell, J., Paisley, R., Moon, P., Novak, E., Villines, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coronary+Artery+Calcium+Score+and+Long-term+Risk+of+Death,+Myocardial+Infarction+and+Stroke:+The+Walter+Reed+Cohort+Study.">Coronary Artery Calcium Score and Long-term Risk of Death, Myocardial Infarction and Stroke: The Walter Reed Cohort Study.</a> Journal of the American College of Cardiology: Cardiovascular Imaging. , (2017).</li><li>McCormick, N., Lacaille, D., Bhole, V., Avina-Zubieta, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+of+myocardial+infarction+diagnoses+in+administrative+databases:+a+systematic+review.">Validity of myocardial infarction diagnoses in administrative databases: a systematic review.</a> PLoS ONE. 9 (3), e92286 (2014).</li><li>Huo, J., Yang, M., Tina Shih, Y. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sensitivity+of+Claims-Based+Algorithms+to+Ascertain+Smoking+Status+More+Than+Doubled+with+Meaningful+Use.">Sensitivity of Claims-Based Algorithms to Ascertain Smoking Status More Than Doubled with Meaningful Use.</a> Value in Health. , (2017).</li><li>Nayan, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+value+of+complementing+administrative+data+with+abstracted+information+on+smoking+and+obesity:+A+study+in+kidney+cancer.">The value of complementing administrative data with abstracted information on smoking and obesity: A study in kidney cancer.</a> Canadian Urological Association Journal. 11 (6), 167-171 (2017).</li><li>Birman-Deych, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+of+ICD-9-CM+codes+for+identifying+cardiovascular+and+stroke+risk+factors.">Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors.</a> Medical Care. 43 (5), 480-485 (2005).</li><li>Preen, D. B., Holman, C. D., Spilsbury, K., Semmens, J. B., Brameld, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Length+of+comorbidity+lookback+period+affected+regression+model+performance+of+administrative+health+data.">Length of comorbidity lookback period affected regression model performance of administrative health data.</a> Journal of Clinical Epidemiology. 59 (9), 940-946 (2006).</li><li>Rector, T. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Specificity+and+sensitivity+of+claims-based+algorithms+for+identifying+members+of+Medicare+Choice+health+plans+that+have+chronic+medical+conditions.">Specificity and sensitivity of claims-based algorithms for identifying members of Medicare+Choice health plans that have chronic medical conditions.</a> Health Services Research. 39 (6 Pt 1), 1839-1857 (2004).</li><li>Hern&#225;n, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Observational+studies+analyzed+like+randomized+experiments:+an+application+to+postmenopausal+hormone+therapy+and+coronary+heart+disease.">Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease.</a> Epidemiology (Cambridge, Mass.). 19 (6), 766-779 (2008).</li><li>Austin, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+relative+ability+of+different+propensity+score+methods+to+balance+measured+covariates+between+treated+and+untreated+subjects+in+observational+studies.">The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.</a> Medical Decision Making. 29 (6), 661-677 (2009).</li><li>Robins, J. M., Hernan, M. A., Brumback, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Marginal+structural+models+and+causal+inference+in+epidemiology.">Marginal structural models and causal inference in epidemiology.</a> Epidemiology. 11 (5), 550-560 (2000).</li><li>Robins, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Marginal+structural+models.">Marginal structural models.</a> 1997 Proceedings of the American Statistical Association, section on Bayesian statistical science. , 1-10 (1998).</li><li>Thoemmes, F., Ong, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Primer+on+Inverse+Probability+of+Treatment+Weighting+and+Marginal+Structural+Models.">A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models.</a> Emerging Adulthood. 4 (1), 40-59 (2016).</li><li>Xu, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+stabilized+inverse+propensity+scores+as+weights+to+directly+estimate+relative+risk+and+its+confidence+intervals.">Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals.</a> Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research. 13 (2), 273-277 (2010).</li><li>Cowper, D. C., Kubal, J. D., Maynard, C., Hynes, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+primer+and+comparative+review+of+major+US+mortality+databases.">A primer and comparative review of major US mortality databases.</a> Annals of Epidemiology. 12 (7), 462-468 (2002).</li><li>Skopp, N. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+a+methodology+to+validate+National+Death+Index+retrieval+results+among+a+cohort+of+U.S.+service+members.">Evaluation of a methodology to validate National Death Index retrieval results among a cohort of U.S. service members.</a> Annals of epidemiology. 27 (6), 397-400 (2017).</li><li>Buck, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> 2015 ICD-9-CM for Hospitals, Volumes 1, 2, &#38; 3, Professional Edition. , (2015).</li><li>Buck, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> 2018 ICD-10-CM for Hospitals, Professional Edition. , (2018).</li><li>Guo, S., Fraser, W. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Propensity Score Analysis: Statistical Methods and Applications, Second Edition. , (2015).</li><li>Elixhauser, A., Steiner, C., Harris, D. R., Coffey, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comorbidity+measures+for+use+with+administrative+data.">Comorbidity measures for use with administrative data.</a> Medical Care. 36 (1), 8-27 (1998).</li><li>Charlson, M. E., Pompei, P., Ales, K. L., MacKenzie, C. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+method+of+classifying+prognostic+comorbidity+in+longitudinal+studies:+development+and+validation.">A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.</a> Journal of Chronic Diseases. 40 (5), 373-383 (1987).</li><li>Deyo, R. A., Cherkin, D. C., Ciol, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adapting+a+clinical+comorbidity+index+for+use+with+ICD-9-CM+administrative+databases.">Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.</a> Journal of Clinical Epidemiology. 45 (6), 613-619 (1992).</li><li>Austin, P. C., Stuart, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+performance+of+inverse+probability+of+treatment+weighting+and+full+matching+on+the+propensity+score+in+the+presence+of+model+misspecification+when+estimating+the+effect+of+treatment+on+survival+outcomes.">The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes.</a> Statistical Methods in Medical Research. 26 (4), 1654-1670 (2017).</li><li>Austin, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Balance+diagnostics+for+comparing+the+distribution+of+baseline+covariates+between+treatment+groups+in+propensity-score+matched+samples.">Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.</a> Statistics in Medicine. 28 (25), 3083-3107 (2009).</li></ol>

