Probability Of Benefit Research Articles

MO1 - A Unified Framework For Classification Of Methods For Benefit-Risk Assessment

Patients, physicians, and other decision-makers make implicit trade-offs among benefits and risks of different treatments. Many methods have been proposed to conduct quantitative benefit-risk analysis (BRA). We propose a framework for classifying BRA methods based on factors that matter most to patients. Using common mathematical notation, we compare the methods using a hypothetical example. We classified available BRA methods into three categories: (1) un-weighted metrics, that use only probabilities of benefits and risks (e.g., number needed to treat and number needed to harm [NNT|NNH]); (2) metrics that incorporate preference weights to account for the impact and duration of outcomes (e.g., Maximum Acceptable Risk [MAR], relative value-adjusted life-years [RVALYs], quality-adjusted life-years [QALYs]); and (3) metrics that incorporate ad hocweights based on decision makers’ opinions (e.g., Multi-criteria Decision Analysis, Benefit-Less-Risk Analysis). We used two hypothetical antiplatelet drugs (A and B), probabilities of benefits (reduction in myocardial infarction and stroke) and harms (increases in major and minor bleeding) based on randomized trial data, and preference weights from the literature to compare the BRA methods within the proposed framework. Use of the framework and notation revealed BRA methods share substantial commonality. In the example, BRA using NNT|NNH indicated that -1.3% of patients would experience net benefit with drug A versus B, (an unfavorable benefit-risk balance for A). In contrast, 4.6% of patients would experience a net benefit with drug A if weighted using MAR. BRA using RVALYs and QALYs suggested gains of 3.8 RVALYs and 5.4 QALYs per 100 patient-years, respectively, with drug A versus B. The proposed framework provides a unified, patient-centered approach to BRA methods classification. All methods impose trade-offs between probabilities of benefits and risks. The weights used in the metrics is a key differentiating feature and can lead to quantitatively and qualitatively different results.

Quantitative Progesterone Receptor Expression and Efficacy of Anti-estrogen Therapy in Breast Cancer

The central role of estrogen receptor (ER) presence in predicting which breast cancer patients are likely to benefit from anti-estrogen therapies is well-established, but the added benefit of progesterone receptor (PR) and in particular low levels of PR is less well understood. The objective of this study was to determine the quantitative relationship between borderline levels of PR and subsequent benefit from anti-estrogen therapy. We examined data from 447 patients, age 50 or older. ER and PR levels were quantitated by conventional ligand binding assay and Scatchard plot analysis or by enzyme-linked immunoassay. Comparison of clinical outcome in relation with ER and PR status was calculated using Kaplan-Meier actuarial survival analysis and the log-rank test. Subpopulation treatment effect pattern plot (STEPP) analysis was used to explore the interaction between treatment effects and ER or PR levels for the 409 patients with ER values greater than 0. For anti-estrogen treated patients, when the ER and PR positivity cut-off was set at 1.0 fmole/mg protein, there was a statistically significant advantage for patients with ER+PR+ over ER+ PR- tumors for both breast cancer-free interval (BCFI) and overall survival (OS). STEPP analysis found no overall interaction between treatment outcome (5 year survival probability) and levels of hormone receptor. However, patients with borderline PR levels did not appear to benefit from anti-estrogen therapy. PR levels above borderline in addition to the presence of ER predicts an increased probability of benefit from anti-estrogen therapy in breast cancer patients.

