title: Cross-linked survey analysis is an approach for separating cause and effect in survey research.
abstract: OBJECTIVE
  We developed a new research approach, called cross-linked survey analysis, to explore how an acute exposure might lead to changes in survey responses. The goal was to identify associations between exposures and outcomes while reducing some ambiguities related to interpreting cause and effect in survey responses from a population-based community questionnaire.
  
  
  METHODS
  Cross-linked survey analysis differs from a cross-sectional, longitudinal, and panel survey analysis by individualizing the timeline to the unique history of each respondent. Cross-linked survey analysis, unlike a repeated-measures self-matching design, does not track changes in a repeated survey question given to the same respondent at multiple time points.
  
  
  RESULTS
  Pilot data from three analyses (n = 1,177 respondents) illustrate how a cross-linked survey analysis can control for population shifts, temporal trends, and reverse causality. Accompanying graphs provide an intuitive display to readers, summarize results, and show differences in response distributions. Population-based individual-level linkages also reduce selection bias and increase statistical power compared with a single-center cross-sectional survey. Cross-linked survey analysis has limitations related to unmeasured confounding, pragmatics, survivor bias, statistical models, and the underlying artifacts in survey responses.
  
  
  CONCLUSIONS
  We suggest that a cross-linked survey analysis may help in epidemiology science using survey data.
authors.affiliation: Department of Medicine, University of Toronto, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5; Evaluative Clinical Science, Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5; Institute for Clinical Evaluative Sciences in Ontario, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5; Division of General Internal Medicine, Sunnybrook Health Sciences Centre, G-151, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5; Center for Leading Injury Prevention Practice Education & Research, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5. Electronic address: dar@ices.on.ca.
authors.forename: Donald A
authors.initials: DA
authors.lastname: Redelmeier
authors.affiliation: Institute for Clinical Evaluative Sciences in Ontario, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5.
authors.forename: Deva
authors.initials: D
authors.lastname: Thiruchelvam
authors.affiliation: Institute for Clinical Evaluative Sciences in Ontario, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5; Department of Psychiatry, University of Toronto, 2075 Bayview Ave, Toronto, Ontario, Canada M4N3M5.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Lustig
mesh_terms: D015982:Bias; D003430:Cross-Sectional Studies; D003625:Data Collection / Q000379:methods*; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010865:Pilot Projects; D011247:Pregnancy; D015983:Selection Bias; D011795:Surveys and Questionnaires
keywords: Epidemiologic research designs; Evaluation studies as topic; Intervention studies; Outcome and process assessment; Self-matched studies; Survey analysis