title: Application of latent growth and growth mixture modeling to identify and characterize differential responders to treatment for COPD.
abstract: OBJECTIVE
  To explore the utility of applying growth mixture models (GMMs) in secondary analyses of clinical trials to identify sources of variability in data reported by patients with COPD.
  
  
  METHODS
  Analyses were performed on data from two 6-month clinical trials comparing indacaterol and open-label tiotropium or blinded salmeterol and the first six months of a 12-month trial comparing indacaterol and blinded formoterol. Latent growth model (LGM) analyses were conducted to explore the response of the SGRQ Symptoms score from baseline to six months and GMM analyses were evaluated as a method to identify latent classes of differential responders.
  
  
  RESULTS
  Variability in SGRQ Symptom scores was found suggesting subsets of patients with differential response to treatment. GMM analyses found subsets of non-responders in all trials. When the responders were analyzed separately from non-responders, there were increased treatment effects (e.g., symptoms score improvement over six months for whole groups: indacaterol=8-12 units, tiotropium=7 units, salmeterol=9 units, formoterol=11 units. Responder subgroup improvement: indacaterol=9-21 units, tiotropium=7 units, salmeterol=10 units, formoterol=20 units). Responders had significantly different baseline SGRQ Symptom scores, smoking history, age, and mMRC dyspnea scores than non-responders.
  
  
  CONCLUSIONS
  Patients with COPD represent a heterogeneous population in terms of their reporting of symptoms and response to treatment. GMM analyses are able to identify sub-groups of responders and non-responders. Application of this methodology could be of value on other endpoints in COPD and in other disease areas.
authors.affiliation: RTI Health Solutions, The Pavilion, Towers Business Park, Wilmslow Road, Didsbury, Manchester, M20 2LS, UK. dstull@rti.org
authors.forename: Donald E
authors.initials: DE
authors.lastname: Stull
authors.forename: Ingela
authors.initials: I
authors.lastname: Wiklund
authors.forename: Rupert
authors.initials: R
authors.lastname: Gale
authors.forename: Gorana
authors.initials: G
authors.lastname: Capkun-Niggli
authors.forename: Katherine
authors.initials: K
authors.lastname: Houghton
authors.forename: Paul
authors.initials: P
authors.lastname: Jones
mesh_terms: D000280:Administration, Inhalation; D000420:Albuterol / Q000008:administration & dosage / Q000031:analogs & derivatives*; D001993:Bronchodilator Agents / Q000008:administration & dosage; D003692:Delayed-Action Preparations; D004305:Dose-Response Relationship, Drug; D004311:Double-Blind Method; D004334:Drug Administration Schedule; D004983:Ethanolamines / Q000008:administration & dosage*; D005260:Female; D005500:Follow-Up Studies; D005541:Forced Expiratory Volume / Q000187:drug effects; D000068759:Formoterol Fumarate; D006801:Humans; D007189:Indans / Q000008:administration & dosage*; D008297:Male; D008875:Middle Aged; D029424:Pulmonary Disease, Chronic Obstructive / Q000188:drug therapy* / Q000503:physiopathology; D015363:Quinolones / Q000008:administration & dosage*; D000068299:Salmeterol Xinafoate; D012602:Scopolamine Derivatives / Q000008:administration & dosage*; D011795:Surveys and Questionnaires; D013997:Time Factors; D000069447:Tiotropium Bromide; D016896:Treatment Outcome
chemical_list: D001993:Bronchodilator Agents; D003692:Delayed-Action Preparations; D004983:Ethanolamines; D007189:Indans; D015363:Quinolones; D012602:Scopolamine Derivatives; D000068299:Salmeterol Xinafoate; C510790:indacaterol; D000420:Albuterol; D000068759:Formoterol Fumarate; D000069447:Tiotropium Bromide