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

BMC Medical Research Methodology

Anne Bukten¹  Jon Michael Gran²  Thomas Clausen¹  Jo Røislien² 

[1] SERAF, Norwegian Centre for Addiction Research, University of Oslo, Kirkeveien 166, 0407 Oslo, Norway;Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Boks 1122 Blindern, 0317 Oslo, Norway

关键词: Dynamic covariate;    Time-dependent covariate;    Recurring event;    Time-to-event;    Criminal activity;    Maintenance treatment;   

Others  :  865409
DOI  :  10.1186/1471-2288-14-68

 received in 2013-09-30, accepted in 2014-05-06,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Røislien et al.; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [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.
• [2]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.
• [3]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.
• [4]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.
• [5]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.
• [6]Gossop M, Marsden J, Stewart D, Treacy S: Change and stability of change after treatment of drug misuse: 2-year outcomes from the National Treatment Outcome Research Study (UK). Addict Behav 2002, 27(2):155-166.
• [7]Hser YI, Hoffman V, Grella CE, Anglin MD: A 33-year follow-up of narcotics addicts. Arch Gen Psychiatry 2001, 58(5):503-508.
• [8]Clausen T, Anchersen K, Waal H: Mortality prior to, during and after opioid maintenance treatment (OMT): a national prospective cross-registry study. Drug Alcohol Depend 2008, 94(1–3):151-157.
• [9]Keene J: A case-linkage study of the relationship between drug misuse, crime, and psychosocial problems in a total criminal justice population. Addict Res Theory 2005, 13(5):489-502.
• [10]Bennett T, Holloway K: The causal connection between drug misuse and crime. Br J Criminol 2009, 49(4):513-531.
• [11]Adamson SJ, Sellman JD: The pattern of intravenous drug use and associated criminal activity in patients on a methadone treatment waiting list. Drug Alcohol Rev 1998, 17(2):159-166.
• [12]Bukten A, Skurtveit S, Gossop M, Waal H, Stangeland P, Havnes I, Clausen T: Engagement with opioid maintenance treatment and reductions in crime: a longitudinal national cohort study. Addiction 2012, 107(2):393-399.
• [13]Ball JC, Ross A: The Effectiveness of Methadone Maintenance Treatment: Patients, Programs, Services, and Outcome. New York: Springer; 1991.
• [14]Marsch LA: The efficacy of methadone maintenance interventions in reducing illicit opiate use, HIV risk behavior and criminality: a meta-analysis. Addiction 1998, 93(4):515-532.
• [15]Oliver P, Keen J, Rowse G, Ewins E, Griffiths L, Mathers N: The effect of time spent in treatment and dropout status on rates of convictions, cautions and imprisonment over 5 years in a primary care-led methadone maintenance service. Addiction 2010, 105(4):732-739.
• [16]Bell J, Burrell T, Indig D, Gilmour S: Cycling in and out of treatment; participation in methadone treatment in NSW, 1990–2002. Drug Alcohol Depend 2006, 81(1):55-61.
• [17]Dolan KA, Shearer J, White B, Zhou J, Kaldor J, Wodak AD: Four-year follow-up of imprisoned male heroin users and methadone treatment: mortality, re-incarceration and hepatitis C infection. Addiction 2005, 100(6):820-828.
• [18]Havnes I, Bukten A, Gossop M, Waal H, Stangeland P, Clausen T: Reductions in convictions for violent crime during opioid maintenance treatment: a longitudinal national cohort study. Drug Alcohol Depend 2012, 124(3):307-310.
• [19]Andersen PK, Gill RD: Cox’s regression model for counting processes: a large sample study. Ann Stat 1982, 10(4):1100-1120.
• [20]Cox DR: Regression models and life-tables. J Roy Stat Soc B Met 1972, 34(2):187-220.
• [21]Andersen PK: Statistical Models Based on Counting Processes. New York: Springer; 1993.
• [22]Aalen OO, Borgan Ø, Gjessing HK: Survival and event history analysis: a process point of view. New York: Springer; 2008.
• [23]Efron B, Tibshirani RJ: An Introduction to the Bootstrap. London: Chapman & Hall; 1993.
• [24]Bukten A, Roislien J, Skurtveit S, Waal H, Gossop M, Clausen T: A day-by-day investigation of changes in criminal convictions before and after entering and leaving opioid maintenance treatment: a national cohort study. BMC Psychiatry 2013, 13(1):262.
• [25]Kalbfleisch JD, Prentice RL: The statistical analysis of failure time data. Hoboken: John Wiley & Sons; 2002.
• [26]Grambish PM, Therneau TM: Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 1994, 81(3):515-526.
• [27]Aalen OO: A model for nonparametric regression analysis of counting processes. In Mathematical Statistics and Probability Theory Lecture Notes in Statistics. Volume 2, edn. New York: Springer-Verlag; 1980:1-25.
• [28]Fosen J, Borgan Ø, Weedon-Fekjær H, Aalen OO: Dynamic analysis of recurrent event data using the additive hazard model. Biom J 2006, 48(3):381-398.
• [29]McKeague IW, Sasieni PD: A partly parametric additive risk model. Biometrika 1994, 81(3):501-514.
• [30]Team RDC: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
• [31]Martinussen T, Scheike TH: Dynamic Regression Models for Survival Data. New York: Springer; 2006.
• [32]Cook RJ, Lawless JF: The statistical analysis of recurrent events. New York: Springer; 2007.
• [33]Andersen PK, Keiding N: Multi-state models for event history analysis. Stat Methods Med Res 2002, 11(2):91-115.
• [34]Commenges D: Multi-state models in epidemiology. Lifetime Data Anal 1999, 5(4):315-327.
• [35]Hernán MA, Robins JM: Estimating causal effects from epidemiological data. J Epidemiol Community Health 2006, 60(7):578-586.
• [36]Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA: Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol 2002, 155(2):176-184.
• [37]Aalen OO: Armitage lecture 2010: understanding treatment effects: the value of integrating longitudinal data and survival analysis. Stat Med 2012, 31(18):1903-1917.
• [38]Aalen OO, Røysland K, Gran JM, Ledergerber B: Causality, mediation and time: a dynamic viewpoint. J Roy Stat Soc A Sta 2012, 175(4):831-861.
• [39]Kristensen P, Aalen OO: Understanding mechanisms: opening the “black box” in observational studies. Scand J Work Environ Health 2013, 39(2):121-124.
• [40]Sampson RJ, Laub JH, Wimer C: Does marriage reduce crime? A counterfactual approach to within-individual causal effects. Criminology 2006, 44(3):465-508.
• [41]Phillips MD: Assessing the impact of drug use and drug selling on violent offending in a panel of delinquent youth. J Drug Issues 2012, 42(3):298-316.
• [42]Maxfield MG, Babbie R: Basics of research methods for criminal justice and criminology. 3rd edition. Belmont: Wadsworth/Cengage Learning; 2011.