Risk factors increase chronic disease incidence and severity. To examine future trends and develop policies addressing chronic diseases, it is important to capture the relationship between exposure and disease development -- challenging given limited data.
To develop parsimonious risk factor models embeddable in chronic disease models, useful when longitudinal data are unavailable.
The model structures encode relevant features of risk factors (e.g., time-varying, modifiable) and can be embedded in chronic disease models. Calibration captures time-varying exposures for the risk factor models using available, cross-sectional data. We illustrate feasibility with the policy-relevant example of smoking in India.
The model is calibrated to prevalence of male smoking in 12 Indian regions estimated from the 2009–10 Indian Global Adult Tobacco Survey. Nelder-Mead searches (250,000 starting locations) identify distributions of starting, quitting, and re-starting rates that minimize the difference between modeled and observed age-specific prevalence. We compare modeled life expectancies to estimates in the absence of time-varying risk exposures and consider gains from hypothetical smoking cessation programs delivered for 1–30 years.
Calibration achieves concordance between modeled and observed outcomes. Probabilities of starting to smoke rise and fall with age, while quitting and re-starting probabilities fall with age. Accounting for time-varying smoking exposures is important, as not doing so produces smaller estimates of life expectancy losses. Estimated impacts of smoking cessation programs delivered for different periods depend on the fact that people who have been induced to abstain from smoking longer are less likely to re-start.
The approach described is feasible for numerous chronic disease risk factors. Incorporating exposure-change rates can improve modeled estimates of chronic disease outcomes and long-term, risk factor intervention effects.
To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single “goodness-of-fit” (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better.
We demonstrate the Pareto frontier approach in the calibration of two models: a simple, illustrative Markov model and a previously-published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to two possible weighted-sum GOF scoring systems, and compare the health economic conclusions arising from these different definitions of best-fitting.
For the simple model, outcomes evaluated over the best-fitting input sets according to the two weighted-sum GOF schemes were virtually non-overlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95%CI: 72,500 – 87,600] vs. $139,700 [95%CI: 79,900 - 182,800] per QALY gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95%CI: 64,900 – 156,200] per QALY gained). The TAVR model yielded similar results.
Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets.
International guidelines recommend HIV treatment expansion in resource-limited settings, but funding availability is uncertain. We evaluated performance of a model that forecasts lives saved through continued HIV treatment expansion in Haiti.
We developed a computer-based, mathematical model of HIV disease and used incidence density analysis of patient-level Haitian data to derive model parameters for HIV disease progression. We assessed internal validity of model predictions and internally calibrated model inputs when model predictions did not fit the patient-level data. We then derived uncertain model inputs related to diagnosis and linkage to care, pre-treatment retention, and enrollment on HIV treatment through an external calibration process that selected input values by comparing model predictions to Haitian population-level data. Model performance was measured by fit to event-free survival (patient-level) and number receiving HIV treatment over time (population-level).
For a cohort of newly HIV-infected individuals with no access to HIV treatment, the model predicts median AIDS-free survival of 9.0 years pre-calibration and 6.6 years post-calibration versus 5.8 years (95% CI 5.1, 7.0) from the patient-level data. After internal validation and calibration, 16 of 17 event-free survival measures (94%) had a mean percentage deviation between model predictions and the empiric data of <6%. After external calibration, the percentage deviation between model predictions and population-level data on the number on HIV treatment was <1% over time.
Validation and calibration resulted in a good-fitting model appropriate for health policy decision making. Using local data in a policy model-building process is feasible in resource-limited settings.
HIV/AIDS; antiretroviral therapy; mortality; resource-limited settings; simulation; model performance; calibration; validation
Control of C. difficile infection (CDI) is an increasingly difficult problem for healthcare institutions. There are commonly recommended strategies to combat CDI transmission such as oral vancomycin for CDI treatment, increased hand hygiene with soap and water for healthcare workers, daily environmental disinfection of infected patient rooms, and contact isolation of diseased patients. However, the efficacy of these strategies, particularly for endemic CDI, has not been well studied. The objective of this research is to develop a valid agent-based simulation model (ABM) to study C. difficile transmission and control in a mid-sized hospital.
