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. 2014 May 17:14:68.
doi: 10.1186/1471-2288-14-68.

Accounting for individual differences and timing of events: estimating the effect of treatment on criminal convictions in heroin users

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Accounting for individual differences and timing of events: estimating the effect of treatment on criminal convictions in heroin users

Jo Røislien et al. BMC Med Res Methodol. .

Abstract

Background: The reduction of crime is an important outcome of opioid maintenance treatment (OMT). Criminal intensity and treatment regimes vary among OMT patients, but this is rarely adjusted for in statistical analyses, which tend to focus on cohort incidence rates and rate ratios. The purpose of this work was to estimate the relationship between treatment and criminal convictions among OMT patients, adjusting for individual covariate information and timing of events, fitting time-to-event regression models of increasing complexity.

Methods: National criminal records were cross linked with treatment data on 3221 patients starting OMT in Norway 1997-2003. In addition to calculating cohort incidence rates, criminal convictions was modelled as a recurrent event dependent variable, and treatment a time-dependent covariate, in Cox proportional hazards, Aalen's additive hazards, and semi-parametric additive hazards regression models. Both fixed and dynamic covariates were included.

Results: During OMT, the number of days with criminal convictions for the cohort as a whole was 61% lower than when not in treatment. OMT was associated with reduced number of days with criminal convictions in all time-to-event regression models, but the hazard ratio (95% CI) was strongly attenuated when adjusting for covariates; from 0.40 (0.35, 0.45) in a univariate model to 0.79 (0.72, 0.87) in a fully adjusted model. The hazard was lower for females and decreasing with older age, while increasing with high numbers of criminal convictions prior to application to OMT (all p < 0.001). The strongest predictors were level of criminal activity prior to entering into OMT, and having a recent criminal conviction (both p < 0.001). The effect of several predictors was significantly time-varying with their effects diminishing over time.

Conclusions: Analyzing complex observational data regarding to fixed factors only overlooks important temporal information, and naïve cohort level incidence rates might result in biased estimates of the effect of interventions. Applying time-to-event regression models, properly adjusting for individual covariate information and timing of various events, allows for more precise and reliable effect estimates, as well as painting a more nuanced picture that can aid health care professionals and policy makers.

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Figures

Figure 1
Figure 1
Day-by-day mean number of criminal convictions. Day-by-day number of criminal convictions (crime days) per people at risk (grey dots), with cubic smoothing spline superimposed (black lines), for a cohort of 3221 Norwegian heroin users when in opioid maintenance treatment (OMT) and not.
Figure 2
Figure 2
Individual cumulative incidence of criminal convictions. Cumulative incidence of criminal convictions (crime days) for 16 random individuals in opioid maintenance treatment (OMT) from application to OMT (left dotted line) to study end December 31st 2003 (right dotted line). Periods not in treatment (light grey area) and in treatment (dark grey area).
Figure 3
Figure 3
Additive hazards regression models. Univariate and multiple Aalen’s additive hazards regression models for days with criminal convictions as a recurrent event dependent variable and treatment as a time-dependent covariate, and the non-parametric components of a multiple semi-parametric additive hazards regression model.

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References

    1. Sudre P, Rickenbach M, Taffé P, Janin P, Volkart AC, Francioli P, Swiss HIVCS. Clinical epidemiology and research on HIV infection in Switzerland: the Swiss HIV Cohort Study 1988–2000. Schweiz Med Wochenschr. 2000;130(41):1493–1500. - PubMed
    1. Røysland K, Gran JM, Ledergerber B, von Wyl V, Young J, Aalen OO. Analyzing direct and indirect effects of treatment using dynamic path analysis applied to data from the Swiss HIV Cohort Study. Stat Med. 2011;30(24):2947–2958. - PubMed
    1. Moncino MD, Falletta JM. Multiple relapses of clostridium difficile-associated diarrhea in a cancer patient: successful control with long-term cholestyramine therapy. J Pediatr Hematol Oncol. 1992;14(4):4. - PubMed
    1. Pestalozzi BC, Holmes E, de Azambuja E, Metzger-Filho O, Hogge L, Scullion M, Láng I, Wardley A, Lichinitser M, Sanchez RIL, Müller V, Dodwell D, Gelber RD, Piccart-Gebhart MJ, Cameron D. CNS relapses in patients with HER2-positive early breast cancer who have and have not received adjuvant trastuzumab: a retrospective substudy of the HERA trial (BIG 1–01) Lancet Oncol. 2013;14(3):244–248. - PubMed
    1. Gossop M, Marsden J, Stewart D, Rolfe A. Patterns of improvement after methadone treatment: 1 year follow-up results from the National Treatment Outcome Research Study (NTORS) Drug Alcohol Depend. 2000;60(3):275–286. - PubMed