Weighted thyroid-stimulating hormone disturbance in prognosis of hepatitis B virus-related acute-on-chronic liver failure.

AIM: To weight the prognostic value of thyroid hormones in catastrophic acute-on-chronic liver failure (ACLF). METHODS: A retrospective cohort (n = 635) and two prospective cohorts (n = 353, and 198) were enrolled in this study. The performance of a novel developed prognostic score was assessed from aspects of reliability, discrimination, and clinical net benefit. RESULTS: Thyroid-stimulating hormone (TSH) was identified to have the most potential as a prognostic predictor for hepatitis B virus-related ACLF among thyroid hormones. The novel score (modified chronic liver failure-organ failure score [mCLIF-OFs]) was developed with weighted TSH and other scored organs in the CLIF-OFs using the retrospective cohort (n = 635). The predicted risk and observed probabilities of death were comparable across the deciles of mCLIF-OFs (Hosmer-Lemeshow χ2  = 4.28, p = 0.83; Brier scaled = 11.9). The C-index of mCLIF-OFs (0.885 [0.883-0.887]) for 30-day mortality was significantly higher than that of the CLIF-OFs, chronic liver failure-sequential organ failure assessment score (CLIF-SOFAs), CLIF-C ACLFs, Model of End-stage Liver Disease (MELD), and Child-Pugh (all p < 0.001). The absolute improvements of prediction error rates of the mCLIF-OFs compared to the above five scores were from 19.0% to 61.1%. After the analysis of probability density function, the mCLIF-OFs showed the least overlapping coefficients (27.9%) among the above prognostic scores. Additionally, the mCLIF-OFs showed greater net benefit than the above five prognostic scores over a wide range of risk threshold of death. Similar results were validated in two prospective ACLF cohorts with HBV and non-HBV etiologies. CONCLUSION: Weighted TSH portended the outcome of ACLF patients, which could be treated as a "damaged organ" of the hypothalamic-pituitary-thyroid axis. The novel mCLIF-OFs is a reliable prognostic score with better discrimination power and clinical net benefit than CLIF-OFs, CLIF-SOFAs, CLIF-C ACLFs, MELD, and Child-Pugh.

Evaluating the clinical utility of an easily applicable prediction model of suicide attempts, newly developed and validated with a general community sample of adults.

BACKGROUND: A suicide attempt (SA) is a clinically serious action. Researchers have argued that reducing long-term SA risk may be possible, provided that at-risk individuals are identified and receive adequate treatment. Algorithms may accurately identify at-risk individuals. However, the clinical utility of algorithmically estimated long-term SA risk has never been the predominant focus of any study. METHODS: The data of this report stem from CoLaus|PsyCoLaus, a prospective longitudinal study of general community adults from Lausanne, Switzerland. Participants (N = 4,097; Mage = 54 years, range: 36-86; 54% female) were assessed up to four times, starting in 2003, approximately every 4-5 years. Long-term individual SA risk was prospectively predicted, using logistic regression. This algorithm's clinical utility was assessed by net benefit (NB). Clinical utility expresses a tool's benefit after having taken this tool's potential harm into account. Net benefit is obtained, first, by weighing the false positives, e.g., 400 individuals, at the risk threshold, e.g., 1%, using its odds (odds of 1% yields 1/(100-1) = 1/99), then by subtracting the result (400*1/99 = 4.04) from the true positives, e.g., 5 individuals (5-4.04), and by dividing the result (0.96) by the sample size, e.g., 800 (0.96/800). All results are based on 100 internal cross-validations. The predictors used in this study were: lifetime SA, any lifetime mental disorder, sex, and age. RESULTS: SA at any of the three follow-up study assessments was reported by 1.2%. For a range of seven a priori selected threshold probabilities, ranging between 0.5% and 2%, logistic regression showed highest overall NB in 97.4% of all 700 internal cross-validations (100 for each selected threshold probability). CONCLUSION: Despite the strong class imbalance of the outcome (98.8% no, 1.2% yes) and only four predictors, clinical utility was observed. That is, using the logistic regression model for clinical decision making provided the most true positives, without an increase of false positives, compared to all competing decision strategies. Clinical utility is one among several important prerequisites of implementing an algorithm in routine practice, and may possibly guide a clinicians' treatment decision making to reduce long-term individual SA risk. The novel metric NB may become a standard performance measure, because the a priori invested clinical considerations enable clinicians to interpret the results directly.

