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PubMed2006

Instrumental variables: application and limitations.

To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of unfamiliarity or inapplicability. By introducing the method in a nontechnical way, we show that IV in a linear model is quite easy to understand and easy to apply once an appropriate instrumental variable has been identified. We also point out some limitations of the IV estimator when the instrumental variable is only weakly correlated with the exposure. The IV estimator will be imprecise (large standard error), biased when sample size is small, and biased in large samples when one of the assumptions is only slightly violated. For these reasons, it is advised to use an IV that is strongly correlated with exposure. However, we further show that under the assumptions required for the validity of the method, this correlation between IV and exposure is limited. Its maximum is low when confounding is strong, such as in case of confounding by indication. Finally, we show that in a study in which strong confounding is to be expected and an IV has been used that is moderately or strongly related to exposure, it is likely that the assumptions of IV are violated, resulting in a biased effect estimate. We conclude that instrumental variables can be useful in case of moderate confounding but are less useful when strong confounding exists, because strong instruments cannot be found and assumptions will be easily violated.

Bias↗

PubMed2000

An introduction to instrumental variables for epidemiologists.

Instrumental-variable (IV) methods were invented over 70 years ago, but remain uncommon in epidemiology. Over the past decade or so, non-parametric versions of IV methods have appeared that connect IV methods to causal and measurement-error models important in epidemiological applications. This paper provides an introduction to those developments, illustrated by an application of IV methods to non-parametric adjustment for non-compliance in randomized trials.

Bias↗

PubMed1993

Reducing attrition bias with an instrumental variable in a regression model: results from a panel of rheumatoid arthritis patients.

This study proposes an econometric technique to reduce attrition bias in panel data. In the simplest case, one estimates two regressions. The first is a probit regression based on sociodemographic and clinical characteristics measured at baseline. The probit regression estimates the probability that subjects stay or leave over the duration of the study. We insert the predicted probabilities from the probit regression into an inverse Mills ratio (IMR) or hazard rate to form an instrumental variable. We use this instrumental variable subsequently as an additional covariate in a second regression model that attempts to explain fluctuations in the dependent variable. The second regression, which is linear, includes only subjects who remained in the study. In alternative models, instrumental variables are created using predicted values from least squares and logit regressions estimating the probability that subjects stay or leave. The use of the instrumental variables reduces the effects of attrition bias in the linear regression model. We applied the technique to a panel of patients with rheumatoid arthritis (RA) enrolled in 1981 and followed through 1990. We attempted to predict values for a measure of functional disability recorded in 1990 with use of covariates measured in 1981. The dependent variable was an index of disability in 1990 and the independent variables (covariates) included the disability index from 1981, the years of duration of RA, gender, marital status, education, and age in 1981. The correction technique suggested that ignoring attrition bias would underestimate the strength of associations between being female and the subsequent disability index, and overestimate the strength of associations between being married spouse present, age, and the initial disability index on the one hand and the subsequent disability index on the other.

Age Factors↗

PubMed2006

Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable.

BACKGROUND: Postmarketing observational studies of the safety and effectiveness of prescription medications are critically important but fraught with methodological problems. The data sources available for such research often lack information on indications and other important confounders for the drug exposure under study. Instrumental variable methods have been proposed as a potential approach to control confounding by indication in nonexperimental studies of treatment effects; however, good instruments are hard to find. METHODS: We propose an instrument for use in pharmacoepidemiology that is based on a time-varying estimate of the prescribing physician's preference for one drug relative to a competing therapy. The use of this instrument is illustrated in a study comparing the effect of exposure to COX-2 inhibitors with nonselective, nonsteroidal antiinflammatory medications on gastrointestinal complications. RESULTS: Using conventional multivariable regression adjusting for 17 potential confounders, we found no protective effect due to COX-2 use within 120 days from the initial exposure (risk difference = -0.06 per 100 patients; 95% confidence interval = -0.26 to 0.14). However, the proposed instrumental variable method attributed a protective effect to COX-2 exposure (-1.31 per 100 patients; -2.42 to -0.20) compatible with randomized trial results (-0.65 per 100 patients; -1.08 to -0.22). CONCLUSIONS: The instrumental variable method that we have proposed appears to have substantially reduced the bias due to unobserved confounding. However, more work needs to be done to understand the sensitivity of this approach to possible violations of the instrumental variable assumptions.