We develop an ABM of a mid-sized hospital with agents such as patients, healthcare workers, and visitors. We model the natural progression of CDI in a patient using a Markov chain and the transmission of CDI through agent and environmental interactions. We derive input parameters from aggregate patient data from the 2007-2010 Wisconsin Hospital Association and published medical literature. We define a calibration process, which we use to estimate transition probabilities of the Markov model by comparing simulation results to benchmark values found in published literature.
Comparing CDI control strategies implemented individually, routine bleach disinfection of CDI+ patient rooms provides the largest reduction in nosocomial asymptomatic colonizations (21.8%) and nosocomial CDIs (42.8%). Additionally, vancomycin treatment provides the largest reduction in relapse CDIs (41.9%), CDI-related mortalities (68.5%), and total patient LOS (21.6%).
We develop a generalized ABM for CDI control that can be customized and further expanded to specific institutions and/or scenarios. Additionally, we estimate transition probabilities for a Markov model of natural CDI progression in a patient through calibration.
clostridium difficile; simulation methods; agent-based simulation; infectious disease control; hospital-acquired infections
Applications of cost-effectiveness analysis do not typically incorporate effects on caregiver quality of life despite increasing evidence that these effects are measurable.
Using a national sample of US adults, we conducted two cross-sectional surveys in December 2011–January 2012. One version asked respondents to value their own experience as the family member of a person with a chronic illness (experienced sample) and the other version asked respondents to value hypothetical scenarios describing the experience of having a family member with a chronic illness (community sample). Conditions included Alzheimer’s disease/dementia, arthritis, cancer, and depression. Using standard gamble questions, respondents were asked to value the spillover effects of a family member’s illness. We used regression analysis to evaluate the disutility (loss in health-related quality of life) of having a family member with a chronic illness by condition and relationship type controlling for respondent’s own conditions and sociodemographic characteristics.
For the experienced sample (n=1389), regression analyses suggested greater spillover was associated with certain conditions (arthritis, depression) compared with other conditions (Alzheimer’s disease, cancer). For the community sample (n=1205), regression analyses indicated that lower spillover was associated with condition (cancer) but not the type of relationship with the ill family member (parent, child, spouse).
The effects of illness extend beyond the individual patient to include effects on caregivers of patients, parents of ill children, spouses, and other close family and household members. Cost-effectiveness analyses should consider the inclusion of health-related quality of life spillover effects in addition to caregiving time costs incurred by family members of ill individuals.
health utility; cost-effectiveness analysis; caregivers; family; health related quality of life
Despite widespread advocacy for shared decision making (SDM), the
empirical evidence regarding its effectiveness to improve patient outcomes
has not been systematically reviewed.
To systematically review the empirical evidence linking patient
outcomes and SDM, when the decision-making process has been explicitly
measured, and to identify under what measurement perspectives SDM is
associated with which types of patient outcomes (affective-cognitive,
behavioral, and health).
PubMed (through December 2012) and hand search of article
Studies were included if they empirically (1) measured SDM in the context of a patient-clinician
interaction, and (2) evaluated the
relationship between SDM and at least one patient outcome.
Study results were categorized by SDM measurement perspective
(patient-reported, clinician-reported, or observer-rated) and outcome type
(affective-cognitive, behavioral, or health).
Thirty-nine studies met inclusion criteria. Thirty-three used
patient-reported measures of SDM, six used observer-rated, and two used
clinician-reported. Ninety-seven unique patient outcomes were assessed;
51% affective-cognitive, 28% behavioral, and 21%
health. Only 43% of assessments (n=42) found a significant and
positive relationship between SDM and the patient outcome. This proportion
varied by SDM measurement perspective and outcome category. 52% of
outcomes assessed with patient-reported SDM were significant and positive,
compared to 21% with observer-rated and 0% with
clinician-reported SDM. Regardless of measurement perspective, SDM was most
likely to be associated with affective-cognitive patient outcomes
(54%), compared to 37% of behavioral, and 25% of
The relatively small number of studies, precludes meta-analysis. The
study inclusion and exclusion criteria requiring both an empirical measure
of SDM as well as an assessment of the association between that measure and
a patient outcome, resulted in most included studies being observational in
SDM, when perceived by patients as occurring, tends to result in
improved affective-cognitive outcomes. Evidence is lacking for the
association between empirical measures of SDM and patient behavioral and
Extensive use of unnecessary antibiotics has driven the emergence of
resistant bacterial strains, posing a threat to public health. Physicians
are more likely to prescribe antibiotics when they believe that patients
expect them. Current attempts to change these expectations highlight the
distinction between viruses and bacteria (“Germs are Germs”).
