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Objective: To present a case study on how to compare various matching methods applying different measures of balance and to point out some pitfalls involved in relying on such measures.Data Sources: Administrative claims data from a German statutory health insurance fund covering the years 2004-2008.Study Design: We applied three different covariance balance diagnostics to a choice of 12 different matching methods used to evaluate the effectiveness of the German disease management program for type 2 diabetes (DMPDM2). We further compared the effect estimates resulting from applying these different matching techniques in the evaluation of the DMPDM2.Principal Findings: The choice of balance measure leads to different results on the performance of the applied matching methods. Exact matching methods performed well across all measures of balance, but resulted in the exclusion of many observations, leading to a change of the baseline characteristics of the study sample and also the effect estimate of the DMPDM2. All PS-based methods showed similar effect estimates. Applying a higher matching ratio and using a larger variable set generally resulted in better balance. Using a generalized boosted instead of a logistic regression model showed slightly better performance for balance diagnostics taking into account imbalances at higher moments.Conclusion: Best practice should include the application of several matching methods and thorough balance diagnostics. Applying matching techniques can provide a useful preprocessing step to reveal areas of the data that lack common support. The use of different balance diagnostics can be helpful for the interpretation of different effect estimates found with different matching methods. [ABSTRACT FROM AUTHOR]

The present study examines the long-term recidivism patterns of a group of unauthorized immigrants identified to be at high risk of recidivism. Using a sample of 517 male unauthorized immigrants, we used three measures of recidivism to assess 9-year rearrest differences between unauthorized immigrants who have and who have not been previously removed from the United States. Results indicate that prior removal was a significant risk marker for recidivism, with previously removed immigrants showing a higher likelihood of rearrest, a greater frequency of rearrest, and a more rapid time-to-first rearrest. While the present study does not establish whether previous removal is a consistent indicator of high recidivism, it suggests that this group of unauthorized immigrants may be worthy of review and policy consideration. Much potential value for law enforcement lies in the sharing of federal immigration records with academics to further study the outcomes of unauthorized immigrants. [ABSTRACT FROM AUTHOR]

We implement a propensity score matching technique to present the first evidence on the impact of professional sports lockouts on player productivity. In particular, we utilize a unique natural experiment from the 2012-2013 National Hockey League lockout, during which approximately 200 players decided to play overseas, while the rest stayed in North America. We separate players based on their nationality and investigate the effect of playing abroad on postlockout player performance. We find limited evidence of enhanced productivity among European players and no evidence of a benefit or drawback for North American players. Our study contributes to the understanding of lockouts in professional sports and the general discussion of labor disputes and worker productivity. [ABSTRACT FROM AUTHOR]

As policymakers contemplate expanding preschool opportunities for low-income children, one possibility is to fund 2, rather than 1 year of Head Start for children at ages 3 and 4. Another option is to offer 1 year of Head Start followed by 1 year of pre-K. We ask which of these options is more effective. We use data from the Oklahoma pre-K study to examine these two “pathways” into kindergarten using regression discontinuity to estimate the effects of each age 4 program, and propensity score weighting to address selection. We find that children attending Head Start at age 3 develop stronger prereading skills in a high-quality pre-kindergarten at age 4 compared with attending Head Start at age 4. Pre-K and Head Start were not differentially linked to improvements in children’s prewriting skills or premath skills. This suggests that some impacts of early learning programs may be related to the sequencing of learning experiences to more academic programming. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate with an ex post facto quasi-experimental design the impact of tactical police response on residential theft from vehicle crime in micro-time hot spots as well as whether spatial displacement occurs. Methods: The evaluation uses 5 years of data from one police agency that responded to micro-time hot spots as part of its normal crime reduction efforts. To determine the experimental comparison group, propensity scores were computed using logistic regression. Cases were matched using greedy 1-1 matching with a caliper of 0.10 of the standard deviation of the logit of the propensity score and resulted in 86 pairs. t Tests were used to examine the effect of the treatment and whether spatial displacement of crime occurred as a result. Results: Results showed that when police responded with about seven responses per day and for between 2 and 3 weeks, there was nearly a 20 % reduction in residential theft from vehicle crimes, and the micro-time hot spots with response did not last as long as those that did not. Results also showed no spatial displacement of crime as a result of the response. Conclusions: This evaluation is first to examine tactical police strategies for property crimes occurring at micro-places in micro-time. Findings support the hypothesis that micro-time hot spots are less severe and 'cool off' more quickly after a response. Thus, police should consider responding to property crime occurring in micro-places at a smaller temporal unit. Future research should further explore this unit of police response to corroborate these results. [ABSTRACT FROM AUTHOR]

The big advancement in technology and the availability of 'big data', pose new demands for more detailed, more accurate and timelier official statistics. The target of the present article is to discuss some of the major new methodological challenges underlying the production of official statistics (POS) in coming years, and in some cases suggest ways of dealing with them. In particular, I consider the following challenges: collection and management of big data for POS with integration of computer science, increasing data accessibility but maintaining privacy and confidentiality, possible use of web-panels for POS, how to deal with mode effects, measurement of error in small area estimation in conjunction with modern censuses, and integration of statistics and geospatial information. In the last part of the article I confront the question of whether universities train students to work at National Statistical Offices. [ABSTRACT FROM AUTHOR]