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PubMed1998

Econometrics in outcomes research: the use of instrumental variables.

We describe an econometric technique, instrumental variables, that can be useful in estimating the effectiveness of clinical treatments in situations when a controlled trial has not or cannot be done. This technique relies upon the existence of one or more variables that induce substantial variation in the treatment variable but have no direct effect on the outcome variable of interest. We illustrate the use of the technique with an application to aggressive treatment of acute myocardial infarction in the elderly.

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PubMed2004

Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies.

Inferring causal effects from longitudinal repeated measures data has high relevance to a number of areas of research, including economics, social sciences and epidemiology. In observational studies in particular, the treatment receipt mechanism is typically not under the control of the investigator; it can depend on various factors, including the outcome of interest. This results in differential selection into treatment levels, and can lead to selection bias when standard routines such as least squares regression are used to estimate causal effects. Interestingly, both the characterization of and methodology for handling selection bias can differ substantially by disciplinary tradition. In social sciences and economics, instrumental variables (IV) is the standard method for estimating linear and nonlinear models in which the error term may be correlated with an observed covariate. When such correlation is not ruled out, the covariate is called endogenous and least squares estimates of the covariate effect are typically biased. The availability of an instrumental variable can be used to reduce or eliminate the bias. In public health and clinical medicine (e.g., epidemiology and biostatistics), selection bias is typically viewed in terms of confounders, and the prevailing methods are geared toward making proper adjustments via explicit use of observed confounders (e.g., stratification, standardization). A class of methods known as inverse probability weighting (IPW) estimators, which relies on modeling selection in terms of confounders, is gaining in popularity for making such adjustments. Our objective is to review and compare IPW and IV for estimating causal treatment effects from longitudinal data, where the treatment may vary with time. We accomplish this by defining the causal estimands in terms of a linear stochastic model of potential outcomes (counterfactuals). Our comparison includes a review of terminology typically used in discussions of causal inference (e.g., confounding, endogeneity); a review of assumptions required to identify causal effects and their implications for estimation and interpretation; description of estimation via inverse weighting and instrumental variables; and a comparative analysis of data from a longitudinal cohort study of HIV-infected women. In our discussion of assumptions and estimation routines, we try to emphasize sufficient conditions needed to implement relatively standard analyses that can essentially be formulated as regression models. In that sense this review is geared toward the quantitative practitioner. The objective of the data analysis is to estimate the causal (therapeutic) effect of receiving combination antiviral therapy on longitudinal CD4 cell counts, where receipt of therapy varies with time and depends on CD4 count and other covariates. Assumptions are reviewed in context, and resulting inferences are compared. The analysis illustrates the importance of considering the existence of unmeasured confounding and of checking for 'weak instruments.' It also suggests that IV methodology may have a role in longitudinal cohort studies where potential instrumental variables are available.

Antiretroviral Therapy, Highly Active↗

PubMed2006

Simultaneous assessment of short-term gastrointestinal benefits and cardiovascular risks of selective cyclooxygenase 2 inhibitors and nonselective nonsteroidal antiinflammatory drugs: an instrumental variable analysis.

OBJECTIVE: To simultaneously assess the short-term reduction in risk of gastrointestinal (GI) complications and increase in risk of acute myocardial infarction (MI) by celecoxib compared with rofecoxib and several nonselective nonsteroidal antiinflammatory drugs (NSAIDs) using instrumental variable analysis. METHODS: A population of 49,711 Medicare beneficiaries ages 65 years and older who initiated nonselective NSAID or selective cyclooxygenase 2 inhibitor therapy between January 1, 1999, and December 31, 2002, was identified. The increase in risk of GI complications and MI within 180 days after initiation of NSAID (rofecoxib, diclofenac, ibuprofen, and naproxen compared with celecoxib) therapy was assessed using instrumental variable analysis. RESULTS: Compared with nonselective NSAIDs, celecoxib reduced the risk of GI complications by 1.4 per 100 users but increased the risk of MI by 0.3 per 100 users. Rofecoxib decreased GI complications by 1.1 per 100 users and increased the risk of MI by 0.3 per 100 users. Using celecoxib as the reference exposure showed an increase in the MI risk for rofecoxib (risk difference [RD] 1.40, 95% confidence interval [95% CI] -0.20, 3.01) and diclofenac (RD 6.07, 95% CI -0.02, 12.15). The RD for naproxen as well as its upper 95% CI was the lowest of all NSAIDs (RD -0.30, 95% CI -2.74, 2.14) and there was no significant difference in GI complication rates among all NSAIDs. CONCLUSION: In this instrumental variable analysis, diclofenac and rofecoxib had the least favorable benefit-risk balance among NSAIDs in older adults.