Fuzzy Trace Theory further predicts that patients expect antibiotics because
they make decisions based on categorical gist, producing strategies that
encourage risk taking when the status quo is bad (i.e., “Why Not Take
a Risk?”). We investigate both hypotheses.
We surveyed patients visiting the emergency department of a large
urban hospital (72, 64%, were African-American) using 17 Likert-scale
questions and two free-response questions regarding patient expectations for
After the clinical encounter, 113 patients completed the survey. 54
(48%) patients agreed with items that assess the “Germs are
Germs” hypothesis, whereas 86 (76%) agreed with items that assess the
“Why Not Take a Risk?” hypothesis. “Why Not Take a
Risk?” captures significant unique variance in a factor analysis, and
is neither explained by “Germs are Germs,” nor by
patients’ lack of knowledge regarding side effects. Of the 81
patients who rejected the “Germs are Germs” hypothesis, 61
(75%) still indicated agreement with the “Why Not Take a
Risk?” hypothesis. Several other misconceptions were also
Our findings suggest that recent public health campaigns that have
focused on educating patients about the differences between viruses and
bacteria omit a key motivation for why patients expect antibiotics,
supporting Fuzzy Trace Theory’s predictions about categorical gist.
The implications for public health and emergency medicine are discussed.
Preference for the status quo, or clinical inertia, is a barrier towards implementing treat-to-target protocols in patients with chronic diseases such as rheumatoid arthritis (RA). The objectives of this study were to examine the influence of subjective numeracy on RA-patient preference for the status quo and to determine whether age modifies this relationship.
RA patients participated in a single face-to-face interview. Numeracy was measured using the Subjective Numeracy Scale. Treatment preference was measured using Adaptive Conjoint Analysis.
Of 205 eligible subjects, 156 agreed to participate. Higher subjective numeracy was associated with lower preference for the status quo in a regression model including race, employment, and biologic use [Adjusted OR (95% CI)= 0.71 (0.52–0.95), p= 0.02]. Higher subjective numeracy was protective against status quo preferences among subjects less than 65 years of age [Adjusted OR (95% CI)= 0.64 (0.43–0.94), p= 0.02], but not among older subjects.
In summary, subjective numeracy is independently associated with younger, but not older, RA patients’ preferences for the status quo. Our results add to the literature demonstrating age and numeracy differences in treatment preferences and medical-decision-making processes.
Decision making; aging; numeracy; status quo bias
Many healthy women consider genetic testing for breast cancer risk, yet BRCA testing issues are complex.
Determining whether an intelligent tutor, BRCA Gist, grounded in fuzzy-trace theory (FTT), increases gist comprehension and knowledge about genetic testing for breast cancer risk, improving decision-making.
In two experiments, 410 healthy undergraduate women were randomly assigned to one of three groups: an online module using a web-based tutoring system (BRCA Gist) that uses artificial intelligence technology, a second group read highly similar content from the NCI web site, and a third completed an unrelated tutorial.
BRCA Gist applied fuzzy trace theory and was designed to help participants develop gist comprehension of topics relevant to decisions about BRCA genetic testing, including how breast cancer spreads, inherited genetic mutations, and base rates.
We measured content knowledge, gist comprehension of decision-relevant information, interest in testing, and genetic risk and testing judgments.
Control knowledge scores ranged from 54% to 56%, NCI improved significantly to 65% and 70%, and BRCA Gist improved significantly more to 75% and 77%, p<.0001. BRCA Gist scored higher on gist comprehension than NCI and control, p<.0001. Control genetic risk-assessment mean was 48% correct; BRCA Gist (61%), and NCI (56%) were significantly higher, p<.0001. BRCA Gist participants recommended less testing for women without risk factors (not good candidates), (24% and 19%) than controls (50%, both experiments) and NCI, (32%) Experiment 2, p<.0001. BRCA Gist testing interest was lower than controls, p<.0001.
BRCA Gist has not been tested with older women from diverse groups.
Intelligent tutors, such as BRCA Gist, are scalable, cost effective ways of helping people understand complex issues, improving decision-making.
Given the long natural history of prostate cancer we assessed differing graphical formats for imparting knowledge about the longitudinal risks of prostate cancer recurrence with or without therapy.
