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The use of evidence from clinical trials to support decisions for individual patients is a form of “reference class forecasting”: implicit predictions for an individual are made on the basis of outcomes in a reference class of “similar” patients treated with alternative therapies. Evidence based medicine has generally emphasized the broad reference class of patients qualifying for a trial. Yet patients in a trial (and in clinical practice) differ from one another in many ways that can affect the outcome of interest and the potential for benefit. The central goal of personalized medicine, in its various forms, is to narrow the reference class to yield more patient specific effect estimates to support more individualized clinical decision making. This article will review fundamental conceptual problems with the prediction of outcome risk and heterogeneity of treatment effect (HTE), as well as the limitations of conventional (one-variable-at-a-time) subgroup analysis. It will also discuss several regression based approaches to “predictive” heterogeneity of treatment effect analysis, including analyses based on “risk modeling” (such as stratifying trial populations by their risk of the primary outcome or their risk of serious treatment-related harms) and analysis based on “effect modeling” (which incorporates modifiers of relative effect). It will illustrate these approaches with clinical examples and discuss their respective strengths and vulnerabilities.

The machine learning community has become alert to the ways that predictive algorithms can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts of algorithmic fairness might apply in healthcare, where predictive algorithms are being increasingly used to support decision-making. Central to our discussion is the distinction between algorithmic fairness and algorithmic bias. Fairness concerns apply specifically when algorithms are used to support polar decisions (i.e., where one pole of prediction leads to decisions that are generally more desired than the other), such as when predictions are used to allocate scarce health care resources to a group of patients that could all benefit. We review different fairness criteria and demonstrate their mutual incompatibility. Even when models are used to balance benefits-harms to make optimal decisions for individuals (i.e., for non-polar decisions)–and fairness concerns are not germane–model, data or sampling issues can lead to biased predictions that support decisions that are differentially harmful/beneficial across groups. We review these potential sources of bias, and also discuss ways to diagnose and remedy algorithmic bias. We note that remedies for algorithmic fairness may be more problematic, since we lack agreed upon definitions of fairness. Finally, we propose a provisional framework for the evaluation of clinical prediction models offered for further elaboration and refinement. Given the proliferation of prediction models used to guide clinical decisions, developing consensus for how these concerns can be addressed should be prioritized.

Clinical prediction models that support treatment decisions are usually evaluated for their ability to predict the risk of an outcome rather than treatment benefit–the difference between outcome risk with vs. without therapy. We aimed to define performance metrics for a model's ability to predict treatment benefit. We analyzed data of the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial and of three recombinant tissue plasminogen activator trials. We assessed alternative prediction models with a conventional risk concordance-statistic (c-statistic) and a novel c-statistic for benefit. We defined observed treatment benefit by the outcomes in pairs of patients matched on predicted benefit but discordant for treatment assignment. The ‘c-for-benefit’ represents the probability that from two randomly chosen matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. Compared to a model without treatment interactions, the SYNTAX score II had improved ability to discriminate treatment benefit (c-for-benefit 0.590 vs. 0.552), despite having similar risk discrimination (c-statistic 0.725 vs. 0.719). However, for the simplified stroke–thrombolytic predictive instrument (TPI) vs. the original stroke-TPI, the c-for-benefit (0.584 vs. 0.578) was similar. The proposed methodology has the potential to measure a model's ability to predict treatment benefit not captured with conventional performance metrics.

In a retrospective cohort study of 170 solid organ transplant recipients who completed treatment for an episode of cytomegalovirus disease, absolute lymphocyte count within a week of cessation of primary treatment emerged as an independent predictor of relapse. Abstract Background Recurrent cytomegalovirus (CMV) disease in solid organ transplant recipients frequently occurs despite effective antiviral therapy. We previously demonstrated that patients with lymphopenia before liver transplantation are more likely to develop posttransplant infectious complications including CMV. The aim of this study was to explore absolute lymphocyte count (ALC) as a predictor of relapse following treatment for CMV disease. Methods We performed a retrospective cohort study of heart, liver, and kidney transplant recipients treated for an episode of CMV disease. Our primary outcome was time to relapse of CMV within 6 months. Data on potential predictors of relapse including ALC were collected at the time of CMV treatment completion. Univariate and multivariate hazard ratios (HRs) were calculated with a Cox model. Multiple imputation was used to complete the data. Results Relapse occurred in 33 of 170 participants (19.4%). Mean ALC in relapse-free patients was 1.08 ± 0.69 vs 0.73 ± 0.42 × 103 cells/μL in those who relapsed, corresponding to an unadjusted hazard ratio of 1.11 (95% confidence interval, 1.03-1.21; P = .009, n = 133) for every decrease of 100 cells/μL. After adjusting for potential confounders, the association between ALC and relapse remained significant (HR, 1.11 [1.03-1.20]; P = .009). Conclusions Low ALC at the time of CMV treatment completion was a strong independent predictor for recurrent CMV disease. This finding is biologically plausible given the known importance of T-cell immunity in maintaining CMV latency. Future studies should consider this inexpensive, readily available marker of host immunity.