The framework of principal stratification provides a way to think about treatment effects conditional on post-randomization variables, such as level of compliance. In particular, the complier average causal effect (CACE) - the effect of the treatment for those individuals who would comply with their treatment assignment under either treatment condition - is often of substantive interest. However, estimation of the CACE is not always straightforward, with a variety of estimation procedures and underlying assumptions, but little advice to help researchers select between methods. In this article, we discuss and examine two methods that rely on very different assumptions to estimate the CACE: a maximum likelihood ('joint') method that assumes the 'exclusion restriction,' (ER) and a propensity score-based method that relies on 'principal ignorability.' We detail the assumptions underlying each approach, and assess each methods' sensitivity to both its own assumptions and those of the other method using both simulated data and a motivating example. We find that the ER-based joint approach appears somewhat less sensitive to its assumptions, and that the performance of both methods is significantly improved when there are strong predictors of compliance. Interestingly, we also find that each method performs particularly well when the assumptions of the other approach are violated. These results highlight the importance of carefully selecting an estimation procedure whose assumptions are likely to be satisfied in practice and of having strong predictors of principal stratum membership. [ABSTRACT FROM AUTHOR]

The topic of ‘illegal’ immigration is currently the focus of intense ideological and policy debate in the United States. A common assertion is that those without legal immigration status are disproportionately involved in criminal offending relative to other foreign-born populations. The current study examines the long-term recidivism patterns of a group of male removable aliens compared to those foreign-born with legal authorisation to be present in the Unites States. The sample includes 1297 foreign-born males released from the Los Angeles County Jail during a 1-month period in 2002, and the follow-up period extends through 2011. Using three measures of rearrest and a rigorous counterfactual modelling approach, we find no statistically significant differences between the two groups in likelihood, frequency, or timing of first rearrest over 9 years. The findings do not lend support to arguments that removable aliens pose a disproportionate risk of repeat involvement in local criminal justice systems. [ABSTRACT FROM PUBLISHER]

Background: Police agencies around the country are implementing various strategies to reduce crime in their communities that need to be evaluated. These strategies are often based on systematic crime analysis and are focused on crime occurring in hot spots, which are areas of disproportionate amounts of crime. Objective: This article takes a practice-based research approach to evaluate whether evidence-based police strategies implemented by one police agency as its normal everyday crime reduction practice are effective in reducing residential burglary incidents in micro-time hot spots. Research Design: A quasi-experimental ex post facto design is employed using 5 years of data from one police agency that has institutionalized the identification and response to micro-time hot spots into its day-to-day practices. Propensity score matching is used to match 54 pairs of micro-time hot spots using logistic regression to compute the propensity scores and greedy 1 to 1 matching with a caliper width of 0.5 of the standard deviation of the logit to match the cases. Results: Independent t-tests show that tactical police response to micro-time hot spots can lead to significant reductions in residential burglary incidents without the spatial displacement of crime. Conclusion: Tactical police responses that seek to achieve short-term reductions in crime appear to be well suited for micro-time hot spots since they are, by nature, short term. Importantly, the conclusions are based on the evaluation of an agency’s systematic implementation of the evidence-based practices as its normal practices and not for the sake of research. [ABSTRACT FROM AUTHOR]

Background: National mortality data are obtained routinely by the Disease Surveillance Points system (DSPs) in China and under-reporting is a big challenge in mortality surveillance. Methods: We carried out an under-reporting field survey in all 161 DSP sites to collect death cases during 2009-2011, using a multi-stage stratified sampling. To identify under-reporting, death data were matched between field survey system and the routine online surveillance system by an automatic computer checking followed by a thorough manual verification. We used a propensity score (PS) weighting method based on a logistic regression to calculate the under-reporting rate in different groups classified by age, gender, urban/rural residency, geographic locations and other mortality related variables. For comparison purposes, we also calculated the under-reporting rate by using capture-mark-recapture (CMR) method. Results: There were no significant differences between the field survey system and routine online surveillance system in terms of age group, causes of death, highest level of diagnosis and diagnostic basis. The overall under-reporting rate in the DSPs was 12.9 % (95%CI 11.2 %, 14.6 %) based on PS. The under-reporting rate was higher in the west (18.8 %, 95%CI 16.5 %, 21.0 %) than the east (10.1 %, 95%CI 8.6 %, 11.3 %) and central regions (11.2 %, 95%CI 9.6 %, 12.7 %). Among all age groups, the under-reporting rate was highest in the 0-5 year group (23.7 %, 95%CI 16.1 %, 35.5 %) and lowest in the 65 years and above group (12.4 %, 95%CI 10.9 %, 13.6 %). The under-reporting rates in each group by PS were similar to the results calculated by the CMR methods. Conclusions: The mortality data from the DSP system in China needs to be adjusted. Compared to the commonly used CMR method in the estimation of under-reporting rate, the results of propensity score weighting method are similar but more flexible when calculating the under-reporting rates in different groups. Propensity score weighting is suitable to adjust DSP data and can be used to address under-reporting in mortality surveillance in China. [ABSTRACT FROM AUTHOR]