Understanding What Participants in Empirical Bioethical Studies Mean: Historical Cautions From William James and Ludwig Wittgenstein

Methods from psychology are informing much empirical research in bioethics by helping to understand the thoughts, feelings, and beliefs of people as they relate to a variety of bioethical issues. This can lead to improvements in practice or policy only if the subjective mental states under study have been characterized accurately. In this article, I describe two cautions from the history of psychology concerning the accurate characterization of mental states that have significant implications for how we elicit and interpret data in empirical bioethical studies. Both make reference to tendencies of mind that can be difficult to combat and that are the cause of other more specific methodological errors. The first historical caution, William James's “psychologist's fallacy,” warns against substituting the ethicist/researcher's point of view with that of the person under study. Failure to appreciate this essentially egocentric bias can result in asking people to report on things (e.g., probability of benefit from an experimental therapy) that are not a part of the person's experience in the same way they are a part of the researcher's worldview. The responses the person provides in such cases do not provide good information about his or her experience and so cannot be used to guide sound policy. The second historical caution is Wittgenstein's discursive perspective, which urges us to interpret the meaning of things said by a person in a research study by examining the function each utterance serves for the person. For example, one should avoid assuming that people respond to queries about understanding by simply describing their understanding. Instead, research participants might provide responses to achieve other goals, such as establishing a desired attitude in themselves. I suggest that both cautions can be addressed through careful qualitative investigation at the beginning of a research project.

The structure of medical decisions: uncertainty, probability and risk in five common choice situations

Increasingly, medical choices involve deciding whether to look for evidence of undetected, asymptomatic conditions, or increased risk of future conditions (i.e. screening). Those who screen at sufficiently high risk face decisions about interventions to prevent or postpone the onset of possible, but not certain, future symptomatic conditions. Other preventive decisions include whether or not to accept population-based intervention, such as vaccination. Using decision trees, we model the normative structures and associated uncertainties that underlie five medical decision situations, each of which involves assessing the probabilistic hypothesis that a person has, or will in the future have, a given symptomatic condition. The probability estimate that results from assessment becomes an input into predicting treatment benefit, with the probability of benefit decreasing as that of the symptomatic condition decreases. The five situations identified in this paper involve assessing: (1) a symptomatic patient; (2) an asymptomatic individual for an undetected condition; (3) an individual for risk of a future condition; (4) an individual for multiple risks simultaneously (shotgun assessment); and (5) an individual for a population-based intervention. Analysis of these situations facilitates examination of intuitive probabilistic reasoning. Drawing on evidence in related literature, we discuss some implications of decision-makers imposing the wrong structure or probabilistic reasoning when making medical choices. In particular, we discuss (1) overestimation of expected benefit due to systematic underestimation of uncertainty in a given decision; (2) overconfidence in probabilistic test results; and (3) failure to understand the implications of cumulative probabilities when ‘shot-gun’ testing.

Abstract 18743: Bayesian Comparative Analysis for Prevention of Stroke and Net Clinical Benefit of Novel Oral Anticoagulants in Non-Valvular Atrial Fibrillation

Background: The recent emergence of novel oral anticoagulants dabigatran, rivaroxaban and apixaban for prevention of stroke and systemic embolization in atrial fibrillation are attractive alternatives to warfarin. Despite this, the differences in efficacy and bleeding events between these medications remains unclear. To compare differential benefit, we performed a Bayesian analysis comparing the efficacy and safety parameters of the 3 novel agents. Methods: Using data from the 3 major trials investigating the novel oral anticoagulants, we conducted a Bayesian analysis using null prior probabilities, which is particularly suited to indirect comparisons. The primary endpoints of stroke and systemic embolization were assessed, along with safety endpoints of major bleeding. A Bayesian analysis was performed on each group, defining probabilities of benefit for the posterior probability. From this data, net clinical benefit calculations were performed and posterior probabilities were obtained. Results: Posterior probabilities for stroke and systemic embolization indicate the apixaban, rivaroxaban and dabigatran have an 87.3%, 88.7%, and 66.3% probability of benefit over warfarin, respectively. Major bleeding events were found to have a 98.6%, 88.0% and 55.0% probability of benefit over warfarin. Net clinical benefit analysis indicates benefit with apixaban and rivaroxaban that was greater than dabigatran. Conclusions: Using Bayesian analytic techniques, all of the novel oral anticoagulants were found to be non-inferior to warfarin. However, the effectiveness in prevention of stroke suggests that both apixaban and rivaroxaban significantly outperform dabigatran. Net clinic benefit and safety endpoints support the use of apixaban as opposed to rivaroxaban and dabigatran. While more data emerges investigating the differential benefit of these new medications, Bayesian techniques offer novel comparative statistics which can guide clinical decision making.