Commentary: Value of information case study strongly supports use of the Threshold of Toxicological Concern (TTC).

A Value of Information (VOI) analysis can play a key role in decision-making for adopting new approach methodologies (NAMs). We applied EPA's recently developed VOI framework to the Threshold of Toxicological Concern (TTC). Obtaining/deriving a TTC value for use as a toxicity reference value (TRV) for substances with limited toxicity data was shown to provide equivalent or greater health protection, immense return on investment (ROI), greater net benefit, and substantially lower costs of delay (CoD) compared with TRVs derived from either traditional human health assessment (THHA) chronic toxicity testing in lab animals or the 5-day in vivo EPA Transcriptomic Assessment Product (ETAP). For all nine exposure scenarios examined, the TTC was more economical terms of CoD and ROI than the ETAP or the THHA; expected net benefit was similar for the TTC and ETAP with both of these more economical than the THHA The TTC ROI was immensely greater (5,000,000-fold on average) than the ROI for THHA and the ETAP ROI (100,000-fold on average). These results support the use of the TTC for substances within its domain of applicability to waive requiring certain in vivo tests, or at a minimum, as an initial screening step before conducting either the ETAP or THHA in vivo studies.

Understanding decision curve analysis in clinical prediction model research.

BACKGROUND: Many medical graduate students lack a thorough understanding of decision curve analysis (DCA), a valuable tool in clinical research for evaluating diagnostic models. METHODS: This article elucidates the concept and process of DCA through the lens of clinical research practices, exemplified by its application in diagnosing liver cancer using serum alpha-fetoprotein levels and radiomics indices. It covers the calculation of probability thresholds, computation of net benefits for each threshold, construction of decision curves, and comparison of decision curves from different models to identify the one offering the highest net benefit. RESULTS: The paper provides a detailed explanation of DCA, including the creation and comparison of decision curves, and discusses the relationship and differences between decision curves and receiver operating characteristic curves. It highlights the superiority of decision curves in supporting clinical decision-making processes. CONCLUSION: By clarifying the concept of DCA and highlighting its benefits in clinical decisionmaking, this article has improved researchers' comprehension of how DCA is applied and interpreted, thereby enhancing the quality of research in the medical field.

Health-Economic Evaluation of the German Osteoporotic Fracture Prevention Program in Rural Areas (OFRA): Mobility and Falls Prevention Classes, Examination of Bone Health, and Consultation on Safety in the Living Environment.

BACKGROUND: Fragility fractures are one of the leading causes of disability in older adults. Yet, evidence for effectiveness and cost-effectiveness of preventive approaches combining bone health and fall prevention is rare. OBJECTIVE: To conduct a health-economic evaluation of the German osteoporotic fracture prevention program in rural areas (OFRA). DESIGN: Secondary cluster-randomized intervention study based on routine data. PARTICIPANTS: All districts in five federal states in Germany were cluster-randomized as intervention or control districts. OFRA was offered to community-living (a) women aged 75-79 years or (b) women and men aged 70-84 years with a prior fragility fracture in the intervention districts. Individuals who meet these criteria in the control districts were assigned to the control group. INTERVENTION: OFRA comprised mobility and falls prevention classes, examination of bone health by bone density measurement, and consultation on safety in the home living environment. MAIN MEASURES: We measured health-care costs and effectiveness in terms of time to fragility fracture or death within 1 year after initial contact, based on health insurance claims data. Implementation costs were recorded by the intervention performers. We calculated an incremental cost-effectiveness ratio (ICER) and employed the net-benefit approach to construct a cost-effectiveness acceptability curve (CEAC). KEY RESULTS: There were 9408 individuals in the intervention group and 27,318 in the control group. Mean time to fragility fracture or death (difference: 0.82 days) and health-care costs (difference: 111.73€, p < .01) were reduced, but mean intervention costs (difference: 260.10€) increased total costs (difference: 148.37€, p < .001) in the intervention group. The ICER per fracture-free year of survival was 66,094.63€. The CEAC showed no acceptable probability of cost-effectiveness at a reasonable willingness to pay. CONCLUSION: OFRA showed reduced rates of fragility fractures, but had high implementation costs, resulting in an unfavorable ICER. The cost-effectiveness of OFRA may improve with a longer follow-up.