The authors have nothing to disclose.

Retrospective analyses using large administrative datasets provide an efficient and cost-effective alternative when randomized controlled trials are not feasible. The appropriate data set will depend on the population and variables of interest, but the MDR is an attractive option that does not have the age restrictions seen with Medicare data. With any data set, it is important to be intimately familiar with its layout and data dictionary. Care should be taken along the way to ensure that complete data are captured, and ...

Randomized, placebo-controlled trials are the strongest study design to quantify efficacy of treatment, but they are not always feasible due to cost and time requirements or a lack of equipoise between treatment groups1. In these instances, a retrospective cohort design using large-scale administrative data (&#34;big data&#34;) often provides an efficient and cost-effective alternative, though the lack of randomization introduces treatment selection bias2. Treatment selection bias occurs in non-randomized studies when the treatment decision is dependent on pre-treatment characteristics that are associated with the outcome of interest. These characteristics are known as confounding factors.
Because propensity scores minimize this bias by balancing the known confounders between treatment groups, they have become increasingly popular3. Propensity scores have been used to compare surgical approaches4 and medical regimens5. Recently, we have used a propensity analysis of data from the United States Military Health System Data Repository (MDR) to assess the effect of statins in primary prevention of cardiovascular outcomes based on the presence and severity of coronary artery calcium6.
The MDR, utilized less frequently than the Medicare and VA data sets for research purposes, contains comprehensive administrative and medical claims information from inpatient and outpatient services provided for active duty military, retirees, and other Department of Defense (DoD) healthcare beneficiaries and their dependents. The database includes services provided worldwide at US military treatment facilities or at civilian facilities billed to the DoD. The database includes complete pharmacy data since October 1, 2001. Laboratory data is available from 2009 but is only limited to military treatment facilities. Within the MDR, cohorts have been defined with methods including use of diagnoses codes (e.g., diabetes mellitus7) or procedure codes (e.g., arthroscopic surgery8). Alternatively, an externally defined cohort of eligible beneficiaries, such as a registry, can be matched to the MDR to obtain baseline and follow-up data9. Unlike Medicare, the MDR includes patients of all ages. It is also less biased towards males than the VA database since it includes dependents. Access to the MDR is limited, however. Generally, only investigators that are members of the Military Health System can request access, analogous to requirements for use of the VA database. Non-government researchers seeking access to Military Health Systems data must do so through a data sharing agreement under the supervision of a government sponsor.
When using any administrative data set, it is important to bear in mind the limitations as well as strengths of administrative coding. The sensitivity and specificity of the code can vary based on the related diagnosis, whether it is a primary or secondary diagnosis, or whether it is an inpatient or outpatient file. Inpatient codes for acute myocardial infarction are generally accurately reported with positive predictive values over 90%10, but tobacco use is often undercoded11. Such undercoding may or may not have a meaningful effect on a study&#39;s results12. Additionally, several codes for a given condition may exist with varying levels of correlation to the disease in question13. An investigative team should perform a comprehensive literature search and review of the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) and/or ICD-10-CM coding manuals to ensure that the appropriate codes are included in the study.
Several methods can be employed to improve the sensitivity and accuracy of the diagnostic codes to define comorbid conditions. An appropriate &#34;look-back&#34; period should be included to establish baseline comorbidities. The look-back period includes the inpatient and outpatient services provided prior to study entry. A period of one year may be optimal14. Additionally, requiring two separate claims instead of a single claim can increase specificity, while supplementing coding data with pharmaceutical data can improve sensitivity15. Select manual chart audits on a portion of the data can be used to verify accuracy of the coding strategy.
