Health services research methods

The validity of ecological studies in epidemiology for inferring causal relationships has been widely challenged as observed associations could be biased by the Ecological Fallacy. We reconsider the important design components of ecological studies, and discuss the conditions that may lead to spurious associations. Ecological associations are useful and valid when the ecological exposures can be interpreted as Instrumental Variables. A suitable example may be a time series analysis of environmental pollution (e...

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution...

We explore the 'reassessment' design in a logistic regression setting, where a second wave of sampling is applied to recover a portion of the missing data on a binary exposure and/or outcome variable. We construct a joint likelihood function based on the original model of interest and a model for the missing data mechanism, with emphasis on non-ignorable missingness. The estimation is carried out by numerical maximization of the joint likelihood function with close approximation of the accompanying Hessian matrix, using sharable programs that take advantage of general optimization routines in standard software...

Evaluation of intervention effects on multiple outcomes is a common scenario in clinical studies. In longitudinal studies, such evaluation is a challenge if one wishes to adequately capture simultaneous data behavior. In this situation, a common approach is to analyze each outcome separately. As a result, multiple statistical statements describing the intervention effect need to be reported and an adjustment for multiple testing is necessary. This is typically done by means of the Bonferroni procedure, which does not take into account the correlation between outcomes, thus resulting in overly conservative conclusions...

OBJECTIVES: Heterogeneity of effect measures in intervention studies undermines the use of evidence to inform policy. Our objective was to develop a comprehensive algorithm to convert all types of effect measures to one standard metric, relative risk reduction (RRR). STUDY DESIGN AND SETTING: This work was conducted to facilitate synthesis of published intervention effects for our epidemic modeling of the health impact of human immunodeficiency virus [HIV testing and counseling (HTC)]...

Cluster randomized trials randomly assign groups of individuals to examine research questions or test interventions and measure their effects on individuals. Recent emphasis on quality improvement, comparative effectiveness, and learning health systems has prompted expanded use of pragmatic cluster randomized trials in routine health-care settings, which in turn poses practical and ethical challenges that current oversight frameworks may not adequately address. The 2012 Ottawa Statement provides a basis for considering many issues related to pragmatic cluster randomized trials but challenges remain, including some arising from the current US research and health-care regulations...

Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical random-effects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases...

No abstract text is available yet for this article.

OBJECTIVES: We used elective total joint replacement (TJR) as a case study to demonstrate selection bias toward offering this procedure to younger and healthier patients. STUDY DESIGN AND SETTING: Longitudinal data from 2,202 men were integrated with hospital data and mortality records. Study participants were followed from recruitment (1996-1999) until TJR, death, or 2007 (end of follow-up). A propensity score (PS) was constructed to quantify each subject's likelihood of undergoing TJR...

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)...

OBJECTIVE: To develop and test incident drug user designs for assessing cost savings from statin use in diabetics. DATA SOURCE: Random 5 percent sample of Medicare beneficiaries, 2006-2008. STUDY DESIGN: Seven-step incident user design to assess impact of statin initiation on subsequent Medicare spending: (1) unadjusted pre/post initiation test; (2) unadjusted difference-in-difference (DID) with comparison series; (3) adjusted DID; (4) propensity score (PS)-matched DID with static and dynamic baseline covariates; (5) PS-matched DID by drug adherence strata; (6) PS-matched DID for high adherers controlling for healthy adherer bias; and (7) replication for ACE-inhibitor/ARB initiators...

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...

OBJECTIVE: To demonstrate how a researcher can investigate the appropriateness of a published comorbidity summary measure for use with a given sample. DATA SOURCE: Surveillance, Epidemiology, and End Results linked to Medicare claims data. STUDY DESIGN: We examined Kaplan-Meier estimated survival curves for four diseases within strata of a comorbidity summary measure, the Charlson Comorbidity Index. DATA COLLECTION: We identified individuals with early-stage kidney cancer diagnosed from 1995 to 2009...

No abstract text is available yet for this article.

OBJECTIVES: Regression discontinuity (RD) designs allow for rigorous causal inference when patients receive a treatment based on scoring above or below a cutoff point on a continuously measured variable. We provide an introduction to the theory of RD and a systematic review and assessment of the RD literature in medicine, epidemiology, and public health. STUDY DESIGN AND SETTING: We review the necessary conditions for valid RD results, provide a practical guide to RD implementation, compare RD to other methodologies, and conduct a systematic review of the RD literature in PubMed...

Randomised control trials (RCT) are considered the gold standard for the strength of evidence used in formulation of guidelines in the contemporary clinical practice. The analysis is based on the design of an experiment that analyses data collected in a future event space with no regard to past observations. It bases its conclusions on arbitrary values of significance that might not be clinically relevant or plausible. A Bayesian analysis looks at all the past evidence and observations to reach a conclusion and does not have the shortcomings of a RCT...

This commentary clarifies our original commentary (Bell and Jones, 2014c) and illustrates some concerns we have regarding the response article in this issue (Reither et al., 2015). In particular, we argue that (a) linear effects do not have to be produced by exact linear mathematical functions to behave as if they were linear, (b) linear effects by this wider definition are extremely common in real life social processes, and (c) in the presence of these effects, the Hierarchical Age Period Cohort (HAPC) model will often not work...

This paper deals with the optimal sample sizes for a multicentre trial in which the cost-effectiveness of two treatments in terms of net monetary benefit is studied. A bivariate random-effects model, with the treatment-by-centre interaction effect being random and the main effect of centres fixed or random, is assumed to describe both costs and effects. The optimal sample sizes concern the number of centres and the number of individuals per centre in each of the treatment conditions. These numbers maximize the efficiency or power for given research costs or minimize the research costs at a desired level of efficiency or power...

The time is right for the use of Bayesian Adaptive Designs (BAD) in comparative effectiveness trials. For example, Patient Centered Outcomes Research Institute has joined the Food and Drug Administration and National Intitutes of Health in adopting policies/guidelines encouraging their use. There are multiple aspects to BAD that need to be considered when designing a comparative effectiveness design. First, the adaptation rules can determine the expected size of the trial. Second, a utility function can be used to combine extremely important co-endpoints (e...

Structural equation models (SEMs) are widely recognized as the most important statistical tool for assessing the interrelationships among latent variables. This study develops a Bayesian adaptive group least absolute shrinkage and selection operator procedure to perform simultaneous model selection and estimation for semiparametric SEMs, wherein the structural equation is formulated using the additive nonparametric functions of observed and latent variables. We propose the use of basis expansions to approximate the unknown functions...