Impact of correlations between prioritized outcomes on the net benefit and its estimate by generalized pairwise comparisons.

Benefit-risk balance is gaining interest in clinical trials. For the comprehensive assessment of benefits and risks, generalized pairwise comparisons are increasingly used to estimate the net benefit based on multiple prioritized outcomes. Although previous research has demonstrated that the correlations between the outcomes impact the net benefit and its estimate, the direction and magnitude of this impact remain unclear. In this study, we investigated the impact of correlations between two binary or Gaussian variables on the true net benefit values via theoretical and numerical analyses. We also explored the impact of correlations between survival and categorical variables on the net benefit estimates based on four existing methods (Gehan, Péron, Gehan with correction, and Péron with correction) in the presence of right censoring via simulation and application to actual oncology clinical trial data. Our theoretical and numerical analyses revealed that the true net benefit values were impacted by the correlations in various directions depending on the outcome distributions. With binary endpoints, this direction was governed by a simple rule with a threshold of 50% for a favorable outcome. Our simulation showed that the net benefit estimates based on Gehan's or Péron's scoring rule could be substantially biased in the presence of right censoring, and that the direction and magnitude of this bias were associated with the outcome correlations. The recently proposed correction method greatly reduced this bias, even in the presence of strong outcome correlations. The impact of correlations should be carefully considered when interpreting the net benefit and its estimate.

Individualized Net Benefit estimation and meta-analysis using generalized pairwise comparisons in N-of-1 trials.

BACKGROUND: The Net Benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes and thresholds of clinical relevance. We extended Δ to N-of-1 trials, with a focus on patient-level and population-level Δ. METHODS: We developed a Δ estimator at the individual level as an extension of the stratum-specific Δ, and at the population-level as an extension of the stratified Δ. We performed a simulation study mimicking PROFIL, a series of 38 N-of-1 trials testing sildenafil in Raynaud's phenomenon, to assess the power for such an analysis with realistic data. We then reanalyzed PROFIL using GPC. This reanalysis was finally interpreted in the context of the main analysis of PROFIL which used Bayesian individual probabilities of efficacy. RESULTS: Simulations under the null showed good size of the test for both individual and population levels. The test lacked power when being simulated from the true PROFIL data, even when increasing the number of repetitions up to 140 days per patient. PROFIL individual-level estimated Δ were well correlated with the probabilities of efficacy from the Bayesian analysis while showing similarly wide confidence intervals. Population-level estimated Δ was not significantly different from zero, consistently with the previous Bayesian analysis. CONCLUSION: GPC can be used to estimate individual Δ which can then be aggregated in a meta-analytic way in N-of-1 trials. GPC ability to easily incorporate patient preferences allow for more personalized treatment evaluation, while needing much less computing time than Bayesian modeling.

Pneumococcal Vaccination in Children: A Systematic Review and Meta-Analysis of Cost-Effectiveness Studies.

OBJECTIVES: Pneumococcal conjugate vaccines (PCVs) have significantly reduced disease burden caused by Streptococcus pneumoniae, a leading cause of childhood morbidity and mortality globally. This systematic review and meta-analysis aimed to assess the incremental net benefit (INB) of the 13-valent PCV (PCV13) and 10-valent PCV (PCV10) in children. METHODS: We performed a comprehensive search in several databases published before May 2022. Studies were included if they were cost-effectiveness or cost-utility analyses of PCV13 or PCV10 compared with no vaccination or with each other in children. Various monetary units were converted to purchasing power parity, adjusted to 2021 US dollars. The INBs were calculated and then pooled across studies stratified by country income level, perspective, and consideration of herd effects, using a random-effect model. RESULTS: Seventy studies were included. When herd effects were considered, PCV13 was cost-effective compared with PCV10 from the payer perspective in both high-income countries (HICs) (INB, $103.94; 95% confidence interval, $75.28-$132.60) and low- and middle-income countries (LMICs) (INB, $53.49; 95% confidence interval, $30.42-$76.55) with statistical significance. These findings were robust across a series of sensitivity analyses. PCV13 was cost-effective compared with no vaccination across perspectives and consideration of herd effects in both HICs and LMICs, whereas findings were less consistent for PCV10. CONCLUSION: PCVs were generally cost-effective compared with no vaccination in HICs and LMICs. Our study found that PCV13 was cost-effective compared with PCV10 when herd effects were considered from the payer perspective in both HICs and LMICs. The results are sensitive to the consideration of herd effects.