Male volunteers without a history of prostate cancer were randomized to one of eight risk communication instruments that depicted the likelihood of prostate cancer returning or spreading over 1, 2, and 3 years. The tools differed in format (line, pie, bar, or pictograph) and whether the graph also included no numbers, 1 number (indicating the number of affected individuals) or 2 numbers (indicting both the number affected and the number unaffected). The main outcome variables evaluated were graphical preference and knowledge.
A total of 420 men were recruited with respondents being least familiar and experienced with pictographs (p<0.0001) and only 10% preferred this particular format. Overall accuracy ranged from 79-92%, and when assessed across all graphical sub-types the addition of numerical information did not improve verbatim knowledge (p=0.1). Self-reported numeracy was a strong predictor of accuracy of responses ((OR=2.6, p=0.008), and the impact of high numeracy varied across graphical type having a greater impact upon line (OR= 5.1 95%CI [1.6,16],p=0.04) and pie charts (OR=7.1 95%CI [2.6,19],p=0.01) without an impact on pictographs (OR=0.4 95%CI [0.1,1.7], p=0.17) or bar charts (OR=0.5 95%CI [0.1,1.8], p=0.24).
For longitudinal presentation of risk, baseline numeracy was strongly prognostic for outcome. However, The addition of numbers to risk graphs only improved the delivery of verbatim knowledge for subjects with lower numeracy. Although subjects reported the least familiarity with pictographs they were one of the most effective means of transferring information regardless of numeracy.
Shared Decision Making (SDM) is an approach to medical care based on collaboration between provider and patient with both sharing in medical decisions. When patients’ values and preferences are incorporated in decision-making, then care is more appropriate, ethically sound, and often lower in cost. However, SDM is difficult to implement in routine practice because of the time required for SDM methods, the lack of integration of SDM approaches into electronic health records systems (EHRs), and absence of explanatory mechanisms for providers on the results of patients’ use of decision aids. This paper discusses potential solutions including the concept of a “Personalize Button” for EHRs. Leveraging a four-phased clinical model for SDM, this article describes how computer decision support (CDS) technologies integrated into EHRs can help insure that healthcare is delivered in a way that is respectful of those preferences. The architecture described herein, called CDS for SDM, is built upon recognized standards that are currently integrated into certification requirements for EHRs as part of Meaningful Use regulations. While additional work is needed on modeling of preferences and on techniques for rapid communication models of preferences to clinicians, unless EHRs are re-designed to support SDM around and during clinical encounters, they are likely to continue to be an unintended barrier to SDM. With appropriate development, EHRs could be a powerful tool to promote SDM by reminding providers of situations for SDM and monitoring on going care to insure treatments are consistent with patients’ preferences.
shared decision-making; workflow; patient preferences; electronic health records; decision-support; Medicine, personalized; Infobutton; user computer interface
Decision making experts emphasize that understanding and using probabilistic information is important for making informed decisions about medical treatments involving complex risk-benefit tradeoffs. Yet empirical research demonstrates that individuals may not use probabilities when making decisions.
To explore decision making and the use of probabilities for decision making from the perspective of women who were risk-eligible to enroll in the Study of Tamoxifen and Raloxifene (STAR).
We conducted narrative interviews with 20 women who agreed to participate in STAR and 20 women who declined. The project was based on a narrative approach. Analysis included the development of summaries of each narrative, and thematic analysis with developing a coding scheme inductively to code all transcripts to identify emerging themes.
Interviewees explained and embedded their STAR decisions within experiences encountered throughout their lives. Such lived experiences included but were not limited to breast cancer family history, personal history of breast biopsies, and experiences or assumptions about taking tamoxifen or medicines more generally.
Women’s explanations of their decisions about participating in a breast cancer chemoprevention trial were more complex than decision strategies that rely solely on a quantitative risk-benefit analysis of probabilities derived from populations In addition to precise risk information, clinicians and risk communicators should recognize the importance and legitimacy of lived experience in individual decision making.