JS1-1 - Biomarker-Based Cancer Therapy Personalization

ABSTRACT The efficacy of cancer treatments varies significant from case to case, even if their tumors are categorized into the same histological subtypes and stages. Thus it would be necessary to identify a biomarker that could provide evidence about the probability of benefit or toxicity from a specific therapeutic intervention. EGFR gene mutation, for example, is a predictive biomarker for epidermal growth factor receptor tyrosine kinase inhibitors, but its identification is a rare successful case and only a limited number of predictive biomarkers are currently available for cancer therapeutics. Technologies such as microarray, high-speed sequencing, and mass spectrometry have advanced rapidly in recent years, allowing comprehensive analyses of the cancer genome and proteome. It is anticipated that the application of these technologies will contribute to the discovery of new cancer biomarkers. Antibody-based proteomics is a concept that emerged in alliance with the progression of genome-wide antibody production projects. The completion of proteome-scale antibody libraries is expected to enable not only rapid verification of biomarkers, but also direct screening of antibodies by high-throughput assays. Here we report new methods that can screen antibodies on the basis of their reactivity with a large number of patient samples with reasonable comprehensiveness and throughput: automated quantification of immunofluorescence tissue microarray and immunofluorescence reverse-phase protein microarray (RPPM). Using these antibody-based proteomics technologies, we were able to identify biomarkers that can predict the efficacy of combinational chemotherapy, radiotherapy, and molecular targeting therapy. We also discuss the potential application of the antibody-based proteomics technologies to early-phase drug development.

340P - Fulvestrant After Failur of Adjuvant Hormonal Therapy: Multicenter Retrospective Observational Study

ABSTRACT Background Aromatase inhibitors (AI) are superior to TAM in the treatment of hormonal receptor positive metastatic breast cancer (MBC). Fulvestrant (F), an estrogen receptor antagonist demonstrated activity when used after failure of previous TAM or AI. Objective: To evaluate the position of F in the hormonal strategy sequence in hormonal responsive MBC related to the evolution of adjuvant hormonal therapy (HT). Material and methods MBC patients with disease progression after adjuvant HT or after treatment (CT or HT) for metastatic disease, candidate to receive F at the dose of 250 mg/ month (registered in AIFA database) from May 1st 2006 to July 31st 2008. The study registered the HT sequence in clinical practice. Overall 201 patients were collected from 13 Italian Centers. Main patients' characteristics are summarized in the table. Table: 340P Characteristics N 201 (%) Characteristics N 201 (%) T size Tis-T1-T2 T3-T4 T Unk 138(68,6) 40 (20) 23 (11,4) PgR Pos Neg Unk 142(70,6) 33 (16,4) 26 (13) Nodal Status N0 N+ Nx 54 (26,9) 120 (59,7) 27 (13,4) Adj CT Yes No Unk 105 (51,7) 94 (46,8) 3 (1,5) ER Pos Neg Unk 169 (84) 12 (6) 20 (10) Adj HT Yes No Unk 141 (70,1) 46 (22,9) 14 (7) Results ORR were: CR 2%; PR 7%; SD 43%; PD 41%; NE 7%. Eighty-six patients (43%) had visceral metastases. Median duration of F was 7 months (1-42); 8 patients are ongoing and for 1 patient the duration was unknown. 16, 39 and 56 patients received F as 1st, 2nd and 3rd line therapy respectively. For the remaining patients F was administered as more than the 4th line therapy. The possibility to obtain a benefit from F did not seem to correlate with the line of treatment. One of the 2 CR occurred in a patient with visceral disease (lung). Among the patients who progressed under treatment 42% had visceral disease, that was almost the same rate of the whole population. This data suggests that there is lack of correlation between the sites of disease and the probability of benefit. Toxicity was negligible. Conclusions More than 50% of the patients benefited from F notwithstanding 73% of them received the treatment as third or more line of HT. This study does not show any correlation between the sites of the metastases and the treatment results. Disclosure All authors have declared no conflicts of interest.