Aged↗

PubMed1984

The effect of instrument variables on calculation factors for standardisation of colorimetric analyses.

A number of colorimetric methods, particularly enzyme activity assays, are usually standardised using calculation factors based on the molar absorptivity of a principle reactant or product. Such methods are subject to long-term variation. The relationship between long-term variation in results and instrument variables affecting calculation factors has not been quantitated. In this study, we have shown that, on a centrifugal analyser having a within-run coefficient of variation of less than 1%, instrument variables affecting calculation factor alone could result in changes in results of up to 8.5% over 75 days. We therefore advocate daily use of a solution of potassium dichromate to monitor instrument variables that can independently affect calculation factors and within-run imprecision. This procedure is useful for maintaining long-term performance and for differentiating problems of instrumental or chemical origin.

Autoanalysis↗

PubMed2006

Analysis with respect to instrumental variables for the exploration of microarray data structures.

BACKGROUND: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various factors structuring the data which should be taken into account. The objective of these experiments is the exploration of some given factors while controlling other factors. RESULTS: We present here a family of methods, the analyses with respect to instrumental variables, which can be easily applied to the particular case of microarray data. An illustrative example of analysis with instrumental variables is given in the case of microarray data investigating the effect of beverage intake on peripheral blood gene expression. This approach is compared to an ANOVA-based gene-by-gene statistical method. CONCLUSION: Instrumental variables analyses provide a simple way to control several sources of variation in a multivariate analysis of microarray data. Due to their flexibility, these methods can be associated with a large range of ordination techniques combined with one or several qualitative and/or quantitative descriptive variables.

Algorithms↗

PubMed2006

Evaluating disease management programme effectiveness: an introduction to instrumental variables.

This paper introduces the concept of instrumental variables (IVs) as a means of providing an unbiased estimate of treatment effects in evaluating disease management (DM) programme effectiveness. Model development is described using zip codes as the IV. Three diabetes DM outcomes were evaluated: annual diabetes costs, emergency department (ED) visits and hospital days. Both ordinary least squares (OLS) and IV estimates showed a significant treatment effect for diabetes costs (P = 0.011) but neither model produced a significant treatment effect for ED visits. However, the IV estimate showed a significant treatment effect for hospital days (P = 0.006) whereas the OLS model did not. These results illustrate the utility of IV estimation when the OLS model is sensitive to the confounding effect of hidden bias.

Data Interpretation, Statistical↗

PubMed2007

Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.