Neuroimaging research is usually conducted in volunteers who meet a priori selection criteria. Selection/volunteer bias is assumed but cannot be assessed. During an ongoing population-based cohort study of 1982 older adults, we asked 1702 active participants about their interest in undergoing a research brain scan. Compared with those not interested, the 915 potentially interested individuals were significantly younger, more likely to be male, better educated, generally healthier, and more likely to be cognitively intact and dementia-free. In 48 of the interested individuals, we conducted a previously reported pilot structural magnetic resonance imaging (sMRI) study modelling mild cognitive impairment (MCI) vs. normal cognition, and Clinical Dementia Rating (CDR) = 0.5 vs. CDR = 0, as a function of sMRI atrophy ratings. We now compare these 48 individuals (1) with all interested participants, to assess selection bias; (2) with all who had been asked about their interest, to assess volunteer bias; and (3) with the entire study cohort, to assess attrition bias from those who had dropped out before the question was asked. Using these data in propensity score models, we generated weights which we applied to logistic regression models reanalyzing the data from the pilot sMRI study. These weighted models adjusted, in turn, for selection bias, interest/volunteer bias, and attrition bias. They show fewer regions of interest to be associated with MCI/ CDR than were in the original unweighted models. When study participants are drawn from a well-characterized population, they can be compared with non-participants, and the information used to correct study results for potential bias and thus provide more generalizable estimates. [ABSTRACT FROM AUTHOR]

Background: Exploring causal associations in HIV research requires careful consideration of numerous epidemiologic limitations. First, a primary cause of HIV, unprotected anal intercourse (UAI), is time-varying and, if it is also associated with an exposure of interest, may be on a confounding path. Second, HIV is a rare outcome, even in high-risk populations. Finally, for most causal, non-preventive exposures, a randomized trial is impossible. In order to address these limitations and provide a practical illustration of efficient statistical control via propensity-score weighting, we examine the causal association between rectal STI and HIV acquisition in the InvolveMENt study, a cohort of Atlanta-area men who have sex with men (MSM). We hypothesized that, after controlling for potentially confounding behavioral and demographic factors, the significant STI-HIV association would attenuate, but yield an estimate of the causal effect. Methods: The exposure of interest was incident rectal gonorrhea or chlamydia infection; the outcome was incident HIV infection. To adjust for behavioral confounding, while accounting for limited HIV infections, we used an inverse probability of treatment weighted (IPTW) Cox proportional hazards (PH) model for incident HIV. Weights were derived from propensity score modeling of the probability of incident rectal STI as a function of potential confounders, including UAI in the interval of rectal STI acquisition/censoring. Results: Of 556 HIV-negative MSM at baseline, 552 (99%) men were included in this analysis. 79 men were diagnosed with an incident rectal STI and 26 with HIV. 6 HIV-infected men were previously diagnosed with a rectal STI. In unadjusted analysis, incident rectal STI was significantly associated with subsequent incident HIV (HR (95%CI): 3.6 (1.4-9.2)). In the final weighted and adjusted model, the association was attenuated and more precise (HR (95% CI): 2.7 (1.2-6.4)). Conclusions: We found that, controlling for time-varying risk behaviors and time-invariant demographic factors, diagnosis with HIV was significantly associated with prior diagnosis of rectal CT or GC. Our analysis lends support to the causal effect of incident rectal STI on HIV diagnosis and provides a framework for similar analyses of HIV incidence. [ABSTRACT FROM AUTHOR]

Football hooliganism has long occupied the social, media, and political landscapes of fans around the world. In this study, we use data from the Cambridge Study in Delinquent Development, a longitudinal study of a large sample of males followed from childhood to late middle adulthood, to examine the relationship between involvement in football hooliganism and both conviction frequency and conviction trajectories. Results show that, although hooligans differ from non-hooligans on several childhood and teenage risk factors, and that they also incur many more convictions than their non-hooligan counterparts, these relationships do not hold after carefully matching hooligans and non-hooligans on key risk factors. Football hooliganism, for the most part, seems to be part of a constellation of antisocial behaviors that also includes criminal offending. [ABSTRACT FROM AUTHOR]

This longitudinal study aims to explore the potential causal relationship between parental knowledge and youth risky behavior among a sample of rural, early adolescents (84 % White, 47 % male). Using inverse propensity weighting, the sample was adjusted by controlling for 33 potential confounding variables. Confounding variables include other aspects of the parent-child relationship, parental monitoring, demographic variables, and earlier levels of problem behavior. The effect of parental knowledge was significant for youth substance and polysubstance use initiation, alcohol and cigarette use, attitudes towards substance use, and delinquency. Our results suggest that parental knowledge may be causally related to substance use during middle school, as the relationship between knowledge and youth outcomes remained after controlling for 33 different confounding variables. The discussion focuses on understanding issues of causality in parenting and intervention implications. [ABSTRACT FROM AUTHOR]