Cardiovascular Disease and Oral Agent Glucose-Lowering Therapies in the Management of Type 2 Diabetes

Although glucose-lowering oral agents have been available for clinical use for over 60 years, the formal evidence base supporting their advantage and safety in regard of cardiovascular (CV) outcomes remains less than optimal. However, a synthesis of the evidence results in a high probability of benefit. For metformin, the United Kingdom Prospective Diabetes Study (UKPDS) substudy is convincing for a definite effect in reducing myocardial infarction (MI), but the quantitative extent of that is uncertain. For sulfonylureas, support for reduction in MI comes from the UKPDS extension study, where the central estimate for risk reduction remains the same as in the original planned end to the study, but the greater number of events was statistically significant for the sulfonylurea/insulin arm. Other studies do not support the view that metformin and sulfonylureas differ with respect to MI or indeed CV outcomes more generally. The data available for acarbose, an α-glucosidase inhibitor, are weak but not of concern, although some positive substudy data are available for people with impaired glucose tolerance. For peroxisome proliferator-activated receptor-γ agonists the CV data are more controversial, but the purpose-designed randomized controlled trials are clear that pioglitazone is advantageous to placebo (except for heart failure [HF]), whereas rosiglitazone is indistinguishable from metformin/sulfonylureas (even when including HF data). Lower-quality data do, however, lead to significant concerns for MI with rosiglitazone. Early and somewhat low-quality data for the dipeptidyl peptidase inhibitors show they are safe and hold promise for cardiovascular advantage, with major randomized controlled trials being underway. Preliminary CV data are available for one sodium/glucose cotransporter 2 inhibitor and look reassuring.

Does aggressive phototherapy increase mortality while decreasing profound impairment among the smallest and sickest newborns?

ObjectiveAggressive phototherapy (AgPT) is widely used and assumed to be safe and effective for even the most immature infants. We assessed whether the benefits and hazards for the smallest and sickest infants differed from those for other extremely low birth weight (ELBW; (≤1000 g) infants in our Neonatal Research Network trial, the only large trial of AgPT.Study DesignELBW infants (n=1974) were randomized to AgPT or conservative phototherapy at age 12–36 hours. The effect of AgPT on outcomes (death; impairment; profound impairment; death or impairment [primary outcome], and death or profound impairment) at 18–22 months corrected age was related to BW stratum (501–750 g; 751–1000 g) and baseline severity of illness using multilevel regression equations. The probability of benefit and of harm was directly assessed with Bayesian analyses.ResultsBaseline illness severity was well characterized using mechanical ventilation and FiO2 at 24 hours age. Among mechanically ventilated infants ≤750 g BW (n =684), a reduction in impairment and in profound impairment was offset by higher mortality (p for interaction <0.05) with no significant effect on composite outcomes. Conservative Bayesian analyses of this subgroup identified a 99% (posterior) probability that AgPT increased mortality, a 97% probability that AgPT reduced impairment, and a 99% probability that AgPT reduced profound impairment.ConclusionsFindings from the only large trial of AgPT suggest that AgPT may increase mortality while reducing impairment and profound impairment among the smallest and sickest infants. New approaches to reduce their serum bilirubin need development and rigorous testing.

Patient preferences for psoriasis treatment: process characteristics considered more important than outcome attributes

Evaluation of: Schaarschmidt ML, Schmieder A, Umar N et al. Patient preferences for psoriasis treatments. Process characteristics can outweigh outcome attributes. Arch. Dermatol. 147(11), 1285–1294 (2011).In a recent paper by Schaarschmidt et al., patients with moderate-to-severe psoriasis completed a conjoint analysis survey (discrete choice experiment) to determine which factors had influenced their choice of treatment, and the relative importance of each factor. They also assessed how socioeconomic and demographic factors affected patient choice. Using relative importance scores, treatment location was found to be the more important attribute to patients, followed by probability of benefit and method of delivery.