Once comorbidities have been defined and assessed for the cohort in question, a propensity score may be used to balance differences in covariates between treatment groups. The propensity score is derived from the probability that a patient is assigned to a treatment based on known covariates. Accounting for this propensity treatment reduces the effect that the covariates have on treatment assignment and helps generate a truer estimate of the treatment effect on the outcome. While propensity scores do not necessarily provide superior results to multivariate models, they do allow for assessment of whether the treated and untreated groups are comparable after applying the propensity score3. Study investigators can analyze the absolute standardized differences in covariates before and after propensity matching or inverse probability of treatment weighting (IPTW) to ensure known confounders have been balanced between groups. Importantly, unknown confounders may not be balanced, and one should be aware of the potential for residual confounding.
When executed properly, though, propensity scores are a powerful tool that can predict and replicate results of randomized controlled trials16. Of the available propensity-score techniques, matching and IPTW are generally preferred17. Within IPTW, patients are weighted by their propensity or probability for treatment. Stabilizing weights are generally recommended over raw weights, while trimming of the weights can also be considered18,19,20,21.
Once study groups are balanced, they may be followed until the outcome of interest. Studies utilizing administrative data may be interested in outcomes such as readmission rates and time-to-event analyses. In studies interested in mortality, the Military Health System Data Repository includes a field for vital status that can be further augmented using the national death index (NDI)22,23. The NDI is a centralized database of death record information from state offices that is managed by the Center for Disease Control. Investigators can request basic vital status and/or specific cause of death based on the death certificate.
The following protocol details the process of conducting an administrative database study using the MDR augmented with mortality information from the NDI. It details the use of IPTW to balance baseline differences between two treatment groups including SAS code and example output.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CD Burner (for NDI Request)</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Computer</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Putty.exe</td>
			<td>Putty.org</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SAS 9.4</td>
			<td>SAS Institute Cary, NC</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>WinSCP or other FTP software</td>
			<td><a href="https://winscp.net/eng/index.php" target="_blank">https://winscp.net/eng/index.php</a></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

inverse probability of treatment weighting (propensity score) using the military health system data repository and national death index

When randomized controlled trials are not feasible, retrospective studies using big data provide an efficient and cost-effective alternative, though they are at risk for treatment selection bias. Treatment selection bias occurs in a non-randomized study when treatment selection is based on pre-treatment characteristics that are also associated with the outcome. These pre-treatment characteristics, or confounders, can influence evaluation of a treatment&#39;s effect on the outcome. Propensity scores minimize this bias by balancing the known confounders between treatment groups. There are a few approaches to performing propensity score analyses, including stratifying by the propensity score, propensity matching, and inverse probability of treatment weighting (IPTW). Described here is the use of IPTW to balance baseline comorbidities in a cohort of patients within the US Military Health System Data Repository (MDR). The MDR is a relatively optimal data source, as it provides a contained cohort in which nearly complete information on inpatient and outpatient services is available for eligible beneficiaries. Outlined below is the use of the MDR supplemented with information from the national death index to provide robust mortality data. Also provided are suggestions for using administrative data. Finally, the protocol shares an SAS code for using IPTW to balance known confounders and plot the cumulative incidence function for the outcome of interest.

Upon completion of IPTW, tables or plots of the absolute standardized differences can be generated using the stddiff macro code or the asdplot macro code, respectively. Figure 1 shows an example of appropriate balancing in a large cohort of 10,000 participants using the asdplot macro. After application of the propensity score, the absolute standardized differences were reduced significantly. The cutoff used for the absolute standardized difference is somewhat...

Watch this Scientific Journal Video about Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index at JoVE.com

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

When randomized controlled trials are not feasible, a comprehensive health care data source like the Military Health System Data Repository provides an attractive alternative for retrospective analyses. Incorporating mortality data from the national death index and balancing differences between groups using propensity weighting helps reduce biases inherent in retrospective designs.