Increasing nonresponse rates in federal surveys and potentially biased survey estimates are a growing concern, especially with regard to establishment surveys. Unlike household surveys, not all establishments contribute equally to survey estimates. With regard to agricultural surveys, if an extremely large farm fails to complete a survey, the United States Department of Agriculture (USDA) could potentially underestimate average acres operated among other things. In order to identify likely nonrespondents prior to data collection, the USDA's National Agricultural Statistics Service (NASS) began modeling nonresponse using Census of Agriculture data and prior Agricultural Resource Management Survey (ARMS) response history. Using an ensemble of classification trees, NASS has estimated nonresponse propensities for ARMS that can be used to predict nonresponse and are correlated with key ARMS estimates. [ABSTRACT FROM AUTHOR]

Confounding is widely recognized in settings where all variables are fully observed, yet recognition of and statistical methods to address confounding in the context of latent class regression are slowly emerging. In this study we focus on confounding when regressing a distal outcome on latent class; extending standard confounding methods is not straightforward when the treatment of interest is a latent variable. We describe a recent 1-step method, as well as two 3-step methods (modal and pseudoclass assignment) that incorporate propensity score weighting. Using simulated data, we compare the performance of these three adjusted methods to an unadjusted 1-step and unadjusted 3-step method. We also present an applied example regarding adolescent substance use treatment that examines the effect of treatment service class on subsequent substance use problems. Our simulations indicated that the adjusted 1-step method and both adjusted 3-step methods significantly reduced bias arising from confounding relative to the unadjusted 1-step and 3-step approaches. However, the adjusted 1-step method performed better than the adjusted 3-step methods with regard to bias and 95 % CI coverage, particularly when class separation was poor. Our applied example also highlighted the importance of addressing confounding-both unadjusted methods indicated significant differences across treatment classes with respect to the outcome, yet these class differences were not significant when using any of the three adjusted methods. Potential confounding should be carefully considered when conducting latent class regression with a distal outcome; failure to do so may results in significantly biased effect estimates or incorrect inferences. [ABSTRACT FROM AUTHOR]

Theory and simulations show that variables affecting the outcome only through exposure, known as instrumental variables (IVs), should be excluded from propensity score (PS) models. In pharmacoepidemiologic studies based on automated healthcare databases, researchers will sometimes use a single PS model to control for confounding when evaluating the effect of a treatment on multiple outcomes. Because these 'full' models are not constructed with a specific outcome in mind, they will usually contain a large number of IVs for any individual study or outcome. If researchers subsequently decide to evaluate a subset of the outcomes in more detail, they can construct reduced 'outcome-specific' models that exclude IVs for the particular study. Accurate estimates of PSs that do not condition on IVs, however, can be compromised when simply excluding instruments from the full PS model. This misspecification may have a negligible impact on effect estimates in many settings, but is likely to be more pronounced for situations where instruments modify the effects of covariates on treatment (instrument-confounder interactions). In studies evaluating drugs during early dissemination, the effects of covariates on treatment are likely modified over calendar time and IV-confounder interaction effects on treatment are likely to exist. In these settings, refitting more flexible PS models after excluding IVs and IV-confounder interactions can work well. The authors propose an alternative method based on the concept of marginalization that can be used to remove the negative effects of controlling for IVs and IV-confounder interactions without having to refit the full PS model. This method fits the full PS model, including IVs and IV-confounder interactions, but marginalizes over values of the instruments. Fitting more flexible PS models after excluding IVs or using the full model to marginalize over IVs can prevent model misspecification along with the negative effects of balancing instruments in certain settings. [ABSTRACT FROM AUTHOR]

In this article, we investigated the effects of marriage and spousal characteristics on the use of preventive care. We accounted for the endogeneity of marriage by combining propensity score estimation techniques with marriage transitions observed in longitudinal data. Results indicated that marriage increases the probability of dental check-ups and physical examinations for both sexes and mammograms and Pap smears for women. Next, we examined whether spousal characteristics affect the use of preventive care. Spousal education, income, health conditions, and preferences for risk and health care all affected use of preventive care in expected directions. Taken together, we conclude that marriage increases the use of preventive care; however, the net marital effect masks a heterogeneity that results from the characteristics of the spouse an individual marries. [ABSTRACT FROM AUTHOR]

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders ( p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership ( p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work. [ABSTRACT FROM AUTHOR]

Treatment effects, especially when comparing two or more therapeutic alternatives as in comparative effectiveness research, are likely to be heterogeneous across age, gender, co-morbidities and co-medications. Propensity scores ( PSs), an alternative to multivariable outcome models to control for measured confounding, have specific advantages in the presence of heterogeneous treatment effects. Implementing PSs using matching or weighting allows us to estimate different overall treatment effects in differently defined populations. Heterogeneous treatment effects can also be due to unmeasured confounding concentrated in those treated contrary to prediction. Sensitivity analyses based on PSs can help to assess such unmeasured confounding. PSs should be considered a primary or secondary analytic strategy in nonexperimental medical research, including pharmacoepidemiology and nonexperimental comparative effectiveness research. [ABSTRACT FROM AUTHOR]

