Melissa M. Garrido

Methods for Constructing and Assessing Propensity Scores

OBJECTIVES To model the steps involved in preparing for and carrying out propensity score analyses by providing step-by-step guidance and Stata code applied to an empirical dataset. STUDY DESIGN Guidance, Stata code, and empirical examples are given to illustrate (1) the process of choosing variables to include in the propensity score; (2) balance of propensity score across treatment and comparison groups; (3) balance of covariates across treatment and comparison groups within blocks of the propensity score; (4) choice of matching and weighting strategies; (5) balance of covariates after matching or weighting the sample; and (6) interpretation of treatment effect estimates. EMPIRICAL APPLICATION We use data from the Palliative Care for Cancer Patients (PC4C) study, a multisite observational study of the effect of inpatient palliative care on patient health outcomes and health services use, to illustrate the development and use of a propensity score. CONCLUSIONS Propensity scores are one useful tool for accounting for observed differences between treated and comparison groups. Careful testing of propensity scores is required before using them to estimate treatment effects.

Prospective Cohort Study of Hospital Palliative Care Teams for Inpatients With Advanced Cancer: Earlier Consultation Is Associated With Larger Cost-Saving Effect

PURPOSE Previous studies report that early palliative care is associated with clinical benefits, but there is limited evidence on economic impact. This article addresses the research question: Does timing of palliative care have an impact on its effect on cost? PATIENTS AND METHODS Using a prospective, observational design, clinical and cost data were collected for adult patients with an advanced cancer diagnosis admitted to five US hospitals from 2007 to 2011. The sample for economic evaluation was 969 patients; 256 were seen by a palliative care consultation team, and 713 received usual care only. Subsamples were created according to time to consult after admission. Propensity score weights were calculated, matching the treatment and comparison arms specific to each subsample on observed confounders. Generalized linear models with a γ distribution and a log link were applied to estimate the mean treatment effect on cost within subsamples. RESULTS Earlier consultation is associated with a larger effect on total direct cost. Intervention within 6 days is estimated to reduce costs by -

Choosing Models for Health Care Cost Analyses: Issues of Nonlinearity and Endogeneity

1,312 (95% CI, -

The end-of-life experience: modifiable predictors of caregivers' bereavement adjustment.

2,568 to -

End-of-life treatment preferences: A key to reducing ethnic/racial disparities in advance care planning?

56; P = .04) compared with no intervention and intervention within 2 days by -

Pathways From Religion to Advance Care Planning: Beliefs About Control Over Length of Life and End-of-Life Values

2,280 (95% CI, -

Propensity Scores: A Practical Method for Assessing Treatment Effects in Pain and Symptom Management Research

3,438 to -

Cost analysis of a prospective multi-site cohort study of palliative care consultation teams for adults with advanced cancer: Where do cost-savings come from?:

1,122; P < .001); these reductions are equivalent to a 14% and a 24% reduction, respectively, in cost of hospital stay. CONCLUSION Earlier palliative care consultation during hospital admission is associated with lower cost of hospital stay for patients admitted with an advanced cancer diagnosis. These findings are consistent with a growing body of research on quality and survival suggesting that early palliative care should be more widely implemented.

Association Between Hospice Use and Depressive Symptoms in Surviving Spouses

OBJECTIVE To compare methods of analyzing endogenous treatment effect models for nonlinear outcomes and illustrate the impact of model specification on estimates of treatment effects such as health care costs. DATA SOURCES Secondary data on cost and utilization for inpatients hospitalized in five Veterans Affairs acute care facilities in 2005-2006. STUDY DESIGN We compare results from analyses with full information maximum simulated likelihood (FIMSL); control function (CF) approaches employing different types and functional forms for the residuals, including the special case of two-stage residual inclusion; and two-stage least squares (2SLS). As an example, we examine the effect of an inpatient palliative care (PC) consultation on direct costs of care per day. DATA COLLECTION/EXTRACTION METHODS We analyzed data for 3,389 inpatients with one or more life-limiting diseases. PRINCIPAL FINDINGS The distribution of average treatment effects on the treated and local average treatment effects of a PC consultation depended on model specification. CF and FIMSL estimates were more similar to each other than to 2SLS estimates. CF estimates were sensitive to choice and functional form of residual. CONCLUSIONS When modeling cost or other nonlinear data with endogeneity, one should be aware of the impact of model specification and treatment effect choice on results.

Benzodiazepine and Sedative-hypnotic Use Among Older Seriously Ill Veterans: Choosing Wisely?

The objective of the current study was to determine the best set of predictors of psychological disorders, regrets, health‐related quality of life, and mental health function among bereaved caregivers of patients with cancer, thereby identifying promising targets for interventions to improve bereavement adjustment.