Personalized medicine is health care that tailors interventions to individual variation in risk and treatment response. Although medicine has long strived to achieve this goal, advances in genomics promise to facilitate this process. Relevant to present-day practice is the use of genomic information to classify individuals according to disease susceptibility or expected responsiveness to a pharmacologic treatment and to provide targeted interventions. A symposium at the annual meeting of the Society for Medical Decision Making on 23 October 2007 highlighted the challenges and opportunities posed in translating advances in molecular medicine into clinical practice. A panel of US experts in medical practice, regulatory policy, technology assessment, and the financing and organization of medical innovation was asked to discuss the current state of practice and research on personalized medicine as it relates to their own field. This article reports on the issues raised, discusses potential approaches to meet these challenges, and proposes directions for future work. The case of genetic testing to inform dosing with warfarin, an anticoagulant, is used to illustrate differing perspectives on evidence and decision making for personalized medicine.
cost-effectiveness analysis; pharmacoeconomics; resource allocation
Background. Policy makers require estimates of comparative effectiveness that apply to the population of interest, but there has been little research on quantitative approaches to assess and extend the generalizability of randomized controlled trial (RCT)–based evaluations. We illustrate an approach using observational data. Methods. Our example is the Whole Systems Demonstrator (WSD) trial, in which 3230 adults with chronic conditions were assigned to receive telehealth or usual care. First, we used novel placebo tests to assess whether outcomes were similar between the RCT control group and a matched subset of nonparticipants who received usual care. We matched on 65 baseline variables obtained from the electronic medical record. Second, we conducted sensitivity analysis to consider whether the estimates of treatment effectiveness were robust to alternative assumptions about whether “usual care” is defined by the RCT control group or nonparticipants. Thus, we provided alternative estimates of comparative effectiveness by contrasting the outcomes of the RCT telehealth group and matched nonparticipants. Results. For some endpoints, such as the number of outpatient attendances, the placebo tests passed, and the effectiveness estimates were robust to the choice of comparison group. However, for other endpoints, such as emergency admissions, the placebo tests failed and the estimates of treatment effect differed markedly according to whether telehealth patients were compared with RCT controls or matched nonparticipants. Conclusions. The proposed placebo tests indicate those cases when estimates from RCTs do not generalize to routine clinical practice and motivate complementary estimates of comparative effectiveness that use observational data. Future RCTs are recommended to incorporate these placebo tests and the accompanying sensitivity analyses to enhance their relevance to policy making.
causal inference; external validity; generalizability; randomized trials; telehealth; chronic health conditions
Several treatments for alcohol dependence have been tested in randomized clinical trials, giving rise to systematic reviews with a network of evidence, or mixed treatment comparisons, structure. Within the network, there are few direct comparisons of active treatments. Thus far, this network has not been adequately analyzed. For example, “indirect comparisons” between treatments (e.g., the comparison of treatments B:C obtained via estimates from A:B and A:C trials) have not been incorporated into estimates of treatment effects. This has implications for the planning of future randomized controlled trials.
We apply recent developments in Bayesian mixed treatment comparisons (MTC) meta-analysis to analyze the network of evidence. Using these results, we propose a methodology to inform, design, and power a hypothetical trial in the context of an updated meta-analysis, for treatments that have been infrequently compared, and therefore whose effects sizes are not well informed by meta-analysis.
MTC meta-analysis provides more accurate estimates than pairwise meta-analysis and uncovers decisive differences between active treatments that have been infrequently directly compared. Weighting across all outcomes indicates that combination (naltrexone+acamprosate) treatment has the highest posterior probability of being the “best” treatment. If a new clinical trial were to be performed of combination therapy versus acamprosate alone, there is no feasible sample size that would result in a decisive meta-analysis.
MTC meta-analysis should be used to estimate treatment effects in networks where direct and indirect evidence are consistent, and to inform the design of future studies.
Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here we present an alternative to the binormal model based on the Lehmann family, also known as the proportional hazards specification. The resulting ROC curve and its functionals (such as the area under the curve and the sensitivity at a given level of specificity) have simple analytic forms. Closed-form expressions for the functional estimates and their corresponding asymptotic variances are derived. This family accommodates the comparison of multiple markers, covariate adjustments and clustered data through a regression formulation. Evaluation of the underlying assumptions, model fitting and model selection can be performed using any off the shelf proportional hazards statistical software package.
Regression; clustered data; accuracy; concordance; proportional hazards
Much of the literature on obesity has consistently documented the
unprecedented rise in body weight over the last two decades. Less attention
is paid to future projections of the population distribution of body-mass
To forecast the distribution of body-mass index in children
(6–17 years) and adults (>17 years) in the United States by
age-group, sex and race over the period 2004–2014.