Previous studies have provided conflicting evidence on whether being a family caregiver is associated with increased or decreased risk for all-cause mortality. This study examined whether 3,503 family caregivers enrolled in the national Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study showed differences in all-cause mortality from 2003 to 2012 compared with a propensity-matched sample of noncaregivers. Caregivers were individually matched with 3,503 noncaregivers by using a propensity score matching procedure based on 15 demographic, health history, and health behavior covariates. During an average 6-year follow-up period, 264 (7.5%) of the caregivers died, which was significantly fewer than the 315 (9.0%) matched noncaregivers who died during the same period. A proportional hazards model indicated that caregivers had an 18% reduced rate of death compared with noncaregivers (hazard ratio = 0.823, 95% confidence interval: 0.699, 0.969). Subgroup analyses by race, sex, caregiving relationship, and caregiving strain failed to identify any subgroups with increased rates of death compared with matched noncaregivers. Public policy and discourse should recognize that providing care to a family member with a chronic illness or disability is not associated with increased risk of death in most cases, but may instead be associated with modest survival benefits for the caregivers. [ABSTRACT FROM PUBLISHER]

ABSTRACT Purpose It is increasingly necessary to analyze data from multiple sources when conducting public health safety surveillance or comparative effectiveness research. However, security, privacy, proprietary, and legal concerns often reduce data holders' willingness to share highly granular information. We describe and compare two approaches that do not require sharing of patient-level information to adjust for confounding in multi-site studies. Methods We estimated the risks of angioedema associated with angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and aliskiren in comparison with beta-blockers within Mini-Sentinel, which has created a distributed data system of 18 health plans. To obtain the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs), we performed (i) a propensity score-stratified case-centered logistic regression analysis, a method identical to a stratified Cox regression analysis but needing only aggregated risk set data, and (ii) an inverse variance-weighted meta-analysis, which requires only the site-specific HR and variance. We also performed simulations to further compare the two methods. Results Compared with beta-blockers, the adjusted HR was 3.04 (95% CI: 2.81, 3.27) for ACEIs, 1.16 (1.00, 1.34) for ARBs, and 2.85 (1.34, 6.04) for aliskiren in the case-centered analysis. The corresponding HRs were 2.98 (2.76, 3.21), 1.15 (1.00, 1.33), and 2.86 (1.35, 6.04) in the meta-analysis. Simulations suggested that the two methods may produce different results under certain analytic scenarios. Conclusion The case-centered analysis and the meta-analysis produced similar results without the need to share patient-level data across sites in our empirical study, but may provide different results in other study settings. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Parsimony is important for the interpretation of causal effect estimates of longitudinal treatments on subsequent outcomes. One method for parsimonious estimates fits marginal structural models by using inverse propensity scores as weights. This method leads to generally large variability that is uncommon in more likelihood-based approaches. A more recent method fits these models by using simulations from a fitted g-computation, but requires the modeling of high-dimensional longitudinal relations that are highly susceptible to misspecification. We propose a new method that, first, uses longitudinal propensity scores as regressors to reduce the dimension of the problem and then uses the approximate likelihood for the first estimates to fit parsimonious models. We demonstrate the methods by estimating the effect of anticoagulant therapy on survival for cancer and non-cancer patients who have inferior vena cava filters. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective To assess the covariate balancing properties of propensity score-based algorithms in which covariates affecting treatment choice are both measured and unmeasured. Data Sources/Study Setting A simulation model of treatment choice and outcome. Study Design Simulation. Data Collection/Extraction Methods Eight simulation scenarios varied with the values placed on measured and unmeasured covariates and the strength of the relationships between the measured and unmeasured covariates. The balance of both measured and unmeasured covariates was compared across patients either grouped or reweighted by propensity scores methods. Principal Findings Propensity score algorithms require unmeasured covariate variation that is unrelated to measured covariates, and they exacerbate the imbalance in this variation between treated and untreated patients relative to the full unweighted sample. Conclusions The balance of measured covariates between treated and untreated patients has opposite implications for unmeasured covariates in randomized and observational studies. Measured covariate balance between treated and untreated patients in randomized studies reinforces the notion that all covariates are balanced. In contrast, forced balance of measured covariates using propensity score methods in observational studies exacerbates the imbalance in the independent portion of the variation in the unmeasured covariates, which can be likened to squeezing a balloon. If the unmeasured covariates affecting treatment choice are confounders, propensity score methods can exacerbate the bias in treatment effect estimates. [ABSTRACT FROM AUTHOR]

The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of latent class membership. In this article, 2 propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix. [ABSTRACT FROM AUTHOR]

