A Review of Disease Risk Scores and Their Application in Pharmacoepidemiology

Adaptability of High Dimensional Propensity Score Procedure in the Transition from ICD-9 to ICD-10 in the US Healthcare System

Background

High-Dimensional Propensity Score procedure (HDPS) is a data-driven approach to assist control for confounding in pharmacoepidemiologic research. The transition to the International Classification of Disease (ICD-9/10) in the US health system may pose uncertainty in applying the HDPS procedure.

Methods

We assembled a base cohort of patients in MarketScan^® Commercial Claims Database who had newly initiated celecoxib or traditional NSAIDs to compare gastrointestinal bleeding risk. We then created bootstrapped hypothetical cohorts from the base cohort with predefined patient selection patterns from the ICD eras. Three strategies for HDPS deployment were tested: 1) split the cohort by ICD era, deploy HDPS twice, and pool the relative risks (pooled RR), 2) consider codes from each ICD era as a separate data dimension and deploy HDPS in the entire cohort (data dimensions) and 3) map ICD codes from both eras to Clinical Classifications Software (CCS) concepts before deploying HDPS in the entire cohort (CCS mapping). We calculated percent bias and root-mean-squared error to compare the strategies.

Results

A similar bias reduction was observed in cohorts where patient selection pattern from each ICD era was comparable between the exposure groups. In the presence of considerable disparity in patient selection, we observed a bimodal distribution of propensity scores in the data dimensions strategy, indicating instrument-like covariates. Moreover, the CCS mapping strategy resulted in at least 30% less bias than pooled RR and data dimensions strategies (RMSE: 0.14, 0.19, 0.21, respectively) in this scenario.

Conclusion

Mapping ICD codes to a stable terminology like CCS serves as a helpful strategy to reduce residual bias when deploying HDPS in pharmacoepidemiologic studies spanning both ICD eras.

A likely responder approach for the analysis of randomized controlled trials

OBJECTIVE

To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T.

METHODS

Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder.

RESULTS

A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R.

CONCLUSION

The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.

Practical recommendations on double score matching for estimating causal effects

Unlike in randomized clinical trials (RCTs), confounding control is critical for estimating the causal effects from observational studies due to the lack of treatment randomization. Under the unconfoundedness assumption, matching methods are popular because they can be used to emulate an RCT that is hidden in the observational study. To ensure the key assumption hold, the effort is often made to collect a large number of possible confounders, rendering dimension reduction imperative in matching. Three matching schemes based on the propensity score (PSM), prognostic score (PGM), and double score (DSM, ie, the collection of the first two scores) have been proposed in the literature. However, a comprehensive comparison is lacking among the three matching schemes and has not made inroads into the best practices including variable selection, choice of caliper, and replacement. In this article, we explore the statistical and numerical properties of PSM, PGM, and DSM via extensive simulations. Our study supports that DSM performs favorably with, if not better than, the two single score matching in terms of bias and variance. In particular, DSM is doubly robust in the sense that the matching estimator is consistent requiring either the propensity score model or the prognostic score model is correctly specified. Variable selection on the propensity score model and matching with replacement is suggested for DSM, and we illustrate the recommendations with comprehensive simulation studies. An R package is available at https://github.com/Yunshu7/dsmatch.

Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores

Suppose that an investigator wants to estimate an association between a continuous exposure variable and an outcome, adjusting for a set of confounders. If the exposure variable suffers classical measurement error, in which the measured exposures are distributed with independent error around the true exposure, then an estimate of the covariate-adjusted exposure-outcome association may be biased. We propose an approach to estimate a marginal exposure-outcome association in the setting of classical exposure measurement error using a disease score-based approach to standardization to the exposed sample. First, we show that the proposed marginal estimate of the exposure-outcome association will suffer less bias due to classical measurement error than the covariate-conditional estimate of association when the covariates are predictors of exposure. Second, we show that if an exposure validation study is available with which to assess exposure measurement error, then the proposed marginal estimate of the exposure-outcome association can be corrected for measurement error more efficiently than the covariate-conditional estimate of association. We illustrate both of these points using simulations and an empirical example using data from the Orinda Longitudinal Study of Myopia (California, 1989-2001).

Marginal Structural Models for Risk or Prevalence Ratios for a Point Exposure Using a Disease Risk Score

The disease risk score is a summary score that can be used to control for confounding with a potentially large set of covariates. While less widely used than the exposure propensity score, the disease risk score approach might be useful for novel or unusual exposures, when treatment indications or exposure patterns are rapidly changing, or when more is known about the nature of how covariates cause disease than is known about factors influencing propensity for the exposure of interest. Focusing on the simple case of a binary point exposure, we describe a marginal structural model for estimation of risk (or prevalence) ratios. The proposed model incorporates the disease risk score as an offset in a regression model, and it yields an estimate of a standardized risk ratio where the target population is the exposed group. Simulations are used to illustrate the approach, and an empirical example is provided. Confounder control based on the proposed method might be a useful alternative to approaches based on the exposure propensity score, or as a complement to them.