Analysis of Medical Expenditure Panel Survey data (2001–2002
and 2004–2005) to estimate and compare the 1-year transitions across
BMI categories for children and adults. Forecasting distributions of obesity
over 2004–2014 using a probabilistic population-level simulation
model and validate it with prevalence data from 2005–2006
During 2004–2005, a majority of adults in each BMI category
remained in the same category after one year, these estimates being not
significantly different that the corresponding estimates in
2001–2002. Among children, stabilities within BMI categories are low
during 2004–2005, and compared to 2001–2002, transition
probabilities into Overweight Class 2 from other BMI categories increase
Forecasts reveal significant increases in the Risk of Overweight
category among children 6–9 years old (5% to 14% in
5 years), with a greater increase anticipated in males; increases in
overweight category for many years to come for adults, although the adult
obesity prevalence remain at the current levels.
Although the absolute levels of obesity remain high among US adults,
the growth in obesity appear to have stagnated. On the contrary, continued
growth in the prevalence of the highest BMI category for children is
To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at one or zero, and heteroskedasticity.
Regression estimators based on features of the Beta distribution. Firstly, we present both a single equation and a two-part model, along with estimation algorithms based on maximum-likelihood, quasi-likelihood and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Secondly, we present a simulation exercise to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, we assess the performance of the proposed estimators by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson’s correlation test, Link and Reset tests and a modified Hosmer-Lemeshow test.
Our simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at one. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect.
One and two-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.
Regression; Quality of Life; QALYs; Beta distribution; Quasi-likelihood; Bayesian
The benefits of prescribing cardiac rehabilitation (CR) for patients following heart surgery is well documented. However physicians continue to underutilize CR programs and disparities in the referral of women are common. Previous research into the causes of these problems has relied on self-report methods which presume that physicians have insight into their referral behavior and can describe it accurately. In contrast, the research presented here employed clinical judgment analysis (CJA) to discover the tacit judgment and referral policies of individual physicians.
The specific aims were to determine 1) what these policies were, 2) the degree of self-insight that individual physicians had into their own policies, 3) the amount of agreement among physicians, and 4) the extent to which judgments were related to attitudes toward CR.
Thirty-six Canadian physicians made judgments and decisions regarding 32 hypothetical cardiac patients, each described on five characteristics (gender, age, type of surgical procedure, presence/absence of musculoskeletal pain, and degree of motivation) and then completed the 19 items of the Attitude towards Cardiac Rehabilitation Referral instrument.
There was wide variation among physicians in their tacit and stated judgment policies. Physicians exhibited greater agreement in what they believed they were doing (stated policies) than in what they actually did (tacit policies). Nearly one-third of the physicians showed evidence of systematic, and perhaps subliminal, gender bias as they judged women as less likely than men to benefit from CR. Correlations between attitude statements and CJA measures were modest.
These findings offer some explanation for the slow progress of efforts to improve CR referrals and for gender disparities in referral rates.
PMID: 23784848 CAMSID: cams4937
clinical judgment analysis; cardiac rehabilitation; gender disparity
Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method.
expected value of sample information; economic evaluation model; Monte Carlo methods; Bayesian decision theory; computational methods; nonparametric regression; generalized additive model.
Cost-effectiveness analysis (CEA) models are routinely used to inform health care policy. Key model inputs include relative effectiveness of competing treatments, typically informed by meta-analysis. Heterogeneity is ubiquitous in meta-analysis, and random effects models are usually used when there is variability in effects across studies. In the absence of observed treatment effect modifiers, various summaries from the random effects distribution (random effects mean, predictive distribution, random effects distribution, or study-specific estimate [shrunken or independent of other studies]) can be used depending on the relationship between the setting for the decision (population characteristics, treatment definitions, and other contextual factors) and the included studies. If covariates have been measured that could potentially explain the heterogeneity, then these can be included in a meta-regression model. We describe how covariates can be included in a network meta-analysis model and how the output from such an analysis can be used in a CEA model. We outline a model selection procedure to help choose between competing models and stress the importance of clinical input. We illustrate the approach with a health technology assessment of intravenous immunoglobulin for the management of adult patients with severe sepsis in an intensive care setting, which exemplifies how risk of bias information can be incorporated into CEA models. We show that the results of the CEA and value-of-information analyses are sensitive to the model and highlight the importance of sensitivity analyses when conducting CEA in the presence of heterogeneity. The methods presented extend naturally to heterogeneity in other model inputs, such as baseline risk.