P3-17-09: Neutralizing the Prolactin Receptor with Therapeutic Antibody LFA102: A Novel Approach for the Treatment of Breast Cancer.

Abstract The prolactin receptor (PRLR) is a class I cytokine receptor required for the normal development of the mammary gland and is frequently found to be overexpressed in breast tumors. The polypeptide hormone prolactin (PRL) has been demonstrated to induce PRLR signaling through the Jak/Stat, PI3-kinase/AKT and MAPK pathways, leading to cell proliferation and survival. Mammary gland-specific overexpression of PRL in transgenic mice leads to a higher incidence of ER+ and ER- mammary tumors. In addition, the PRLR locus is the site of frequent viral integrations in MMTV-induced mammary tumors. Elevated serum PRL levels in humans have been correlated with an increased risk for breast cancer, especially for ER+ cases, implicating a role for this hormone in the development of human breast tumors. An analysis of more than 3000 breast tumor specimens indicates that PRLR is expressed with high prevalence (60-70% of tumors) across all breast cancer subtypes, with a trend towards higher expression in ER+ tumors. All of these lines of evidence support the hypothesis that targeting the PRL/PRLR axis may be a new approach for addressing unmet medical need in breast cancer. LFA102 is a Human Engineered™ anti-PRLR antibody of the IgG1 isotype that neutralizes the function of PRLR through a non-ligand competitive binding interaction. LFA102 blocks PRL-induced signaling and proliferation in T47D and MCF7 ER+ human breast cancer cells in vitro, and abolishes PRL-induced phosphorylation of Stat5 in T47D xenograft tumors in vivo. An examination of disaggregated primary human breast tumors ex vivo has indicated that PRL frequently induces signaling through Stat5 in the cells and that LFA102 is capable of completely antagonizing this signaling. LFA102 also neutralizes rat PRLR and the antibody potently regresses PRL-dependent Nb2-C11 pre-T cell lymphoma tumors in vivo. Preliminary data suggests that LFA102 is also capable of inhibiting the growth of carcinogen-induced rat mammary tumors. In vitro studies have shown that LFA102 can also mediate antibody-dependent cellular cytotoxicity (ADCC) and inhibit the PRL-dependent release of the pro-angiogenic factor VEGF from breast cancer cells. Thus, there are multiple potential mechanisms through which LFA102 could show anti-tumor activity in vivo. Preclinical toxicological studies of LFA102 indicate that this therapeutic is well tolerated and exhibits a normal pharmacokinetic profile in relevant animal species. The safety and pharmacokinetics of LFA102 in humans are currently being evaluated in a phase I healthy volunteer trial. At the three dose levels explored so far, no infusion reactions or severe adverse events related to the drug have been reported. Preliminary results suggest that LFA102 has an adequate pharmacokinetic profile for further clinical development. An assessment of LFA102 in a population of metastatic breast cancer patients predicted to have the highest probability of benefit is imminent. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-17-09.

Patient Preferences for Psoriasis Treatments

To assess patients' preferences for psoriasis treatments and to identify the effect of sociodemographic and socioeconomic characteristics on these preferences. A computer-based conjoint analysis experiment was conducted to analyze the preferences of individuals with moderate or severe psoriasis for outcome attributes (probability, magnitude, and duration of benefit, as well as probability, severity, and reversibility of adverse effects) and process attributes (treatment location, frequency, duration, delivery method, and individual cost) of psoriasis treatments. Relative importance scores (RISs) for each attribute were calculated. The effect of sociodemographic (age, sex, and marital status) and socioeconomic (income and employment) characteristics and Psoriasis Area and Severity Index and Dermatology Life Quality Index scores on preferences was assessed using analysis of variance, post hoc testing, and multivariate regression analysis. Outpatient dermatology clinic at a German university medical center. Patients with moderate or severe psoriasis (N = 163). Relative importance scores for treatment attributes. The attribute considered to be most important in patients' preferences for psoriasis treatments was treatment location (RIS, 26.76), followed by probability of benefit (RIS, 23.77) and method of delivery (RIS, 23.49). The RISs for all process attributes were higher than for adverse effect-related attributes. Older individuals (≥65 years) were less concerned about the probability of benefit (β = -0.24; P = .005) compared with younger individuals. When choosing among treatment options, individuals with psoriasis appear to be willing to accept treatment-related adverse effects to obtain process attributes compatible with their personal and professional life. Incorporating preferences in shared decision making may facilitate treatment adherence and optimize outcome.