Use of propensity score and disease risk score for multiple treatments with time-to-event outcome: a simulation study

Propensity score (PS) and disease risk score (DRS) are often used in pharmacoepidemiologic safety studies. Methods of applying these two balancing scores are extensively studied in binary treatment settings. However, the use of PS and DRS is not well understood in the case of non-ordinal multiple treatments. Some PS methods of multiple treatments have been implemented since the theoretical establishment. Nevertheless, most of the work applies to continuous or binary outcomes. Little work has been done for time-to-event outcomes. In this study, we extend the application of the PS and DRS methods to time-to-event outcomes in multiple treatment settings. The analytical approaches include weighing, matching, stratification, and regression. Simulation studies with rare event rates are conducted to evaluate the performances of different methods. Different treatment-covariates and outcome-covariates strength of associations are considered. Additionally, the impacts of imbalanced designs and large or limited PS overlaps are investigated on various analytical approaches. We found that the inverse probability treatment weighting with bootstrap variance estimator, the generalized PS matching, and the Cox regression estimated DRS in full cohort generally performed well in multiple treatment settings. This study aims to provide additional guidance for researchers on PS and DRS analyses in pharmacoepidemiologic observational studies.

Overachieving Municipalities in Public Health: A Machine-learning Approach

BACKGROUND

Identifying successful public health ideas and practices is a difficult challenge towing to the presence of complex baseline characteristics that can affect health outcomes. We propose the use of machine learning algorithms to predict life expectancy at birth, and then compare health-related characteristics of the under- and overachievers (i.e., municipalities that have a worse and better outcome than predicted, respectively).

METHODS

Our outcome was life expectancy at birth for Brazilian municipalities, and we used as predictors 60 local characteristics that are not directly controlled by public health officials (e.g., socioeconomic factors).

RESULTS

The highest predictive performance was achieved by an ensemble of machine learning algorithms (cross-validated mean squared error of 0.168), including a 35% gain in comparison with standard decision trees. Overachievers presented better results regarding primary health care, such as higher coverage of the massive multidisciplinary program Family Health Strategy. On the other hand, underachievers performed more cesarean deliveries and mammographies and had more life-support health equipment.

CONCLUSIONS

The findings suggest that analyzing the predicted value of a health outcome may bring insights about good public health practices.

Dronedarone, amiodarone and other antiarrhythmic drugs, and acute liver injuries: a case-referent study

BACKGROUND

Spontaneous reports of acute liver injuries (ALI) in patients taking dronedarone triggered an EMA alert in 2011. This study aimed to assess the risk of ALI for class III antiarrhythmic drugs controlling for the use of other potential ALI-inducing drugs.

METHODS

Between 2010 and 2014, consecutive ALI cases (≥50 years-old) were identified across Germany. ALI was defined as a new increase in at least one of the transaminases ≥3 times the upper limit of normal (ULN) or ≥2 ULN if alkaline phosphatase, with ("definite" case) or without ("biochemical" case) suggestive signs/symptoms of ALI, excluding other liver diseases. Recruited community controls were matched to cases on gender, age and inclusion date. Exposure to antiarrhythmic drugs and co-medication up to 2 years before ALI onset was informed by patients and confirmed by physicians' prescriptions. Adjusted Odds Ratios (aOR) were obtained from conditional multivariable logistic regressions, adjusted for a multivariate disease risk score and co-medication.

RESULTS

252 cases and 1081 matched controls were included (59.1% females; mean age: 64 years). Exposure to class III antiarrhythmic drugs was 4.0% in cases and 1.5% in controls, aOR = 3.6 (95% CI: 1.6-8.4). Associations with exposure to dronedarone and amiodarone were respectively 3.1 (95% CI: 0.7-14. 8) and 5.90 (1.7-20.0). Restricting the analysis to definite or severe ALI cases did not change these results.

CONCLUSIONS

Class III antiarrhythmic drugs were associated with ALI, amiodarone displaying the highest risk, and results were robust to case definitions. Continued vigilance is needed for patients taking these drugs.

Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network

PURPOSE OF REVIEW

An important component of the Food and Drug Administration's Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillance of medical products in the Sentinel Distributed Database. In this manuscript, we review literature relevant to the development of these programs and describe their application within the Sentinel Initiative.

RECENT FINDINGS

These quality-checked and publicly available tools have been successfully used to conduct rapid, replicable, and targeted safety analyses of several medical products. In addition to speed and reproducibility, use of semi-automated tools allows investigators to focus on decisions regarding key methodological parameters. We also identified challenges associated with the use of these methods in distributed and prospective datasets like the Sentinel Distributed Database, namely uncertainty regarding the optimal approach to estimating propensity scores in dynamic data among data partners of heterogeneous size.

SUMMARY

Future research should focus on the methodological challenges raised by these applications as well as developing new modular programs for targeted surveillance of medical products.