Background Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. Methods We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. Results The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). Conclusions Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.

Over the past 20 years, percutaneous transluminal balloon coronary angioplasty (PTCA), bare-metal stents (BMS), and drug-eluting stents (DES) succeeded each other as catheter-based treatments for coronary artery disease. We undertook a systematic overview of randomised trials comparing these interventions with each other and with medical therapy in patients with non-acute coronary artery disease. We searched Medline for trials contrasting at least two of the four interventions (PTCA, BMS, DES, and medical therapy). Eligible outcomes were death, myocardial infarction, coronary artery bypass grafting, target lesion or vessel revascularisation, and any revascularisation. Random effects meta-analyses summarised head-to-head (direct) comparisons, and network meta-analyses integrated direct and indirect evidence. 61 eligible trials (25 388 patients) investigated four of six possible comparisons between the four interventions; no trials directly compared DES with medical therapy or PTCA. In all direct or indirect comparisons, succeeding advancements in percutaneous coronary intervention did not produce detectable improvements in deaths or myocardial infarction. The risk ratio (RR) for indirect comparisons between DES and medical therapy was 0·96 (95% CI 0·60–1·52) for death and 1·15 (0·73–1·82) for myocardial infarction. By contrast, we recorded sequential significant reductions in target lesion or vessel revascularisation with BMS compared with PTCA (RR 0·68 [0–60·0·77]) and with DES compared with BMS (0·44 [0·35–0·56]). The RR for the indirect comparison between DES and PTCA for target lesion or vessel revascularisation was 0·30 (0·17–0·51). Sequential innovations in the catheter-based treatment of non-acute coronary artery disease showed no evidence of an effect on death or myocardial infarction when compared with medical therapy. These results lend support to present recommendations to optimise medical therapy as an initial management strategy in patients with this disease. US National Institutes of Health.

Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. A prespecified protocol was registered on PROSPERO (CRD42018048202).

People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Background Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for “personalizing” medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects. Methods We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike’s Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. Results The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size ( N  = 4,250; ~ 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger ( N  = 17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. Conclusions An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.

Integrated care is effective in reducing all-cause mortality in patients with atrial fibrillation (AF) in primary care, though time and resource intensive. The aim of the current study was to assess whether integrated care should be directed at all AF patients equally. The ALL-IN trial (n = 1,240 patients, median age 77 years) was a cluster-randomized trial in which primary care practices were randomized to provide integrated care or usual care to AF patients aged 65 years and older. Integrated care comprised of (i) anticoagulation monitoring, (ii) quarterly checkups and (iii) easy-access consultation with cardiologists. For the current analysis, cox proportional hazard analysis with all clinical variables from the CHA.sub.2 DS.sub.2 -VASc score was used to predict all-cause mortality in the ALL-IN trial. Subsequently, the hazard ratio and absolute risk reduction were plotted as a function of this predicted mortality risk to explore treatment heterogeneity. Under usual care, after a median of 2 years follow-up the absolute risk of all-cause mortality in the highest-risk quarter was 31.0%, compared to 4.6% in the lowest-risk quarter. On the relative scale, there was no evidence of treatment heterogeneity (p for interaction = 0.90). However, there was substantial treatment heterogeneity on the absolute scale: risk reduction in the lowest risk- quarter of risk 3.3% (95% CI -0.4% - 7.0) compared to 12.0% (95% CI 2.7% - 22.0) in the highest risk quarter. While the relative degree of benefit from integrated AF care is similar in all patients, patients with a high all-cause mortality risk have a greater benefit on an absolute scale and should therefore be prioritized when implementing integrated care.