Comorbidities significantly impact patients’ preferences for psoriasis treatments

Non-adherence rates are high among patients with psoriasis, partly because of discordance between recommended treatments and individual preferences. Our aim was to assess the impact of comorbidities on patients' preferences for psoriasis treatments. A computer-based conjoint analysis experiment was conducted to analyze preferences ofpatients with psoriasis (N= 163) for treatment outcome attributes (probability, magnitude and duration of benefit; probability, severity and reversibility of side effects) and process attributes (treatment location, frequency, duration, delivery method, individual cost). The impact of comorbidities (psoriatic arthritis, cardiovascular disease, diabetes, and depression) on relative importance scores of each attribute was assessed by analyses of variance, post hoc test, and multivariate regression analysis. Among the participants included (58.9% males, mean age 49.3 yrs), 27% suffered from psoriatic arthritis, 13.5% from cardiovascular disease, 8% from diabetes, and 12.9% from depression. Preferences for treatment attributes varied significantly depending on comorbidities. Participants with psoriatic arthritis cared most about the probability of benefit (β 0.166; P= .037), whereas those participants with cardiovascular disease were highly concerned about the probability of side effects (β 0.179; P= .046). For participants with depression, treatment duration (β 0.163; P= .047), and individual cost (P= .023) were highly important. Only patients with moderate and severe psoriasis treated at a university medical center were included. Integrating patients' preferences into shared decision-making may facilitate treatment adherence and optimize outcomes. Addressing patients' comorbidities, particularly depression, may be a currently neglected opportunity to improve care.

Personalizing Evidence-Based Primary Prevention With Aspirin

One of the unfortunate side consequences of evidence-based medicine (EBM) is that sometimes there is no escaping how small the benefit of any individual treatment may seem. In the “days of the giants,” doctors saved lives; now we can consult league tables that bloodlessly inform us how many patients—10, 50, or 200—must receive a treatment to avert even a single bad outcome,1 EBM's venerable number needed to treat. Article see p 268 The number needed to treat is, of course, based only on average treatment effects. A limitation of EBM is that we study groups of patients, whereas we treat individuals. This mismatch can be partially addressed by the use of statistical models to calculate the important risks that determine a more “personalized” probability of benefit. When this is done, it turns out, the degree of benefit for an intervention in any typical patient is often even less than the overall average.2 This is because benefits are determined by the baseline risk, which is usually highly skewed, such that a few high-risk patients may account for most of the benefit, whereas most patients have baseline risks (and therefore potential benefit) considerably less than the average.3 This is true particularly when the overall risk of the outcome of interest is low because there is a lower bound for risk at 0%, creating a “floor effect.” In this issue of Circulation: Cardiovascular Quality and Outcomes , Sussman et al4 describe the “personalized” risks and benefits of aspirin therapy for primary prevention of cardiovascular diseases, a rather extreme exemplar of the pattern described above. The number needed to treat to prevent a single ischemic event for any typical patient in a primary prevention population can easily exceed 1000 per year but may vary substantially, depending on individualized risk. Because …

Abstract LB-220: Molecular predictors of sensitivity to an IGF1R inhibitor in pre-clinical models of lung and colon cancers

