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Subgroup identification in clinical trials via the predicted individual treatment effect.

ABSTRACT: Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.

SUBMITTER: Ballarini NM

PROVIDER: S-EPMC6193713 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Subgroup identification in clinical trials via the predicted individual treatment effect.

Ballarini Nicolás M NM Rosenkranz Gerd K GK Jaki Thomas T König Franz F Posch Martin M

PloS one 20181018 10

Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties o ...[more]

PMID: 30335831

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Project description:BackgroundRisk of the outcome is a mathematical determinant of the absolute treatment benefit of an intervention, yet this can vary substantially within a trial population, complicating the interpretation of trial results.MethodsWe developed risk models using Cox or logistic regression on a set of large publicly available randomized controlled trials (RCTs). We evaluated risk heterogeneity using the extreme quartile risk ratio (EQRR, the ratio of outcome rates in the lowest risk quartile to that in the highest) and skewness using the median to mean risk ratio (MMRR, the ratio of risk in the median risk patient to the average). We also examined heterogeneity of treatment effects (HTE) across risk strata.ResultsWe describe 39 analyses using data from 32 large trials, with event rates across studies ranging from 3% to 63% (median?=?15%, 25th-75th percentile?=?9-29%). C-statistics of risk models ranged from 0.59 to 0.89 (median?=?0.70, 25th-75th percentile?=?0.65-0.71). The EQRR ranged from 1.8 to 50.7 (median?=?4.3, 25th-75th percentile?=?3.0-6.1). The MMRR ranged from 0.4 to 1.0 (median?=?0.86, 25th-75th percentile?=?0.80-0.92). EQRRs were predictably higher and MMRRs predictably lower as the c-statistic increased or the overall outcome incidence decreased. Among 18 comparisons with a significant overall treatment effect, there was a significant interaction between treatment and baseline risk on the proportional scale in only one. The difference in the absolute risk reduction between extreme risk quartiles ranged from -3.2 to 28.3% (median?=?5.1%; 25th-75th percentile?=?0.3-10.9).ConclusionsThere is typically substantial variation in outcome risk in clinical trials, commonly leading to clinically significant differences in absolute treatment effects Most patients have outcome risks lower than the trial average reflected in the summary result. Risk-stratified trial analyses are feasible and may be clinically informative, particularly when the outcome is predictable and uncommon.

Project description:BackgroundProven treatments for low back pain, at best, only provide modest overall benefits. Matching people to treatments that are likely to be most effective for them may improve clinical outcomes and makes better use of health care resources.MethodsWe conducted an individual participant data meta-analysis of randomised controlled trials of three types of therapist delivered interventions for low back pain (active physical, passive physical and psychological treatments). We applied two statistical methods (recursive partitioning and adaptive risk group refinement) to identify potential subgroups who might gain greater benefits from different treatments from our individual participant data meta-analysis.ResultsWe pooled data from 19 randomised controlled trials, totalling 9328 participants. There were 5349 (57%) females with similar ratios of females in control and intervention arms. The average age was 49 years (standard deviation, SD, 14). Participants with greater psychological distress and physical disability gained most benefit in improving on the mental component scale (MCS) of SF-12/36 from passive physical treatment than non-active usual care (treatment effects, 4.3; 95% confidence interval, CI, 3.39 to 5.15). Recursive partitioning method found that participants with worse disability at baseline gained most benefit in improving the disability (Roland Morris Disability Questionnaire) outcome from psychological treatment than non-active usual care (treatment effects, 1.7; 95% CI, 1.1 to 2.31). Adaptive risk group refinement did not find any subgroup that would gain much treatment effect between psychological and non-active usual care. Neither statistical method identified any subgroups who would gain an additional benefit from active physical treatment compared to non-active usual care.ConclusionsOur methodological approaches worked well and may have applicability in other clinical areas. Passive physical treatments were most likely to help people who were younger with higher levels of disability and low levels of psychological distress. Psychological treatments were more likely to help those with severe disability. Despite this, the clinical importance of identifying these subgroups is limited. The sizes of sub-groups more likely to benefit and the additional effect sizes observed are small. Our analyses provide no evidence to support the use of sub-grouping for people with low back pain.

Project description:ObjectivesTo predict treatment effects for individual patients based on data from randomised trials, taking rosuvastatin treatment in the primary prevention of cardiovascular disease as an example, and to evaluate the net benefit of making treatment decisions for individual patients based on a predicted absolute treatment effect.SettingAs an example, data were used from the Justification for the Use of Statins in Prevention (JUPITER) trial, a randomised controlled trial evaluating the effect of rosuvastatin 20 mg daily versus placebo on the occurrence of cardiovascular events (myocardial infarction, stroke, arterial revascularisation, admission to hospital for unstable angina, or death from cardiovascular causes). Population 17,802 healthy men and women who had low density lipoprotein cholesterol levels of less than 3.4 mmol/L and high sensitivity C reactive protein levels of 2.0 mg/L or more.MethodsData from the Justification for the Use of Statins in Prevention trial were used to predict rosuvastatin treatment effect for individual patients based on existing risk scores (Framingham and Reynolds) and on a newly developed prediction model. We compared the net benefit of prediction based rosuvastatin treatment (selective treatment of patients whose predicted treatment effect exceeds a decision threshold) with the net benefit of treating either everyone or no one.ResultsThe median predicted 10 year absolute risk reduction for cardiovascular events was 4.4% (interquartile range 2.6-7.0%) based on the Framingham risk score, 4.2% (2.5-7.1%) based on the Reynolds score, and 3.9% (2.5-6.1%) based on the newly developed model (optimal fit model). Prediction based treatment was associated with more net benefit than treating everyone or no one, provided that the decision threshold was between 2% and 7%, and thus that the number willing to treat (NWT) to prevent one cardiovascular event over 10 years was between 15 and 50.ConclusionsData from randomised trials can be used to predict treatment effect in terms of absolute risk reduction for individual patients, based on a newly developed model or, if available, existing risk scores. The value of such prediction of treatment effect for medical decision making is conditional on the NWT to prevent one outcome event. Trial registration number Clinicaltrials.gov NCT00239681.

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Subgroup identification in clinical trials via the predicted individual treatment effect.

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Subgroup identification in clinical trials via the predicted individual treatment effect.

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