CONTEXT: Comparisons of outcomes between patients treated and untreated in observational studies may be biased due to differences in patient prognosis between groups, often because of unobserved treatment selection biases. OBJECTIVE: To compare 4 analytic methods for removing the effects of selection bias in observational studies: multivariable model risk adjustment, propensity score risk adjustment, propensity-based matching, and instrumental variable analysis. DESIGN, SETTING, AND PATIENTS: A national cohort of 122,124 patients who were elderly (aged 65-84 years), receiving Medicare, and hospitalized with acute myocardial infarction (AMI) in 1994-1995, and who were eligible for cardiac catheterization. Baseline chart reviews were taken from the Cooperative Cardiovascular Project and linked to Medicare health administrative data to provide a rich set of prognostic variables. Patients were followed up for 7 years through December 31, 2001, to assess the association between long-term survival and cardiac catheterization within 30 days of hospital admission. MAIN OUTCOME MEASURE: Risk-adjusted relative mortality rate using each of the analytic methods. RESULTS: Patients who received cardiac catheterization (n = 73 238) were younger and had lower AMI severity than those who did not. After adjustment for prognostic factors by using standard statistical risk-adjustment methods, cardiac catheterization was associated with a 50% relative decrease in mortality (for multivariable model risk adjustment: adjusted relative risk [RR], 0.51; 95% confidence interval [CI], 0.50-0.52; for propensity score risk adjustment: adjusted RR, 0.54; 95% CI, 0.53-0.55; and for propensity-based matching: adjusted RR, 0.54; 95% CI, 0.52-0.56). Using regional catheterization rate as an instrument, instrumental variable analysis showed a 16% relative decrease in mortality (adjusted RR, 0.84; 95% CI, 0.79-0.90). The survival benefits of routine invasive care from randomized clinical trials are between 8% and 21%. CONCLUSIONS: Estimates of the observational association of cardiac catheterization with long-term AMI mortality are highly sensitive to analytic method. All standard risk-adjustment methods have the same limitations regarding removal of unmeasured treatment selection biases. Compared with standard modeling, instrumental variable analysis may produce less biased estimates of treatment effects, but is more suited to answering policy questions than specific clinical questions.

Aged↗

PubMed2007

On estimating treatment effects under non-compliance in randomized clinical trials: are intent-to-treat or instrumental variables analyses perfect solutions?

In this report, we compared four estimators (intent-to-treat, as treated, per protocol, and instrumental variables estimators) that are conventionally considered for treatment effect estimation by simulation under different non-compliance scenarios in typical clinical trial settings. We found that intent-to-treat and instrumental variables estimators are not perfect and can be problematic in some situations although these two estimators carry desirable properties as we assume.

Humans↗

PubMed1988

Instrumental variables in the evaluation of diagnostic test procedures when the true disease state is unknown.

We explore the estimation of sensitivity and specificity of diagnostic tests when the true disease state is unknown. Instrumental variables which subdivide the patient population are used. A logistic model, relating these instrumental variables to the (unknown) true disease state is proposed. It is shown that this procedure allows the goodness-of-fit to the resulting model to be tested.

Dapsone↗

PubMed2001

Effectiveness of chemotherapy for advanced lung cancer in the elderly: instrumental variable and propensity analysis.

PURPOSE: To compare the effectiveness of chemotherapy given to elderly patients in routine practice for stage IV non-small-cell lung cancer (NSCLC) with the efficacy observed in randomized trials. PATIENTS AND METHODS: We used instrumental variable analysis (IVA) and propensity scores (PS) to simulate the conditions of a randomized trial in a retrospective cohort of patients over age 65 from the Survival, Epidemiology, and End Results (SEER) tumor registry. Geographic variation in chemotherapy use served as the instrument for the IVA analysis, and propensity scores were calculated with a logistic model based on patient disease and sociodemographic characteristics. RESULTS: Among 6,232 elderly patients, the instrumental variable estimate indicated an increase in median survival of 33 days and an improvement in 1-year survival of 9% attributable to chemotherapy. In a Cox regression model, chemotherapy administration was associated with a hazard ratio of 0.81 (95% confidence interval, 0.76 to 0.85). When survival was analyzed separately within propensity score quintiles, the hazard ratios were all similar, ranging from 0.78 to 0.85. These results are comparable with those of a large meta-analysis, which found a hazard ratio of 0.87 in the subgroup of patients over age 65. CONCLUSION: Chemotherapy for stage IV NSCLC seems to have effectiveness for elderly patients and those with comorbid conditions that is similar to the efficacy seen in randomized trials containing mostly younger, highly selected patients. All suitable patients should be given the opportunity to consider palliative chemotherapy for metastatic NSCLC.

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PubMed2006

Effect of dialysis center profit-status on patient survival: a comparison of risk-adjustment and instrumental variable approaches.