Abstract Targeting the insulin like growth factor receptor (IGF1R) signaling pathway is a promising approach against a variety of cancers and several clinical trials are now underway designed to evaluate the efficacy of IGF1R inhibitors singly or in combination with chemotherapeutic agents. Identification of the optimal patient population likely to respond is critical to the success of this class of targeted agents. To identify potentially predictive biomarkers, we sought to correlate response to figitumumab (CP-751,871), a fully human monoclonal antibody against IGF1R with molecular characteristics across a panel of cell lines. Growth inhibition to CP-751,871 was assessed in a panel of non-small cell lung carcinoma (NSCLC) (n=27) and colon cancer cell lines (n=21) cultured in anchorage and anchorage-independent conditions. Mutation status, whole-genome mRNA expression and DNA copy number were correlated to drug sensitivity. In addition, RNA expression of key genes in relevant signaling pathways was also interrogated via real time PCR. In the present studies, the frequency of response was greater in colon cancer cell lines than in NSCLC. Tissue or cell origin-specific expression levels of insulin-like growth factor binding proteins (IGFBPs) were associated with sensitivity. Low IGFBP3 expression [p=0.0035] correlated with response in NSCLC cell lines whereas low IGFBP6 [p=0.013] and IGF2BP3 [p=0.038] expression correlated with response in colon cancer lines. Increased IGF1R mRNA [p=0.045] levels, microsatellite stability, and PIK3CA wild-type status were also correlated with sensitivity in colon cancer lines. In addition, high expression of erbB2 [p=0.04] and erbB3 [p=0.04] were also associated with sensitivity of colon cancer lines. Collectively, findings from these studies are of potential value to identify patient populations with increased probability of benefit from IGF1R targeted agents. Our results further indicate that molecular predictors associated with the IGF1R pathway are potentially distinct between lung and colon cancer lines and additional studies to extend to other tumor types are warranted. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-220.

Estimating the Danish Populations' Preferences for Pharmacogenetic Testing Using a Discrete Choice Experiment. The Case of Treating Depression

ObjectiveThe objective is to estimate willingness-to-pay (WTP) for pharmacogenetic testing in the treatment of depression. MethodsIn a web-based discrete choice questionnaire, four attributes were included: 1) number of changes in antidepressants before symptom relief; 2) time with dosage adjustments due to adverse side effects and/or lack of effects; 3) cost of pharmacogenetic testing; 4) probability of benefits from pharmacogenetic testing. Respondents were asked to choose between two scenarios; 1) pharmacogenetic testing; and 2) an opt-out option reflecting a scenario without pharmacogenetic testing. The indirect utility model was assumed to be multiplicative in probability of benefits and reduced time with dosage adjustments as well as reduced number of antidepressant changes. ResultsMost coefficients had the expected signs and were statistically significant. WTP for avoidance of one change in antidepressant medication is 1571 Danish Krone (DKK), whereas WTP for reducing the period with dosage-adjustments by 1 month is DKK604. Both were statistically significantly different from zero. ConclusionIf diagnosed with depression, peoples' WTP for pharmacogenetic testing appears to exceed its price as long as there is a reasonable probability for improvements in treatment (in the present case 10%). Utility is associated with outcomes only. Hence, other modes of provision of similar improvements in treatment may be valued equally highly. WTP estimates and the associated policy implications appear to be robust because they were unaffected by estimation model.

Assessing multiple medication use with probabilities of benefits and harms.

A quantitative framework to assess harms and benefits of candidate medications in the context of drugs that a patient is already taking is proposed. Probabilities of harms and benefits of a given medication are averaged to yield a utility value. The utility values of all medications under consideration are combined as a geometric mean to yield an overall measure of favorability. The grouping of medications yielding the highest favorability value is chosen. Five examples of choosing between widely used candidate medications demonstrate the feasibility of the proposed framework. The framework proposed provides a simple method for considering the trade-offs involved in prescribing multiple medications. It can be adapted to include additional parameters representing severity of condition, prioritization of outcomes, patient preferences, dosages, and medication interactions. Inconsistent reporting in the medical literature of data about benefits and harms of medications, dosages, and interactions constitutes its primary limitation.

