Dataset Information

Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials.

ABSTRACT:

Background

Risk 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.

Methods

We 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.

Results

We 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).

Conclusions

There 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.

SUBMITTER: Kent DM

PROVIDER: S-EPMC5841614 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials.

Kent David M DM Nelson Jason J Dahabreh Issa J IJ Rothwell Peter M PM Altman Douglas G DG Hayward Rodney A RA

International journal of epidemiology 20161201 6

<h4>Background</h4>Risk 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.<h4>Methods</h4>We 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 ris ...[more]

PMID: 27375287

Similar Datasets

Project description:Birth size, perhaps a proxy for prenatal environment, might be a correlate of subsequent breast cancer risk, but findings from epidemiological studies have been inconsistent. We re-analysed individual participant data from published and unpublished studies to obtain more precise estimates of the magnitude and shape of the birth size-breast cancer association.Studies were identified through computer-assisted and manual searches, and personal communication with investigators. Individual participant data from 32 studies, comprising 22,058 breast cancer cases, were obtained. Random effect models were used, if appropriate, to combine study-specific estimates of effect. Birth weight was positively associated with breast cancer risk in studies based on birth records (pooled relative risk [RR] per one standard deviation [SD] [= 0.5 kg] increment in birth weight: 1.06; 95% confidence interval [CI] 1.02-1.09) and parental recall when the participants were children (1.02; 95% CI 0.99-1.05), but not in those based on adult self-reports, or maternal recall during the woman's adulthood (0.98; 95% CI 0.95-1.01) (p for heterogeneity between data sources = 0.003). Relative to women who weighed 3.000-3.499 kg, the risk was 0.96 (CI 0.80-1.16) in those who weighed < 2.500 kg, and 1.12 (95% CI 1.00-1.25) in those who weighed > or = 4.000 kg (p for linear trend = 0.001) in birth record data. Birth length and head circumference from birth records were also positively associated with breast cancer risk (pooled RR per one SD increment: 1.06 [95% CI 1.03-1.10] and 1.09 [95% CI 1.03-1.15], respectively). Simultaneous adjustment for these three birth size variables showed that length was the strongest independent predictor of risk. The birth size effects did not appear to be confounded or mediated by established breast cancer risk factors and were not modified by age or menopausal status. The cumulative incidence of breast cancer per 100 women by age 80 y in the study populations was estimated to be 10.0, 10.0, 10.4, and 11.5 in those who were, respectively, in the bottom, second, third, and top fourths of the birth length distribution.This pooled analysis of individual participant data is consistent with birth size, and in particular birth length, being an independent correlate of breast cancer risk in adulthood.

Project description:BackgroundStatin therapy is effective for the prevention of atherosclerotic cardiovascular disease and is widely prescribed, but there are persisting concerns that statin therapy might frequently cause muscle pain or weakness. We aimed to address these through an individual participant data meta-analysis of all recorded adverse muscle events in large, long-term, randomised, double-blind trials of statin therapy.MethodsRandomised trials of statin therapy were eligible if they aimed to recruit at least 1000 participants with a scheduled treatment duration of at least 2 years, and involved a double-blind comparison of statin versus placebo or of a more intensive versus a less intensive statin regimen. We analysed individual participant data from 19 double-blind trials of statin versus placebo (n=123 940) and four double-blind trials of a more intensive versus a less intensive statin regimen (n=30 724). Standard inverse-variance-weighted meta-analyses of the effects on muscle outcomes were conducted according to a prespecified protocol.FindingsAmong 19 placebo-controlled trials (mean age 63 years [SD 8], with 34 533 [27·9%] women, 59 610 [48·1%] participants with previous vascular disease, and 22 925 [18·5%] participants with diabetes), during a weighted average median follow-up of 4·3 years, 16 835 (27·1%) allocated statin versus 16 446 (26·6%) allocated placebo reported muscle pain or weakness (rate ratio [RR] 1·03; 95% CI 1·01-1·06). During year 1, statin therapy produced a 7% relative increase in muscle pain or weakness (1·07; 1·04-1·10), corresponding to an absolute excess rate of 11 (6-16) events per 1000 person-years, which indicates that only one in 15 ([1·07-1·00]/1·07) of these muscle-related reports by participants allocated to statin therapy were actually due to the statin. After year 1, there was no significant excess in first reports of muscle pain or weakness (0·99; 0·96-1·02). For all years combined, more intensive statin regimens (ie, 40-80 mg atorvastatin or 20-40 mg rosuvastatin once per day) yielded a higher RR than less intensive or moderate-intensity regimens (1·08 [1·04-1·13] vs 1·03 [1·00-1·05]) compared with placebo, and a small excess was present (1·05 [0·99-1·12]) for more intensive regimens after year 1. There was no clear evidence that the RR differed for different statins, or in different clinical circumstances. Statin therapy yielded a small, clinically insignificant increase in median creatine kinase values of approximately 0·02 times the upper limit of normal.InterpretationStatin therapy caused a small excess of mostly mild muscle pain. Most (>90%) of all reports of muscle symptoms by participants allocated statin therapy were not due to the statin. The small risks of muscle symptoms are much lower than the known cardiovascular benefits. There is a need to review the clinical management of muscle symptoms in patients taking a statin.FundingBritish Heart Foundation, Medical Research Council, Australian National Health and Medical Research Council.

