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Parolee Recidivism and Successful Treatment Completion: Comparing Hazard Models Across Propensity Methods

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Abstract

Objectives

Ascertaining the effect of treatment on recidivism is a core area of investigation in criminology and corrections research. The two objectives of the current analysis are: (1) to determine the true effect of treatment regimen completion on time to recidivism controlling for propensity to complete treatment; and (2) to examine the sensitivity of results under various propensity score methods.

Methods

Drawing on the population (n = 1,270) of parolees residing in a Midwestern state, we examine the effect of completing a treatment regimen on days to recidivism (using two failure outcomes) over a 2-year period using proportional hazard models. We adjust for the propensity to complete a treatment regimen using the covariate adjustment, inverse weighting, case matching, and strata methods.

Results

Completing a treatment regimen has a sizable effect at reducing recidivism risk, which grows stronger the longer offenders are on parole. This effect is consistent across treatment propensity methods. It is driven mainly by completion of alcohol and drug treatment regimens. Treatment effects are stable across two measures of recidivism (arrest/prison-return and prison-return only).

Conclusions

Discussion centers on the implications for assessing treatment success in the parole population as well as on methodological implications for researchers conducting similar research. In the current analysis propensity scores produce stable results regardless of propensity method. Guidance is provided on selecting propensity methods based on data distortion, technical expertise, and presentation of results. We conclude that the covariate adjustment method is best suited for novice researchers, and the case matching method for expert researchers. The strata method is recommended for supplemental analyses. Future research should examine treatment effects reporting at least two propensity methods.

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Notes

  1. There were a small number of missing cells on certain LSI questions (under 5 % of cells). Instead of dropping these cases or setting them to zero (artificially lower composite risk scores), we chose to impute the missing cells using the average of 500 imputations using the Markov Chain Monte Carlo algorithm (MCMC). All imputed cells have coefficients of variation under 25 %, indicating stability across imputations.

  2. LSI normative group taken from Multi-Health Systems, Inc. Minimum LSI risk range is below 12, and maximum LSI risk range is above 36.

  3. For purposes of presentation, Table 2 only reports the LSI subscales rather than the 55 base and 55 change items.

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Authors and Affiliations

  1. Sociology, Iowa State University, 304 East Hall, Ames, IA, 50011-1070, USA

    David J. Peters

  2. Sociology, Iowa State University, 203-B East Hall, Ames, IA, 50011-1070, USA

    Andy Hochstetler

  3. Sociology, Iowa State University, 203-A East Hall, Ames, IA, 50011-1070, USA

    Matt DeLisi

  4. Sociology, Iowa State University, 103 East Hall, Ames, IA, 50011-1070, USA

    Hui-Ju Kuo

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  1. David J. Peters
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  2. Andy Hochstetler
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  4. Hui-Ju Kuo
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Correspondence to David J. Peters.

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Peters, D.J., Hochstetler, A., DeLisi, M. et al. Parolee Recidivism and Successful Treatment Completion: Comparing Hazard Models Across Propensity Methods. J Quant Criminol 31, 149–181 (2015). https://doi.org/10.1007/s10940-014-9229-2

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