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

When randomized controlled trials are not feasible, retrospective studies using big data provide an efficient and cost-effective alternative, though they ...

In the past, we've used the military healthcare database to help determine which patients would most benefit from statin treatment to help reduce their chance of a future heart attack. Our protocol offers a roadmap to any medical provider who is interested in using big data to answer their own clinical questions and can be applied to a far ranging and nearly limitless number of topics. After attaining approval from the institutional review board, complete a data sharing agreement application, taking care to specify the data fields and files being requested on the DRT MDR extractions worksheet.Specify whether the team is requesting a data analyst, supply of the raw data, or if the team will access the MDR directly and whether the request is for a one-time data pull or if regular pulls are requested daily, monthly, or yearly. To access the MDR directly, complete the MDR authorization request in the MDR-CS-2875 forms. When accessing the MDR directly, follow the instructions for accessing and using the MDR, including the software requirements and example SAS programs that are available in the MDR users guide and the MDR functional guide.To obtain additional MDR beneficiary data for your cohort, acquire the VM6 files from the server and merge with your cohort file. Use different variable names in the VM6 data draw and cohort files for patient names and birth dates to help with subsequent checking for errors following the merge. As database entries are never completely free of error, use the code to preform error checks after each major step, in addition to checking the program log and output for any potential concerns.When comparing names from the cohort file with the VM6 file, only match the first three letters to improve efficiency and reduce any false errors that may arise with differences in spelling or spacing between files. Then review the error data file, performing a manual review of the health record as needed, to check for other errors of concern. For relevant data extraction, obtain the race and sex data from the VM6 beneficiary files, merge this date with the cohort file, and check for errors as just demonstrated.Obtain death data from the death master file, merge the data with the cohort file and check for errors. Then obtain additional data files as needed for the analysis. For data merging and summative file construction, extract baseline comorbidities using ICD-9-CM or ICD-10-CM codes from the period before the index date.Taking care that the patients had eligibility for the military healthcare system during the baseline period as verified in the VM6 beneficiary file. Search the baseline diagnosis codes in the outpatient and/or inpatient files to establish the baseline comorbidities during the baseline 12-month period prior to the index date. If using Elixhauser comorbidities, use the available software from the healthcare cost and utilization project database, making sure to modify the names of the diagnosis variables and files as needed.To set a study end date for all of the patients as a cut off for followup for patients who have no demonstrated the outcome of interest, search the VM6 beneficiary file to ensure eligibility for healthcare through the study end date. If it is important to limit the study to active users of the healthcare system, independent of eligibility, then determine the last healthcare contact within the data files and censor the patients at that date. When all of the necessary information has been acquired, generate a random patient identifier for each patient using Ranuni.To model the probability of treatment use logistic progression to calculate the weights from the predicted probability. Then stabilize the propensity score by dividing the score by the mean weight. To verify the balancing after applying the inverse probability of treatment weighting the standard differentiation macro simplifies the computing standardized mean differences for co-variances before and after weighting in SAS.To generate a cumulative incidence function plot in PROC PHREG, reference a covariate file to specify covariate values to be used when generating the plot in the PROC PHREG syntax, use the weight statement to specify the standardized propensity score variable and use the baseline statement to specify the values for baseline covariates in order to plot the cumulative incidence function. Then specify the strata to use for the plot using ROWID. Here, an example of appropriate balancing in a large cohort of 10000 participants using the absolute standardized difference plot macro as shown.After application of the propensity score, the absolute standardized differences were reduced significantly. Here, the unsuccessful results of attempting to balance covariates in a cohort of 100 participants can be observed. In this example, PROC PHREG with standardized propensity score weights were used to generate a cumulative incidence function plot, revealing that in this analysis, the untreated control group demonstrated a larger number of events and exhibited a comparatively worse survival than did the treated group.The MDR includes beneficiaries of the department of defense, including retirees and family members. So it's not restricted to just patients over 65, and it is not restricted to just veterans. This protocol provides a starting point for using the MDR.And the statistical technique overview can help eliminating biases that can be intrinsic in this type of research.

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JoVE Journal

Medicine

14.1K visualizações.  Washington University School of Medicine. No passado, usamos o banco de dados de saúde militar para ajudar a determinar quais pacientes se beneficiariam mais do tratamento de estatina para ajudar a reduzir a chance de um futuro ataque cardíaco. Nosso protocolo oferece um roteiro para qualquer provedor médico interessado em usar big data para responder suas próprias perguntas clínicas e pode ser aplicado a um número muito variado e quase ilimitado de tópicos. Após obter a aprovação do conselho de revisão institucional, comp...