ABSTRACT Purpose It is often preferable to simplify the estimation of treatment effects on multiple outcomes by using a single propensity score (PS) model. Variable selection in PS models impacts the efficiency and validity of treatment effects. However, the impact of different variable selection strategies on the estimated treatment effects in settings involving multiple outcomes is not well understood. The authors use simulations to evaluate the impact of different variable selection strategies on the bias and precision of effect estimates to provide insight into the performance of various PS models in settings with multiple outcomes. Methods Simulated studies consisted of dichotomous treatment, two Poisson outcomes, and eight standard-normal covariates. Covariates were selected for the PS models based on their effects on treatment, a specific outcome, or both outcomes. The PSs were implemented using stratification, matching, and weighting (inverse probability treatment weighting). Results PS models including only covariates affecting a specific outcome (outcome-specific models) resulted in the most efficient effect estimates. The PS model that only included covariates affecting either outcome (generic-outcome model) performed best among the models that simultaneously controlled measured confounding for both outcomes. Similar patterns were observed over the range of parameter values assessed and all PS implementation methods. Conclusions A single, generic-outcome model performed well compared with separate outcome-specific models in most scenarios considered. The results emphasize the benefit of using prior knowledge to identify covariates that affect the outcome when constructing PS models and support the potential to use a single, generic-outcome PS model when multiple outcomes are being examined. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article develops semiparametric approaches for estimation of propensity scores and causal survival functions from prevalent survival data. The analytical problem arises when the prevalent sampling is adopted for collecting failure times and, as a result, the covariates are incompletely observed due to their association with failure time. The proposed procedure for estimating propensity scores shares interesting features similar to the likelihood formulation in case-control study, but in our case it requires additional consideration in the intercept term. The result shows that the corrected propensity scores in logistic regression setting can be obtained through standard estimation procedure with specific adjustments on the intercept term. For causal estimation, two different types of missing sources are encountered in our model: one can be explained by potential outcome framework; the other is caused by the prevalent sampling scheme. Statistical analysis without adjusting bias from both sources of missingness will lead to biased results in causal inference. The proposed methods were partly motivated by and applied to the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data for women diagnosed with breast cancer. [ABSTRACT FROM AUTHOR]

ABSTRACT Background A correctly specified propensity score (PS) estimated in a cohort ('cohort PS') should, in expectation, remain valid in a subgroup population. Objective We sought to determine whether using a cohort PS can be validly applied to subgroup analyses and, thus, add efficiency to studies with many subgroups or restricted data. Methods In each of three cohort studies, we estimated a cohort PS, defined five subgroups, and then estimated subgroup-specific PSs. We compared difference in treatment effect estimates for subgroup analyses adjusted by cohort PSs versus subgroup-specific PSs. Then, over 10 million times, we simulated a population with known characteristics of confounding, subgroup size, treatment interactions, and treatment effect and again assessed difference in point estimates. Results We observed that point estimates in most subgroups were substantially similar with the two methods of adjustment. In simulations, the effect estimates differed by a median of 3.4% (interquartile (IQ) range 1.3-10.0%). The IQ range exceeded 10% only in cases where the subgroup had < 1000 patients or few outcome events. Conclusions Our empirical and simulation results indicated that using a cohort PS in subgroup analyses was a feasible approach, particularly in larger subgroups. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Community colleges have increased post-secondary educational access for disadvantaged youth, but it is unknown how community college degrees fit into the educational gradient of health status disparities. Using data from high school graduates in the National Longitudinal Study of Adolescent Health, we compared young adults ages 26-31 whose highest degrees were high school diplomas ( n = 5,584), sub-baccalaureate credentials (sub-BAs include community college certificates and associate's degrees) ( n = 2,415), and baccalaureate degrees (BAs) ( n = 3,303) on measures of hypertension, obesity, smoking, sleep problems, dyslipidemia, and depression. Comparisons used multivariate Poisson regression with robust standard errors after exact and nearest-neighbor Mahalanobis matching within propensity score calipers on 23 baseline factors measured in 1995. High school graduates and sub-BAs differed significantly on 3 of 23 baseline factors. After matching, sub-BAs were 16 % less likely to smoke daily than if they had only a high school diploma but did not differ in other health status measures. Sub-BAs and BAs differed significantly on 14 of 23 baseline factors. After matching, BAs were 60 % less likely to smoke daily, 14 % less likely to be obese, and 38 % less likely to have been diagnosed with depression. Sub-BA degrees are accessible to high school graduates irrespective of academic backgrounds and predict lower smoking prevalence. BAs are less accessible to high school graduates and predict lower chances of smoking, depression, and obesity. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Among the large number of cohort studies that employ propensity score matching, most match patients 1:1. Increasing the matching ratio is thought to improve precision but may come with a trade-off with respect to bias. Objective To evaluate several methods of propensity score matching in cohort studies through simulation and empirical analyses. Methods We simulated cohorts of 20 000 patients with exposure prevalence of 10%-50%. We simulated five dichotomous and five continuous confounders. We estimated propensity scores and matched using digit-based greedy ('greedy'), pairwise nearest neighbor within a caliper ('nearest neighbor'), and a nearest neighbor approach that sought to balance the scores of the comparison patient above and below that of the treated patient ('balanced nearest neighbor'). We matched at both fixed and variable matching ratios and also evaluated sequential and parallel schemes for the order of formation of 1: n match groups. We then applied this same approach to two cohorts of patients drawn from administrative claims data. Results Increasing the match ratio beyond 1:1 generally resulted in somewhat higher bias. It also resulted in lower variance with variable ratio matching but higher variance with fixed. The parallel approach generally resulted in higher mean squared error but lower bias than the sequential approach. Variable ratio, parallel, balanced nearest neighbor matching generally yielded the lowest bias and mean squared error. Conclusions 1: n matching can be used to increase precision in cohort studies. We recommend a variable ratio, parallel, balanced 1: n, nearest neighbor approach that increases precision over 1:1 matching at a small cost in bias. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Treatment effect in an observational study of relatively large scale can be described as a mixture of effects among subgroups. In particular, analysis for estimating the treatment effect at the level of an entire sample potentially involves not only differential effects across subgroups of the entire study cohort, but also differential propensities – probabilities of receiving treatment given study subjects’ pretreatment history. Such complex heterogeneity is of great research interest because the analysis of treatment effects can substantially depend on the hidden data structure for effect sizes and propensities. To uncover the unseen data structure, we propose a likelihood-based regression tree method which we call marginal tree (MT). The MT method is aimed at a simultaneous assessment of differential effects and propensity scores so that both become homogeneous within each terminal node of the resultant tree structure. We assess simulation performances of the MT method by comparing it with other existing tree methods and illustrate its use with a simulated data set, where the objective is to assess the effects of dieting behavior on its subsequent emotional distress among adolescent girls. [ABSTRACT FROM AUTHOR]