Incremental net monetary benefit of biologic therapies in moderate to severe asthma: a systematic review and meta-analysis of economic evaluation studies.

OBJECTIVES: This meta-analysis was conducted to quantitatively pool the incremental net benefit (INB) of using biologic therapies as an add-on treatment to standard therapy in patients with moderate to severe asthma. METHODS: We performed a comprehensive search in several databases published until April 2022. Studies were included if they were cost-effectiveness analyses reporting cost per quality-adjusted life-year or life-year on any biologic therapies as an add-on treatment for moderate to severe asthma in patients of all ages. Various monetary units were converted to purchasing power parity, adjusted to 2021 US dollars. The INBs were pooled across studies using a random-effects model, stratified by country income level (high-income countries (HICs) and low- and middle-income countries (LMICs)) and perspectives (health care or payer perspective (HCPP) and societal perspective (SP)) and age group (>12 years and 6-11 years). Heterogeneity was assessed using the I2 statistic. RESULTS: A total of 32 comparisons from 25 studies were included. Pooled INB indicated that the use of omalizumab as an add-on treatment to standard therapy in those aged >12 years was not cost-effective in HICs from the HCPP (n = 8, INB, -6,341 (95% CI, -$25,000 to $12,210), I2=86.18%) and SP (n = 5, -$14,000 (-$170,000 to $140,000), I2=75.64%). A similar finding was observed in those aged 6-11 years from the HCPP in LMICs (n = 2, -$45,000 (-$73,000 to $17,000), I2=00.00%). Subgroup analyses provided no explanations of the potential sources of heterogeneity. CONCLUSION: The use of biologic therapies in moderate to severe asthma is not cost-effective compared to standard treatment alone.

The win odds: statistical inference and regression.

Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. An important limitation of these statistics is their inability to adjust for covariates other than by stratified analysis. Because the win ratio does not account for ties, the win odds, a modification that includes ties, has attracted attention. We review and combine information on the win odds to articulate the statistical inferences for the win odds. We also show alternative variance estimators based on the exact permutation and bootstrap as well as statistical inference via the probabilistic index. Finally, we extend multiple-covariate regression probabilistic index models to the win odds with a univariate outcome. As an illustration we apply the regression models to the data in the CHARM trial.

Net benefit of diagnostic tests for multistate diseases: an indicator variables approach.

A limitation of the common measures of diagnostic test performance, such as sensitivity and specificity, is that they do not consider the relative importance of false negative and false positive test results, which are likely to have different clinical consequences. Therefore, the use of classification or prediction measures alone to compare diagnostic tests or biomarkers can be inconclusive for clinicians. Comparing tests on net benefit can be more conclusive because clinical consequences of misdiagnoses are considered. The literature suggested evaluating the binary diagnostic tests based on net benefit, but did not consider diagnostic tests that classify more than two disease states, e.g., stroke subtype (large-artery atherosclerosis, cardioembolism, small-vessel occlusion, stroke of other determined etiology, stroke of undetermined etiology), skin lesion subtype, breast cancer subtypes (benign, mass, calcification, architectural distortion, etc.), METAVIR liver fibrosis state (F0- F4), histopathological classification of cervical intraepithelial neoplasia (CIN), prostate Gleason grade, brain injury (intracranial hemorrhage, mass effect, midline shift, cranial fracture) . Other diseases have more than two stages, such as Alzheimer's disease (dementia due to Alzheimer's disease, mild cognitive disability (MCI) due to Alzheimer's disease, and preclinical presymptomatics due to Alzheimer's disease). In diseases with more than two states, the benefits and risks may vary between states. This paper extends the net-benefit approach of evaluating binary diagnostic tests to multi-state clinical conditions to rule-in or rule-out a clinical condition based on adverse consequences of work-up delay (due to false negative test result) and unnecessary workup (due to false positive test result). We demonstrate our approach with numerical examples and real data.