OBJECTIVE: To compare the estimated effects of dialysis center profit status on patient survival using alternative estimation strategies with retrospective data. DATA SOURCES/STUDY SETTING: Patient and provider-level retrospective data from the United States Renal Data System (USRDS), 1996-1999. STUDY DESIGN: Observational risk adjustment and instrumental variable methods. DATA COLLECTION/EXTRACTION METHODS: Study collected measures from various USRDS files describing clinical characteristics, survival, and the profit status of the initial dialysis center for incident end-stage renal disease (ESRD) patients aged 67+. USRDS facility files were used to assess dialysis center profit status and measure patient distances to dialysis centers. PRINCIPAL FINDINGS: Found survival effect related to profit status in the range of previous research using risk-adjusting covariates similar to those used in previous models. Adding further risk-adjusting covariates halved this effect. The relative proximity of for-profit and nonprofit dialysis centers to the patient residence was the strongest determinant of the profit status of the patient's initial dialysis center. The effect of profit status on survival was eliminated using the two-stage least squares variant of instrumental variable estimation with the relative proximity of for-profit and nonprofit dialysis centers to the patient's residence as the instrument. CONCLUSIONS: Using only the variation in initial dialysis center profit status that was related to the relative proximity of for-profit and nonprofit dialysis centers to the patient, we found no relationship between dialysis center profit status and patient survival. These results are in contrast to results obtained using risk-adjustment methods with a limited set of risk-adjusting covariates.

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PubMed1999

The productivity of mental health care: an instrumental variable approach.

BACKGROUND: Like many other medical technologies and treatments, there is a lack of reliable evidence on treatment effectiveness of mental health care. Increasingly, data from non-experimental settings are being used to study the effect of treatment. However, as in a number of studies using non-experimental data, a simple regression of outcome on treatment shows a puzzling negative and significant impact of mental health care on the improvement of mental health status, even after including a large number of potential control variables. The central problem in interpreting evidence from real-world or non-experimental settings is, therefore, the potential "selection bias" problem in observational data set. In other words, the choice/quantity of mental health care may be correlated with other variables, particularly unobserved variables, that influence outcome and this may lead to a bias in the estimate of the effect of care in conventional models. AIMS OF THE STUDY: This paper addresses the issue of estimating treatment effects using an observational data set. The information in a mental health data set obtained from two waves of data in Puerto Rico is explored. The results using conventional models - in which the potential selection bias is not controlled - and that from instrumental variable (IV) models - which is what was proposed in this study to correct for the contaminated estimation from conventional models - are compared. METHODS: Treatment effectiveness is estimated in a production function framework. Effectiveness is measured as the improvement in mental health status. To control for the potential selection bias problem, IV approaches are employed. The essence of the IV method is to use one or more instruments, which are observable factors that influence treatment but do not directly affect patient outcomes, to isolate the effect of treatment variation that is independent of unobserved patient characteristics. The data used in this study are the first (1992-1993) and second (1993-1994) wave of the ongoing longitudinal study Mental Health Care Utilization Among Puerto Ricans, which includes information for an island-wide probability sample of over 3000 adults living in poor areas of Puerto Rico. The instrumental variables employed in this study are travel distance and health insurance sources. RESULTS: It is very noticeable that in this study, treatment effects were found to be negative in all conventional models (in some cases, highly significant). However, after the IV method was applied, the estimated marginal effects of treatment became positive. Sensitivity analysis partly supports this conclusion. According to the IV estimation results, treatment is productive for the group in most need of mental health care. However, estimations do not find strong enough evidence to demonstrate treatment effects on other groups with less or no need. The results in this paper also suggest an important impact of the following factors on the probability of improvement in mental health status: baseline mental health status, previous treatment, sex, marital status and education. DISCUSSION: The IV approach provides a practical way to reduce the selection bias due to the confounding of treatment with unmeasured variables. The limitation of this study is that the instruments explored did not perform well enough in some IV equations, therefore the predictive power remains questionable. The most challenging part of applying the IV approach is on finding "good" instruments which influence the choice/quantity of treatment yet do not introduce further bias by being directly correlated with treatment outcome. CONCLUSIONS: The results in this paper are supportive of the concerns on the credibility of evaluation results using observation data set when the endogeneity of the treatment variable is not controlled. Unobserved factors contribute to the downward bias in the conventional models. The IV approach is shown to be an appropriate method to reduce the selection bias for the group in most need for mental health care, which is also the group of most policy and treatment concern. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The results of this work have implications for resource allocation in mental health care. Evidence is found that mental health care provided in Puerto Rico is productive, and is most helpful for persons in most need for mental health care. According to what estimated from the IV models, on the margin, receiving formal mental health care significantly increases the probability of obtaining a better mental health outcome by 19.2%, and one unit increase in formal treatment increased the probability of becoming healthier by 6.2% to 8.4%. Consistent with other mental health literature, an individual's baseline mental health status is found to be significantly related to the probability of improvement in mental health status: individuals with previous treatment history are less likely to improve. Among demographic factors included in the production function, being female, married, and high education were found to contribute to a higher probability of improvement. IMPLICATION FOR FURTHER RESEARCH: In order to provide accurate evidence of treatment effectiveness of medical technologies to support decision making, it is important that the selection bias be controlled as rigorously as possible when using information from a non-experimental setting. More data and a longer panel are also needed to provide more valid evidence. tion.