Background: Propensity score (PS) methods are increasingly used, even when sample sizes are small or treatments are seldom used. However, the relative performance of the two mainly recommended PS methods, namely PS-matching or inverse probability of treatment weighting (IPTW), have not been studied in the context of small sample sizes. Methods: We conducted a series of Monte Carlo simulations to evaluate the influence of sample size, prevalence of treatment exposure, and strength of the association between the variables and the outcome and/or the treatment exposure, on the performance of these two methods. Results: Decreasing the sample size from 1,000 to 40 subjects did not substantially alter the Type I error rate, and led to relative biases below 10%. The IPTW method performed better than the PS-matching down to 60 subjects. When N was set at 40, the PS matching estimators were either similarly or even less biased than the IPTW estimators. Including variables unrelated to the exposure but related to the outcome in the PS model decreased the bias and the variance as compared to models omitting such variables. Excluding the true confounder from the PS model resulted, whatever the method used, in a significantly biased estimation of treatment effect. These results were illustrated in a real dataset. Conclusion: Even in case of small study samples or low prevalence of treatment, PS-matching and IPTW can yield correct estimations of treatment effect unless the true confounders and the variables related only to the outcome are not included in the PS model. [ABSTRACT FROM AUTHOR]

Background: A small but growing body of research evidence suggests that place-based police interventions generate significant crime control gains. While place-based policing strategies have been adopted by a majority of U.S. police departments, very few agencies make a priori commitments to rigorous evaluations. Objective: Recent methodological developments were applied to conduct a rigorous ex post facto evaluation of the Boston Police Department’s Safe Street Team (SST) hot spots policing program. Research Design: A nonrandomized quasi-experimental design was used to evaluate the violent crime control benefits of the SST program at treated street segments and intersections relative to untreated street segments and intersections. Propensity score matching techniques were used to identify comparison places in Boston. Growth curve regression models were used to analyze violent crime trends at treatment places relative to control places. Units of Analysis: Using computerized mapping and database software, a micro-level place database of violent index crimes at all street segments and intersections in Boston was created. Measures: Yearly counts of violent index crimes between 2000 and 2009 at the treatment and comparison street segments and intersections served as the key outcome measure. Results: The SST program was associated with a statistically significant reduction in violent index crimes at the treatment places relative to the comparison places without displacing crime into proximate areas. Conclusions: To overcome the challenges of evaluation in real-world settings, evaluators need to continuously develop innovative approaches that take advantage of new theoretical and methodological approaches. [ABSTRACT FROM PUBLISHER]

Researchers investigated the impact that a first-year college experience course had on students' first-year grade point averages (GPAs) and retention rates. A sample of 109 first-year students enrolled in the course was compared to a sample of 326 students from the same university who had not taken the course. The goals of the experience course were to reduce attrition, increase grade point averages, and enhance academic skills. Without accounting for selection bias, those who took the course had similar retention rates and lower GPAs than those who did not take the course. After matching on propensity scores, the negative effects of the program on GPA were nullified and those in the program were more likely to enroll for a second year. Although the benefits from the course were weak, the positive impact of the program was more apparent after accounting for individual differences. [ABSTRACT FROM AUTHOR]