Evidence synthesis analysis with prioritized benefit outcomes in oncology clinical trials.

Overall survival, progression-free survival, objective response/complete response, and duration of (complete) response are frequently used as the primary and secondary efficacy endpoints for designs and analyses of oncology clinical trials. However, these endpoints are typically analyzed separately. In this article, we introduce an evidence synthesis approach to prioritize the benefit outcomes by applying the generalized pairwise comparisons (GPC) method, and use win statistics (win ratio, win odds and net benefit) to quantify treatment benefit. Under the framework of GPC, the main advantage of this evidence synthesis approach is the ability to combine relevant outcomes of various types into a single summary statistic without relying on any parametric assumptions. It is particularly relevant since health authorities and the pharmaceutical industry are increasingly incorporating structured quantitative methodologies in their benefit-risk assessment. We apply this evidence synthesis approach to an oncology phase 3 study in first-line renal cell carcinoma to assess the overall effect of an investigational treatment by ranking the most clinically relevant endpoints in cancer drug development. This application and a simulation study demonstrate that the proposed approach can synthesize the evidence of treatment effect from multiple prioritized benefit outcomes, and has substantial advantage over conventional methods that analyze each individual endpoint separately. We also introduce a newly developed R package WINS for statistical inference based on win statistics.

Moderators of the cost-effectiveness of transdiagnostic CBT for anxiety disorders over an 8-month time horizon using a net-benefit regression framework.

BACKGROUND: Access to evidence-based psychological treatment is a concern in many parts of the globe due to government-level financial constraints and patient-level barriers. Transdiagnostic cognitive behavioural therapy (tCBT) is an effective treatment approach that uses a single protocol for anxiety disorders which could enhance the dissemination of evidence-based psychotherapy. In a context of limited resources, the study of treatment moderators can allow to identify subgroups for which the cost-effectiveness of an intervention differs, information that could impact decision-making. So far, there has been no economic evaluation of tCBT for different subpopulations. The objectives of this study, using the net-benefit regression framework, were to explore clinical and sociodemographic factors as potential moderators of the cost-effectiveness of tCBT compared to treatment-as-usual (TAU). METHODS: This is a secondary data analysis of a pragmatic randomized controlled trial opposing tCBT added to TAU (n = 117) to TAU only (n = 114). Data on costs from the health system and the limited societal perspectives, as well as anxiety-free days, an effectiveness measure based on the Beck Anxiety Inventory, were collected over an 8-month time horizon and used to derive individual net-benefits. The net-benefit regression framework was used to assess moderators of the cost-effectiveness of tCBT + TAU as opposed to TAU alone. Variables of sociodemographic and clinical nature were assessed. RESULTS: Results showed that the number of comorbid anxiety disorders significantly moderated the cost-effectiveness of tCBT + TAU compared to TAU from the limited societal perspective. CONCLUSIONS: The number of comorbid anxiety disorders was identified as a moderator affecting the cost-effectiveness of tCBT + TAU compared to TAU from the limited societal perspective. More research is needed to strengthen the case of tCBT from an economic standpoint for large-scale dissemination. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02811458, 23/06/2016.

Patient selection for long-term secondary prevention with ticagrelor: insights from PEGASUS-TIMI 54.