cost-effectiveness analysis; Bayesian meta-analysis; value of information
Current US colorectal cancer screening guidelines that call for shared decision making regarding the choice among several recommended screening options are difficult to implement. Multi-criteria decision analysis (MCDA) is an established methodology well suited for supporting shared decision making. Our study goal was to determine if a streamlined form of MCDA using rank order based judgments can accurately assess patients’ colorectal cancer screening priorities.
We converted priorities for four decision criteria and three sub-criteria regarding colorectal cancer screening obtained from 484 average risk patients using the Analytic Hierarchy Process (AHP) in a prior study into rank order-based priorities using rank order centroids. We compared the two sets of priorities using Spearman rank correlation and non-parametric Bland-Altman limits of agreement analysis. We assessed the differential impact of using the rank order-based versus the AHP-based priorities on the results of a full MCDA comparing three currently recommended colorectal cancer screening strategies. Generalizability of the results was assessed using Monte Carlo simulation.
Correlations between the two sets of priorities for the seven criteria ranged from 0.55 to 0.92. The proportions of absolute differences between rank order-based and AHP-based priorities that were more than ± 0.15 ranged from 1% to 16%. Differences in the full MCDA results were minimal and the relative rankings of the three screening options were identical more than 88% of the time. The Monte Carlo simulation results were similar.
Rank order-based MCDA could be a simple, practical way to guide individual decisions and assess population decision priorities regarding colorectal cancer screening strategies. Additional research is warranted to further explore the use of these methods for promoting shared decision making.
This study used the Ottawa Decision Support Framework to evaluate a model examining associations between clinical trial knowledge, attitudinal barriers to participating in clinical trials, clinical trials self-efficacy, and clinical trial preparedness among 1256 cancer patients seen for their first outpatient consultation at a cancer center. As an exploratory aim, moderator effects for gender, race/ethnicity, education, and metastatic status on associations in the model were evaluated.
Patients completed measures of cancer clinical trial knowledge, attitudinal barriers, self-efficacy and preparedness. Structural equation modeling (SEM) was conducted to evaluate whether self-efficacy mediated the association between knowledge and barriers with preparedness.
The SEM explained 26% of the variance in cancer clinical trial preparedness. Self-efficacy mediated the associations between attitudinal barriers and preparedness, but self-efficacy did not mediate the knowledge-preparedness relationship.
Findings partially support the Ottawa Decision Support Framework and suggest that assessing patients’ level of self-efficacy may be just as important as evaluating their knowledge and attitudes about cancer clinical trials.
Cancer clinical trials; Ottawa Decision Support Framework; cancer patients
Numerous research studies have demonstrated that icon arrays (also called “pictographs”) are an effective method of communicating risk statistics and appear particularly useful to less numerate and less graphically literate people. Yet research is very limited regarding whether icon type affects how people interpret and remember these graphs.
1504 people age 35 to 75 from a demographically-diverse online panel completed a cardiovascular risk calculator based on Framingham data using their actual age, weight, and other health data. Participants received their risk estimate in an icon array graphic that used one of 6 types of icons: rectangular blocks, filled ovals, smile/frown faces, an outline of a person’s head and shoulders, male/female “restroom” person icons (gender matched), or actual head-and-shoulder photographs of people of varied races (gender matched). In each icon array, blue icons represented cardiovascular events and grey icons represented those who would not experience an event. We measured perceived risk magnitude, approximate recall, and opinions about the icon arrays, as well as subjective numeracy and an abbreviated measure of graphical literacy.
Risk recall was significantly higher with more anthropomorphic icons (restroom icons, head outlines, and photos) than with other icon types, and participants rated restroom icons as most preferred. However, while restroom icons resulted in the highest correlations between perceived and actual risk among more numerate and more graphically literate participants, they performed no better than other icon types among less numerate/graphically literate participants.
Icon type influences both risk perceptions and risk recall, with restroom icons in particular resulting in improved outcomes. However, optimal icon types may depend on numeracy and/or graphical literacy skills.
risk; patient education as topic; patient-provider communication; decision aids; visual aids