Educational researchers frequently study the impact of treatments or interventions on educational outcomes. However, when observational or quasi-experimental data are used for such investigations, selection bias can adversely impact researchers' abilities to make causal inferences about treatment effects. One way to deal with selection bias is to use propensity score methods. The authors introduce educational researchers to the general principles underlying propensity score methods, describe 2 practical applications of these methods, and discuss their limitations. [ABSTRACT FROM AUTHOR]

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Prior research indicates that adolescent offenders transferred to adult court are more likely to recidivate than those retained in the juvenile system. The studies supporting this conclusion, however, are limited in addressing the issue of heterogeneity among transferred adolescents. This study estimates the effect of transfer on later crime using a sample of 654 serious juvenile offenders, 29% of whom were transferred. We use propensity score matching to reduce potential selection bias, and we partition the sample on legal characteristics to examine subgroup effects. We find an overall null effect of transfer on re-arrest, but evidence of differential effects of transfer for adolescents with different offending histories. These results suggest that evaluating the effects of transfer for all transferred adolescents together may lead to misguided policy conclusions. [ABSTRACT FROM AUTHOR]

Previously deported aliens are a group about which numerous claims are made but very few facts are known. Using data on male deportable aliens released from a local jail, the study sought to test the ubiquitous claim that they pose a high risk of recidivism. Using multiple measures of recidivism and propensity score weighting to account for preexisting group differences, the authors find consistent support for the assertion that previously deported aliens are a high recidivism risk. Relative to similarly situated deportable aliens with no record of deportation, previously deported aliens are more likely to be rearrested, to be rearrested more quickly, and to be rearrested more frequently in a 1-year follow-up period. [ABSTRACT FROM AUTHOR]

This study uses longitudinal official record data on adult offenders in The Netherlands ( n = 4,246) to compare recidivism after community service to that after short-term imprisonment. To account for possible bias due to selection of offenders into these types of sanctions, we control for a large set of confounding variables using a combined method of ‘matching by variable’ and ‘propensity score matching’. Our findings demonstrate that offenders recidivate significantly less after having performed community service compared to after having been imprisoned. This finding holds for both the short- and long-term. Furthermore, using the Rosenbaum bounds method, we show that the results are robust for hidden bias. [ABSTRACT FROM AUTHOR]

We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly specified model-based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model checking than with models based on the full covariates. Thus, our estimators can also be better when used in place of the regression on the full covariates. We use our methods to compare longitudinal treatments for type II diabetes mellitus. [ABSTRACT FROM AUTHOR]

Ethiopia has launched a pro-poor health services extension programme since 2003 to deliver preventive and basic curative health services to its inhabitants. Despite the massive support and recognition the programme has received, there has not been proper evaluation of its impact. This study has applied propensity score matching and regression adjustment techniques to evaluate the short-term and intermediate-term impacts of the programme on child and maternal health indicators in the programme villages. Empirical data for the study were collected from 3095 households from both programme and non-programme villages in rural Ethiopia. The estimated results indicate that the programme has significantly increased the proportion of children fully and individually vaccinated against tuberculosis, polio, diphtheria-pertussis-tetanus, and measles. The study finds heterogeneity in childhood immunisation coverage as a result of differences in terms of the number of health extension workers, in the quality of health posts and in terms of the educational achievement of mothers across programme villages. The proportions of children and women using insecticide-treated bednets for malaria protection are significantly larger in programme villages than in non-programme villages. The effect on preventive maternal care is rather limited. Whereas women in the programme villages appeared to make their first contact with a skilled health service provider significantly earlier during pregnancy, very little effect is detected on other prenatal and postnatal care services. Moreover, the programme has not reduced the incidence and duration of diarrhoea and cough diseases among under-five children. [ABSTRACT FROM AUTHOR]

Adjustment methods such as propensity scores and analysis of covariance are often used for estimating treatment effects in nonexperimental data. Shadish, Clark, and Steiner used a within-study comparison to test how well these adjustments work in practice. They randomly assigned participating students to a randomized or nonrandomized experiment. Treatment effects were then estimated in the experiment and compared to the adjusted nonexperimental estimates. Most of the selection bias in the nonexperiment was reduced. The present study replicates the study of Shadish et al. despite some differences in design and in the size and direction of the initial bias. The results show that the selection of covariates matters considerably for bias reduction in nonexperiments but that the choice of analysis matters less. [ABSTRACT FROM AUTHOR]

Using data from the Netherlands-based Criminal Career and Life-course Study the effect of first-time imprisonment between age 18–38 on the conviction rates in the 3 years immediately following the year of the imprisonment was examined. Unadjusted comparisons of those imprisoned and those not imprisoned will be biased because imprisonment is not meted out randomly. Selection processes will tend to make the imprisoned group disproportionately crime prone compared to the not imprisoned group. In this study group-based trajectory modeling was combined with risk set matching to balance a variety of measurable indicators of criminal propensity. Findings indicate that first-time imprisonment is associated with an increase in criminal activity in the 3 years following release. The effect of imprisonment is similar across offence types. [ABSTRACT FROM AUTHOR]