Benefit and Risk of Mammography Screening – Considerations from an Epidemiological Viewpoint: Judgement and Decision Processes

According to the authors, many women may choose to participate because they have followed the principle of multiplying the low probability of the adverse event in question (death from breast cancer) by the high magnitude, as it were, of the event (death). It is certainly true that many women would still choose to undergo mammography even if they knew that a healthy woman’s chance of avoiding death from cancer with the aid of early detection is low. Yet precisely this fact is withheld from women in the informational materials that they are generally given in the German-speaking countries. Thus, they are not in a position to make the actuarial evaluation described. The likelihood of a catastrophically adverse event, such as overdiagnosis followed by overtreatment, is likewise low but obviously should also be part of the evaluation and decision process. It has been well documented empirically that decisions depend on the nature and extent of the information provided. Presenting the effect of the intervention only in terms of relative risk creates an unrealistically positive impression (1). Patients consider themselves well-informed if they know absolute numbers and absolute risks (2). Varying presentations of identical facts – half empty versus half full – can lead patients to make different decisions. I think there can only be one conclusion: the judgement about the probabilities of benefit and harm should be left up to each individual woman on the basis of comprehensive information that does not predetermine her ultimate decision. There is no good reason to withhold understandable information from women, as has been done up to the present. Understandable information that should be communicated includes at least the following: the risk of breast cancer in the individual woman’s age group, the likelihood of avoiding death from cancer by early detection, the rates of false positive and false negative findings, and the danger of overdiagnosis leading to overtreatment (3).

Why are clinicians not embracing the results from pivotal clinical trials in severe sepsis? A bayesian analysis.

BackgroundFive pivotal clinical trials (Intensive Insulin Therapy; Recombinant Human Activated Protein C [rhAPC]; Low-Tidal Volume; Low-Dose Steroid; Early Goal-Directed Therapy [EGDT]) demonstrated mortality reduction in patients with severe sepsis and expert guidelines have recommended them to clinical practice. Yet, the adoption of these therapies remains low among clinicians.ObjectivesWe selected these five trials and asked: Question 1-What is the current probability that the new therapy is not better than the standard of care in my patient with severe sepsis? Question 2-What is the current probability of reducing the relative risk of death (RRR) of my patient with severe sepsis by meaningful clinical thresholds (RRR >15%; >20%; >25%)?MethodsBayesian methodologies were applied to this study. Odds ratio (OR) was considered for Question 1, and RRR was used for Question 2. We constructed prior distributions (enthusiastic; mild, moderate, and severe skeptic) based on various effective sample sizes of other relevant clinical trials (unfavorable evidence). Posterior distributions were calculated by combining the prior distributions and the data from pivotal trials (favorable evidence).Main FindingsAnswer 1-The analysis based on mild skeptic prior shows beneficial results with the Intensive Insulin, rhAPC, and Low-Tidal Volume trials, but not with the Low-Dose Steroid and EGDT trials. All trials' results become unacceptable by the analyses using moderate or severe skeptic priors. Answer 2-If we aim for a RRR>15%, the mild skeptic analysis shows that the current probability of reducing death by this clinical threshold is 88% for the Intensive Insulin, 62–65% for the Low-Tidal Volume, rhAPC, EGDT trials, and 17% for the Low-Dose Steroid trial. The moderate and severe skeptic analyses show no clinically meaningful reduction in the risk of death for all trials. If we aim for a RRR >20% or >25%, all probabilities of benefits become lower independent of the degree of skepticism.ConclusionsOur clinical threshold analysis offers a new bedside tool to be directly applied to the care of patients with severe sepsis. Our results demonstrate that the strength of evidence (statistical and clinical) is weak for all trials, particularly for the Low-Dose Steroid and EGDT trials. It is essential to replicate the results of each of these five clinical trials in confirmatory studies if we want to provide patient care based on scientifically sound evidence.

Does Gefitinib Shorten Lung Cancer Survival? Chaos Redux

Does Gefitinib Shorten Lung Cancer Survival? Chaos Redux