Analysis of Multiple Informant/Source Data in Stata

1. Analysis of multiple informant/multiple source data in Stata Nicholas J. Horton Department of Mathematics Smith College, Northampton MA Garrett M. Fitzmaurice Harvard University nhorton at email.smith.edu http://www.biostat.harvard.edu/multinform

2. Acknowledgements • Joint research project with Nan Laird and colleagues, Harvard School of Public Health • Jane Murphy and the Stirling County Study for use of their example dataset (see Horton et al AJE, 2001 for more details) • Supported by NIH grant RO1-MH54693

3. Outline • Motivation for multiple source data • Examples of multiple sources/informants • Models for correlated multiple source data • Accounting for complex survey design • Accounting for incomplete/missing data • Example (Stirling County Study) • Conclusions

4. Why multiple source data? • to provide better measures of some underlying construct that is difficult to measure or likely to be missing • also known as multiple informant reports, proxy reports, co-informants, etc. • discordance is expected, otherwise there is no need to collect multiple reports • Statistical framework developed in (Horton and Fitzmaurice SIM tutorial, 2004)

5. Definition of multiple source data • data obtained from multiple informants or raters (e.g., self-reports, family members, health care providers, teachers) • or via different/parallel instruments or methods (e.g., symptom rating scales, standardized diagnostic interviews, or clinical diagnoses) • None of the reports is a “gold’’ standard • We consider multiple source data that are commensurate (multiple measures of the same underlying variable on a similar scale)

6. Examples of multiple source data • child psychopathology (ask parents, teachers and children about underlying psychological state) • service utilization studies (collect information from subjects and databases) • medical comorbidity (query providers and charts to assess medical problems)

7. Examples of multiple source data (cont.) • adherence studies (collect self-report of adherence, electronic pill caps [MEMS] plus pharmacy records) • nutritional epidemiology (utilize multiple dietary instruments such as food frequency questionnaires, 24-hour recalls, food diaries)

8. Incomplete/missing reports • Multiple source reports are commonly incomplete since, by definition, they are collected from sources other than the primary subject of the study • This missingness may be by design or happenstance (or both!)

9. Example: missing source reports • Consider service utilization studies that collect information from subjects and databases • Subjects may be lost to follow-up (or only contacted periodically) • Databases may be incomplete (lack of consent, lack of appropriate coverage)

10. Analytic approach • Multiple sources can provide information on outcomes or predictors (risk factors) • Multiple source outcome: what is the prevalence of child psychopathology? (measured using parallel parent and teacher reports) • Fitzmaurice et al (AJE, 1995), Horton et al (HSOR, 2002), Horton and Fitzmaurice (SIM tutorial, 2004)

11. Analytic approach (cont.) • Multiple source predictor: what are the odds of developing depression in adulthood, conditional on parallel reports of anxiety (collected from a child and a parent)? • Examples: Horton et al (AJE, 2001), Lash et al (AJE, 2003), Liddicoat et al (JGIM, 2004), Horton and Fitzmaurice (SIM tutorial, 2004) • We will focus on an example using multiple source predictors

12. Notation • Let Y denote a univariate outcome for a given subject • Let denote the l’th multiple source predictor • Let Z denote a vector of other covariates for the subject • To simplify exposition, we consider two sources with dichotomous reports (L=2)

13. Questions to consider • Are the sources reporting on the same underlying construct (are they commensurate or interchangeable?) • Is it possible to combine the reports in some fashion? • How to handle missing reports?

14. Analytic approaches • Reviewed in Horton, Laird and Zahner (IJMPR, 1999) • Use only one source • Fit separate models

15. Analytic approaches (cont.) • Combine (pool) the reports in some fashion • Include both reports in the model

16. Analytic approaches (cont.) • We considered simultaneous estimation of the marginal models: • Non-standard application of GEE • Method independently suggested by Pepe et al (SIM, 1999)

17. Advantages of new approach • can be used to test for source differences in association with the outcome • can test if the effects of other risk factors on the outcome differ by source

18. Advantages of new approach • different source effects where necessary • a pooled model can be fit if no significant source effects (potentially more efficient) • can be fit using general purpose statistical software (Stata and others)

19. Accounting for survey design • Many health services or epidemiologic studies arise from complex survey samples • Need to address stratification, multi-stage clustering and unequal sampling weights • Failing to properly account for survey design may lead to bias and incorrect estimation of variability

20. Accounting for survey design (cont.) • Estimation proceeds using the approximate (quasi) log-likelihood (weighted version of the usual score equations for a GLM, accounting for the multi-stage clustering, including multiple source reports) • Can be fit using general purpose statistical software (elegant and powerful implementation in Stata)

21. Accounting for incomplete source reports • Missing source reports are missing predictors • Use weighted estimating equation methodology of Robins et al (JASA, 1994) and Xie and Paik (Biometrics, 1997), applied by Horton et al, (AJE, 2001) • Adds an additional “missingness weight” • Complications to variance estimation

22. Example: Stirling County Study • Outcome: time to event (death) over 16 year follow-up period (1952-1968) (n=1079) • multiple source predictors: partially observed dichotomous physician report or self report of psychiatric disorder (dpax) • other predictors: age (3 categories), gender • statistical model: piecewise exponential survival with 4 intervals each of 4 years duration (subjects contribute time at risk in each interval)

