Week 3 Educational ProductBY YANA PUCKETT, MD

Introduction

Comparative Effectiveness Research

Multilevel Data in Outcomes Research

Investigating Change Over Time

Estimating Effect of Intervention from Observational Data

Comparative Effectiveness Research

Comparative effectiveness research (CER) is the direct comparison of existing healthcare interventions to determine which work best for which patients and which pose the greatest benefits and harms, and which are cost effective.

A defining objective of CER is to provide information to help patients, consumers, clinicians, and payers make more informed clinical and health policy decisions.

Comparing two different treatments, technologies, pharmacologic drugs on their effectiveness.

Highly needed in this age of evidence-based medicine.

The American Recovery and Reinvestment Act of 2009 allocated a $1.1 billion “down payment” to support comparative effectiveness research (CER) (4).

Comparative Effectiveness Research and RCTs

RCTs, while great, are becoming extremely difficult to approve, design, and carry out.

RCTs take years to complete and very few of them while clinical comparative questions continue to arise.

Medicine is evolving, new technology is built quickly and RCTs have no way of keeping up with that.

Funding is limited and RCTs are extremely expensive to carry out.

RCTS often exclude patients on strict parameters, thus diminishing application of findings/results to the population that is targeted.

Bayesian Statistics may be the solution (4).

References

1. Risk Adjustment for Measuring Health Care Outcomes, 4th Ed. By Iezzoni, L (Ed.) Publisher: Health Administration Press ISBN: 9781567934373.

2. Cho (2003). Using multilevel analysis in patient and organizational outcomes research. Nursing Research, 52(1), 61-65.

3. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press. pp. 3-15 in Singer & Willet (2003).

4. Luce B, Kramer J, Schwartz J, et al. Rethinking Randomized Clinical Trials for Comparative Effectiveness Research: The Need for Transformational Change. Annals Of Internal Medicine[serial online]. August 4, 2009;151(3):206-W.45. Available from: Academic Search Complete, Ipswich, MA. Accessed June 17, 2015.

Multilevel Data in Outcomes Research

Randomized Controlled Trials (RCTs)not always feasible or practical.

RCTs expensive and require years to complete.

Most clinical questions and health outcomes assessed through observational data.

Multivariable model accounts for various baseline differences in risk and confounders.

Has become extremely popular in research.

Multilevel Analysis (Hierarchical Modeling)

Analytic model that measures variables at different levels of hierarchy.

Helpful for comparing patient outcomes across hospitals because can adjust for risk without manipulating risk factors at hospital level.

Allows simultaneous examination of group-level and individual level variables over individual level outcome.

Multivariable Models for Estimating Effects of Interventions

Continuous Outcomes: estimates effect of an intervention on a continuous outcome via linear regression. Ex: estimating effect of enrolling in an MCO and how it influences a persons’ health care expenditures over a year.

Dichotomous Outcomes: uses logistic regression to assess treatment effectiveness. Ex: being alive 30 days after hospital admission.

Time to Event Outcomes: Death is usually the outcome assessed, survival modeling, proportional hazards modeling or Kaplan-Meyer Statistics. Ex: Cancer treatment and survival outcomes.

Investigating Change Over Time

Requires: Good multilevel longitudinal data that describes how something

changes over time.

Sensible metric for time that is reliable and valid.

Continuous outcome that changes systematically over time such as test scores, self-assessments, psychological measurements.

Propensity Score Adjustment

A propensity score is the probability of a unit being assigned to a particular treatment given a set of observed covariates.

Statistical analysis of observational data that accounts for confounders when comparing treatment results.

Attempts to reduce bias due to confounding variables that could be found by simply comparing outcomes among units.

Attempts to mimic randomization by creating a sample of units that received the treatment that is comparable on all observed covariates.

Decreases selection bias.

Estimating Effect of Intervention from Observational Data

In randomized studies, association=causation, but can we say the same for observational data? Generally not.

Two analytical approaches to compute causal effects from observational data: standardisation and inverse probability weighting.

Standardisation: There are two methods of standardisation, direct and indirect. Standardisation allows a single index of comparative mortality to be derived, in a way that permits comparison of mortality measures that are free of the effects of the underlying age distributions of the populations under observation.

Inverse Probability Weighting: statistical technique for calculating statistics standardized to a population different from that in which the data was collected. Ex: study designs with a disparate sampling population and population of target inference (target population) are common in application.

Bayesian Statistics

Use has been very popular in recent years (4).

Early-phase cancer trials are commonly performed using Bayesian designs (4).

Statistical modeling that deals basically determines the likelihood of something happening based on probabilities given by a set of data points.