AIM: In patients with prior myocardial infarction (MI) on aspirin, the addition of ticagrelor reduces ischaemic risk but increases bleeding risk. The simultaneous assessment of baseline ischaemic and bleeding risk may assist clinicians in selecting patients who are most likely to have a favourable risk/benefit profile with long-term ticagrelor. METHODS AND RESULTS: PEGASUS-TIMI 54 randomized 21 162 prior MI patients, 13 956 of which to the approved 60 mg dose or placebo and who had all necessary data. The primary efficacy endpoint was cardiovascular death, MI, or stroke, and the primary safety outcome was TIMI major bleeding; differences in Kaplan-Meier event rates at 3 years are presented. Post-hoc subgroups based on predictors of bleeding and ischaemic risk were merged into a selection algorithm. Patients were divided into four groups: those with a bleeding predictor (n = 2721, 19%) and then those without a bleeding predictor and either 0-1 ischaemic risk factor (IRF; n = 3004, 22%), 2 IRF (n = 4903, 35%), or ≥3 IRF (n = 3328, 24%). In patients at high bleeding risk, ticagrelor increased bleeding [absolute risk difference (ARD) +2.3%, 95% confidence interval (CI) 0.6, 3.9] and did not reduce the primary efficacy endpoint (ARD +0.08%, 95% CI -2.4 to 2.5). In patients at low bleeding risk, the ARDs in the primary efficacy endpoint with ticagrelor were -0.5% (-2.2, 1.3), -1.5% (-3.1, 0.02), and -2.6% (-5.0, -0.24, P = 0.03) in those with ≤1, 2, and 3 risk factors, respectively (P = 0.076 for trend across groups). There were significant trends for greater absolute risk reductions for cardiovascular death (P-trend 0.018), all-cause mortality (P-trend 0.027), and net outcomes (P-trend 0.037) with ticagrelor across these risk groups. CONCLUSION: In a post-hoc exploratory analysis of patients with prior MI, long-term ticagrelor therapy appears to be best suited for those with prior MI with multiple IRFs at low bleeding risk. CLINICAL TRIAL REGISTRATION: NCT01225562 ClinicalTrials.gov.

The stratified win statistics (win ratio, win odds, and net benefit).

The win odds and the net benefit are related directly to each other and indirectly, through ties, to the win ratio. These three win statistics test the same null hypothesis of equal win probabilities between two groups. They provide similar p-values and powers, because the Z-values of their statistical tests are approximately equal. Thus, they can complement one another to show the strength of a treatment effect. In this article, we show that the estimated variances of the win statistics are also directly related regardless of ties or indirectly related through ties. Since its introduction in 2018, the stratified win ratio has been applied in designs and analyses of clinical trials, including Phase III and Phase IV studies. This article generalizes the stratified method to the win odds and the net benefit. As a result, the relations of the three win statistics and the approximate equivalence of their statistical tests also hold for the stratified win statistics.

Generalized pairwise comparisons for censored data: An overview.

The method of generalized pairwise comparisons (GPC) is an extension of the well-known nonparametric Wilcoxon-Mann-Whitney test for comparing two groups of observations. Multiple generalizations of Wilcoxon-Mann-Whitney test and other GPC methods have been proposed over the years to handle censored data. These methods apply different approaches to handling loss of information due to censoring: ignoring noninformative pairwise comparisons due to censoring (Gehan, Harrell, and Buyse); imputation using estimates of the survival distribution (Efron, Péron, and Latta); or inverse probability of censoring weighting (IPCW, Datta and Dong). Based on the GPC statistic, a measure of treatment effect, the "net benefit," can be defined. It quantifies the difference between the probabilities that a randomly selected individual from one group is doing better than an individual from the other group. This paper aims at evaluating GPC methods for censored data, both in the context of hypothesis testing and estimation, and providing recommendations related to their choice in various situations. The methods that ignore uninformative pairs have comparable power to more complex and computationally demanding methods in situations of low censoring, and are slightly superior for high proportions (>40%) of censoring. If one is interested in estimation of the net benefit, Harrell's c index is an unbiased estimator if the proportional hazards assumption holds. Otherwise, the imputation (Efron or Peron) or IPCW (Datta, Dong) methods provide unbiased estimators in case of proportions of drop-out censoring up to 60%.

Net-benefit regression with censored cost-effectiveness data from randomized or observational studies.