23. Stirling County survey design Strata 1 Stratum 1 Stratum k Stratum K PSU 1 PSU J PSU j self- report phys.- report

24. Implementation in Stata Specify probability sampling unit (subject), probability sampling weights (weight) and stratification variable (district): svyset id [pweight=weight], strata(district) Describe the sampling design: svydes

25. Survey: Describing stage 1 sampling units pweight: weight VCE: linearized Strata 1: district SU 1: id FPC 1: <zero> #Obs per Unit ---------------------------- Stratum #Units #Obs min mean max -------- -------- -------- -------- -------- -------- 1 93 654 2 7.0 8 2 37 284 4 7.7 8 3 51 346 2 6.8 8 4 202 1488 2 7.4 8 5 291 2104 2 7.2 8 6 128 946 2 7.4 8 7 50 374 4 7.5 8 8 98 706 2 7.2 8 9 129 968 2 7.5 8 -------- -------- -------- -------- -------- -------- 9 1079 7870 2 7.3 8

26. Implementation in Stata (cont.) xi: svy: poisson event dpax int1 int2 int3 female ageind1ageind2 diag i.diag*ageind1 i.diag*ageind2 i.dpax*female i.dpax*ageind1 i.dpax*ageind2 i.dpax*diag, exposure(atrisk)

27. Implementation in Stata (cont.) Can then test for significant informant effects (any term with dpax [self-report] in the model): test dpax=0 test _IdpaXfemal_1, accumulate test _IdpaXagein_1, accumulate test _IdpaXageina1, accumulate test _IdpaXdiag_1, accumulate

28. Results (separate parameters) • Initially fit model with separate parameters • No evidence for source interactions • Implies that the association between risk factors and mortality did not differ by source • Dropped these terms from the model, yielding parsimonious shared parameter model with smaller standard errors

29. Implementation (shared parameter) xi: svy: poisson event int1 int2 int3 female ageind1 ageind2 diag i.diag*ageind1 i.diag*ageind2, exposure(atrisk)

30. Results (shared parameter) Survey: Poisson regression Number of strata = 9 Number of obs = 7420 Number of PSUs = 1079 Population size = 64723.522 Design df = 1070 F( 9, 1062) = 21.94 Prob > F = 0.0000 ------------------------------------------------------------------------------ | Linearized event | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- int1 | -.9594993 .2058191 -4.66 0.000 -1.363354 -.5556444 int2 | -.5680445 .1936756 -2.93 0.003 -.9480716 -.1880174 int3 | -.360743 .2002561 -1.80 0.072 -.7536821 .0321962 female | -.1298938 .1493215 -0.87 0.385 -.42289 .1631024 ageind1 | 2.484883 .2820244 8.81 0.000 1.931499 3.038266 ageind2 | 3.530875 .2894511 12.20 0.000 2.962919 4.098831 diag | 1.62166 .3256041 4.98 0.000 .982765 2.260555 _IdiaXage~_1 | -1.351475 .379926 -3.56 0.000 -2.09696 -.6059908 _IdiaXage~a1 | -1.313849 .4554167 -2.88 0.004 -2.20746 -.4202376 _cons | -5.577167 .2941931 -18.96 0.000 -6.154428 -4.999906 atrisk | (exposure) ------------------------------------------------------------------------------

31. Results (shared parameters)

32. Interpretation of results (annual mortality rate)

33. Results (2 df test of interaction of age and diagnosis) . test _IdiaXagein_1=0 Adjusted Wald test ( 1) [event]_IdiaXagein_1 = 0 F( 1, 1070) = 12.65 Prob > F = 0.0004 . test _IdiaXageina1, accumulate Adjusted Wald test ( 1) [event]_IdiaXagein_1 = 0 ( 2) [event]_IdiaXageina1 = 0 F( 2, 1069) = 6.67 Prob > F = 0.0013

34. Results (calculation of MRR and 95% CI) . lincom diag, eform ( 1) [event]diag = ------------------------------------------------------------------ event | exp(b) Std.Err. t P>|t| [95% Conf. Interval] ------+----------------------------------------------------------- (1) | 5.0615 1.6480 4.98 0.000 2.6718 9.5884 ------------------------------------------------------------------ . lincom diag + _IdiaXagein_1, eform ( 1) [event]diag + [event]_IdiaXagein_1 = 0 ------------------------------------------------------------------ event | exp(b) Std.Err. t P>|t| [95% Conf. Interval] ------+----------------------------------------------------------- (1) | 1.3102 .25297 1.40 0.162 .89703 1.9137 ------------------------------------------------------------------

35. Conclusions • new methods of analysis of multiple source data are available • can be implemented using existing software • methods allow the assessment of the relative association of each source • each source yielded similar conclusions: association between psychiatric disorder and mortality is stronger for younger subjects • unified model has less variability, pools information after testing for systematic differences

36. Conclusions (cont.) • methods account for complex survey designs • methods incorporate partially observed subjects to contribute, under MAR (Little and Rubin book) assumptions • multiple source reports arise in many settings (not just for children anymore!)

37. Future work • Maximum-likelihood estimation instead of GEE approach • May yield efficiency gains • Particularly useful for missing reports • Non-commensurate reports • Different scales • Different underlying constructs • Consider latent variable models (e.g. work of Normand and colleagues) • See also gllamm and forthcoming Stata book by Rabe-Hesketh and Skrondal)

38. Analysis of multiple informant/multiple source data in Stata Nicholas Horton Department of Mathematics Smith College, Northampton MA nhorton at email.smith.edu http://www.biostat.harvard.edu/multinform