Data-Driven Methods for Identifying and Validating Shorter Symptom Criteria Sets
Subtitle: The Case for DSM-5 Substance Use Disorders
By: Cheryl Raffo
Publisher: [publisher not identified]
Published: 2018
Place of publication: [New York, N.Y.?]
Language: eng
Description: In psychiatry, the Diagnostic and Statistical Manual of Mental Disorders (DSM) is the standard classification system used by clinicians to diagnose disorders. The DSM provides criteria sets that are quantifiable and directly observable measures or symptoms associated with each disorder. For classification, a minimum number of criteria must be observed and once this threshold is met, a disorder is considered to be present. For some disorders, a dimensional classification is also provided by the DSM where severity of disorder increases as the number of criteria observed increases (i.e., None, Mild, Moderate and Severe). While the criteria sets provided by the DSM are the primary assessment mechanisms used by clinicians in psychiatric disease classification, some criteria sets may have too many items making them problematic and/or inefficient in clinical and research settings. In addition, psychiatric disorders are inherently latent constructs without any direct visual or biological observation available which makes validation of psychiatric diagnoses difficult.

The present dissertation proposes and applies two empirical statistical methods to address lengthy criteria sets and validation of diagnoses. The first proposal is a data-driven method packaged as a SAS Macro that systematically identifies subsets of criteria and associated cut-offs (i.e., diagnostic short-forms) that yield diagnoses as similar as possible as using the full criteria set. The motivating example is alcohol use disorder (AUD) which is a type of substance use disorder (SUD) in the DSM-5. A diagnosis of AUD is made when two or more of the 11 possible criteria associated with it are observed. Relying on data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III), the new methodology identifies diagnostic short-forms for AUD by: (1) maximizing the association between the sum scores of all 11 criteria with newly constructed subscales from subsets of criteria, (2) optimizing the similarity of AUD prevalence between the current DSM-5 rule and newly constructed diagnostic short-forms, (3) maximizing sensitivity and specificity of the short-forms against the current DSM-5 rule, and (4) minimizing differences in the accuracy of the short-form across chosen covariates.

The second method introduces external validators of disorder into the process of identifying and validating short-forms. Each step in the first methodology uses some type of comparison (i.e., maximizing correlation, sensitivity, specificity) with the current DSM rule assuming the DSM is the best diagnostic target to use. However, the method does not itself assess the validity of the criteria-based definition but instead relies on the validity of the original diagnosis. For the second methodology, we no longer assume the validity of the current DSM rule and instead introduce the use of external validators (antecedent, concurrent, and predictive) as the target when identifying short-forms. Application of the method is again AUD and the NESARC III is used as the data source. Rather than use the binary yes/no diagnosis, we use the dimensional classification framework provided by the DSM to identify and validate subsets and associated severity cut-offs (i.e., dimensional short-forms) in a systematic way.

Using each external validator separately in the process could prove difficult in determining a consensus across the validators. Instead, our methodology offers a way to combine these external validators into a singular summary measure using factor analysis that derives the external composite validator (ECV). Using NESARC-III and following principles of convergent validity, we identify dimensional short-forms that most relate to the ECV in theoretically justified ways. Specifically, we obtain nested subsets of the original criteria set that (1) maximize the association between ECV and newly constructed subscales from subsets of criteria and (2) obtain associated severity cut-offs that maximally discriminate on ECV based on R-Squared. Substance use disorders in the DSM-5 include alcohol use disorder (AUD), nicotine use disorder (NUD) and drug use disorders (DUDs). Each of these substances is associated with a single underlying SUD construct with the same 11 diagnostic criteria used across each substance and the same diagnostic classifications.

Cannabis and non-medical prescription opioids are two examples of DUDs and both have recently been identified as major public health priorities. Due to their diagnostic similarity to AUD in the DSM-5, these substances were ideal to also test our methodologies. Using data from the NESARC on criteria for cannabis use disorder (CUD) and opioid use disorder (OUD), we forward applied the diagnostic short-forms that accurately replicated AUD and also applied the methods to each substance separately. Overall, the new methodology was able to identify shorter criteria sets for AUD, CUD, and OUD that yielded highly accurate diagnosis compared to the current DSM (i.e., high sensitivity and specificity). Specifically, excluding criteria “Neglected major roles to use” and/or “Activities given up to use” created no marked change in ability to diagnose or measure severity the same way as DSM-5. When applying the method for identifying the most valid dimensional short-forms using external validators, different severity cut-points compared to the current DSM-5 were found and different cut-points were found across AUD, OUD, and CUD.

There were dimensional short-forms with as few as 7 criteria for AUD, CUD and OUD that demonstrated the same or better level of validity as using all 11 criteria. We discuss the implications of these findings and propose recommendations for future DSM revisions. Lastly, we review limitations and future extensions of each of our proposed methodologies.
Notes: Department: Biostatistics.

Thesis advisor: Melanie M. Wall.

Thesis (Dr.P.H.)--Mailman School of Public Health, Columbia University, 2018.