Cost-effectiveness analysis is an essential part of the evaluation of new medical interventions. While in many studies both costs and effectiveness (eg, survival time) are censored, standard survival analysis techniques are often invalid due to the induced dependent censoring problem. We propose methods for censored cost-effectiveness data using the net-benefit regression framework, which allow covariate-adjustment and subgroup identification when comparing two intervention groups. The methods provide a straightforward way to construct cost-effectiveness acceptability curves with censored data. We also propose a more efficient doubly robust estimator of average causal incremental net benefit, which increases the likelihood that the results will represent a valid inference in observational studies. Lastly, we conduct extensive numerical studies to examine the finite-sample performance of the proposed methods, and illustrate the proposed methods with a real data example using both survival time and quality-adjusted survival time as the measures of effectiveness.

The Net Benefit of Personalized Medicine: A Systematic Literature Review and Regression Analysis.

OBJECTIVES: Amidst conflicting expectations about the benefits of personalized medicine (PM) and the potentially high implementation costs, we reviewed the available evidence on the cost-effectiveness of PM relative to non-PM. METHODS: We conducted a systematic literature review of economic evaluations of PM and extracted data, including incremental quality-adjusted life-years (ΔQALYs) and incremental costs (Δcosts). ΔQALYs and Δcosts were combined with estimates of national cost-effectiveness thresholds to calculate incremental net monetary benefit (ΔNMB). Regression analyses were performed with these variables as dependent variables and PM intervention characteristics as independent variables. Random intercepts were used to cluster studies according to country. RESULTS: Of 4774 studies reviewed, 128 were selected, providing cost-effectiveness data for 279 PM interventions. Most studies were set in the United States (48%) and the United Kingdom (16%) and adopted a healthcare perspective (82%). Cancer treatments (60%) and pharmaceutical interventions (72%) occurred frequently. Prognostic tests (19%) and tests to identify (non)responders (37%) were least and most common, respectively. Industry sponsorship occurred in 32%. Median ΔQALYs, Δcosts, and ΔNMB per individual were 0.03, Int$575, and Int$18, respectively. We found large heterogeneity in cost-effectiveness. Regression analysis showed that gene therapies were associated with higher ΔQALYs than other interventions. PM interventions for neoplasms brought higher ΔNMB than PM interventions for other conditions. Nonetheless, average ΔNMB in the 'neoplasm' group was found to be negative. CONCLUSIONS: PM brings improvements in health but often at a high cost, resulting in 0 to negative ΔNMB on average. Pricing policies may be needed to reduce the costs of interventions with negative ΔNMB.

Is My Clinical Prediction Model Clinically Useful? A Primer on Decision Curve Analysis.

Decision curve analysis is an increasingly popular method to assess the impact of a prediction model on medical decision making. The analysis provides a graphical summary. A basic understanding of a decision curve is needed to interpret these graphics. This short introduction addresses the common features of a decision curve. Furthermore, using a glioblastoma patient set provided by the Machine Intelligence in Clinical Neuroscience Lab from the Department of Neurosurgery and Clinical Neuroscience Center, University Hospital Zurich a decision curve is plotted for two prediction models. The corresponding R code is provided.

Operational characteristics of generalized pairwise comparisons for hierarchically ordered endpoints.

The method of generalized pairwise comparisons (GPC) is a multivariate extension of the well-known non-parametric Wilcoxon-Mann-Whitney test. It allows comparing two groups of observations based on multiple hierarchically ordered endpoints, regardless of the number or type of the latter. The summary measure, "net benefit," quantifies the difference between the probabilities that a random observation from one group is doing better than an observation from the opposite group. The method takes into account the correlations between the endpoints. We have performed a simulation study for the case of two hierarchical endpoints to evaluate the impact of their correlation on the type-I error probability and power of the test based on GPC. The simulations show that the power of the GPC test for the primary endpoint is modified if the secondary endpoint is included in the hierarchical GPC analysis. The change in power depends on the correlation between the endpoints. Interestingly, a decrease in power can occur, regardless of whether there is any marginal treatment effect on the secondary endpoint. It appears that the overall power of the hierarchical GPC procedure depends, in a complex manner, on the entire variance-covariance structure of the set of outcomes. Any additional factors (such as thresholds of clinical relevance, drop out, or censoring scheme) will also affect the power and will have to be taken into account when designing a trial based on the hierarchical GPC procedure.