Journal Article↗

PubMed2003

An exploratory instrumental variable analysis of the outcomes of localized breast cancer treatments in a medicare population.

This study is motivated by the potential problem of using observational data to draw inferences about treatment outcomes when experimental data are not available. We compare two statistical approaches, ordinary least-squares (OLS) and instrumental variables (IV) regression analysis, to estimate the outcomes (three-year post-treatment survival) of three treatments for early stage breast cancer in elderly women: mastectomy (MST), breast conserving surgery with radiation therapy (BCSRT), and breast conserving surgery only (BCSO). The primary data source was Medicare claims for a national random sample of 2907 women (age 67 or older) with localized breast cancer who were treated between 1992 and 1994. Contrary to randomized clinical trial (RCT) results, analysis with the observational data found highly significant differences in survival among the three treatment alternatives: 79.2% survival for BCSO, 85.3% for MST, and 93.0% for BCSRT. Using OLS to control for the effects of observable characteristics narrowed the estimated survival rate differences, which remained statistically significant. In contrast, the IV analysis estimated survival rate differences that were not significantly different from 0. However, the IV-point estimates of the treatment effects were quantitatively larger than the OLS estimates, unstable, and not significantly different from the OLS results. In addition, both sets of estimates were in the same quantitative range as the RCT results.We conclude that unadjusted observational data on health outcomes of alternative treatments for localized breast cancer should not be used for cost-effectiveness studies. Our comparisons suggest that whether one places greater confidence in the OLS or the IV results depends on at least three factors: (1) the extent of observable health information that can be used as controls in OLS estimation, (2) the outcomes of statistical tests of the validity of the instrumental variable method, and (3) the similarity of the OLS and IV estimates. In this particular analysis, the OLS estimates appear to be preferable because of the instability of the IV estimates.

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PubMed2000

An instrumental variables approach to measuring the effect of body weight on employment disability.

OBJECTIVE: To measure the effect of body weight on employment disability. DATA SOURCES: Female respondents to the National Longitudinal Survey of Youth (NLSY), a nationally representative sample of American youth, surveyed from 1979 to 1998, merged with data from the child sample of the NLSY. STUDY DESIGN: A series of probit models and probit models with instrumental variables is estimated with the goal of measuring the effect of body weight on employment disability. The two outcomes of interest are whether a woman reports that her health limits the amount of work that she can do for pay, and whether she reports that her health limits the kind of work that she can do for pay. The models control for factors that affect the probability of health limitations on employment, such as education, cognitive ability, income of other family members, and characteristics of children in the household. Self-reports of height and weight are corrected for reporting error. PRINCIPAL FINDINGS: All else being equal, heavier women are more likely to report employment disability. However, this overall correlation may be due to any or all of the following factors: weight causing disability, disability causing weight gain, or unobserved factors causing both. Instrumental variables estimates provide no evidence that body weight affects the probability of either type of employment disability. CONCLUSIONS: This study finds no evidence that body weight causes employment disability. Instead, the observed correlation between heaviness and disability may be due to disability causing weight gain or unobservable factors causing both disability and weight gain.

Adult↗