Project description:BackgroundMeta-analyses of individual-level data from randomised trials are often required to detect clinically worthwhile effects. The Cholesterol Treatment Trialists' Collaboration, which includes data from numerous large long-term statin trials, is conducting a review of the effects of statin therapy on all adverse events collected in those trials. This article describes the approaches used and challenges faced to systematically capture and categorise the data.MethodsProtocols, statistical analysis plans, case report forms, clinical study reports and datasets were obtained, reviewed and checked. Relevant baseline and follow-up data from each trial was then reorganised into standardised formats based upon the Clinical Data Interchange Standards Consortium Study Data Tabulation Model. Adverse event data were organised and coded (automatically or, where necessary, manually) according to a common medical dictionary based upon the Medical Dictionary for Regulatory Activities.ResultsData from 23 double-blind statin trials and 5 open-label statin trials were provided, either through direct data transfer or through online access platforms. Together, these trials provided 845 datasets containing over 38 million records relating to 30,495 study variables and 181,973 randomised participants. Of the 46 Clinical Data Interchange Standards Consortium Study Data Tabulation Model domains that could potentially have been used to organise the data, the 13 most relevant to the project were identified and utilised, including 6 domains related to post-randomisation adverse events. Nearly 1.2 million adverse events were extracted and mapped to over 45,000 unique adverse event terms. Of these adverse events, 99% were coded to a Medical Dictionary for Regulatory Activities 'lower level term', with the remainder coded to a 'higher level term' or, very rarely, only a 'higher level group term'.ConclusionIn this meta-analysis of adverse event data from the large randomised trials of statins, approaches based on common standards for data organisation and classification have provided a resource capable of allowing reliable and rapid evaluation of any previously unknown benefits or hazards of statin therapy.

Project description:ImportanceThe pharmaceutical industry has made substantial investments in developing processes for sharing individual-participant data (IPD) from clinical trials. However, the utility and completeness of shared IPD and supporting documents must be evaluated to ensure the potential for scientific advancements from the data sharing ecosystem can be realized.ObjectiveTo assess the utility and completeness of IPD and supporting documents provided from industry-sponsored clinical trials.Design, setting, and participantsFrom February 9, 2022, to February 9, 2023, 91 of 203 clinical trials supporting US Food and Drug Administration registrations of anticancer medicines for the treatment of solid tumors from the past decade were confirmed as eligible for IPD request. This quality improvement study performed a retrospective audit of the utility and completeness of the IPD and supporting documents provided from the 91 clinical trials for a planned meta-analysis.ExposuresRequest for IPD from 91 clinical oncology trials indicated as eligible for the request.Main outcomes and measuresThe utility and completeness of the IPD and supporting documents provided.ResultsThe IPD packages were obtained from 70 of 91 requested clinical trials (77%). The median time to data provision was 123 (range, 117-352) days. Redactions were observed in 18 of the acquired IPD packages (26%) for outcome data, 11 (16%) for assessment variables, and 19 (27%) for adjustment data. Additionally, 20 IPD packages (29%) lacked a clinical study report, 4 (6%) had incomplete or missing data dictionaries, and 20 (29%) were missing anonymization or redaction description files. Access to IPD from 21 eligible trials (23%) was not granted.Conclusions and relevanceIn this quality improvement study, there was substantial variability within the provided IPD packages regarding the completeness of key data variables and supporting documents. To improve the data sharing ecosystem, key areas for enhancement include (1) ensuring that clinical trials are eligible for IPD sharing, (2) making eligible IPD transparently accessible, and (3) ensuring that IPD packages meet a standard of utility and completeness.

Project description:BACKGROUND:Frailty is common in clinical practice, but trials rarely report on participant frailty. Consequently, clinicians and guideline-developers assume frailty is largely absent from trials and have questioned the relevance of trial findings to frail people. Therefore, we examined frailty in phase 3/4 industry-sponsored clinical trials of pharmacological interventions for three exemplar conditions: type 2 diabetes mellitus (T2DM), rheumatoid arthritis (RA), and chronic obstructive pulmonary disease (COPD). METHODS:We constructed a 40-item frailty index (FI) in 19 clinical trials (7 T2DM, 8 RA, 4 COPD, mean age 42-65?years) using individual-level participant data. Participants with a FI >?0.24 were considered 'frail'. Baseline disease severity was assessed using HbA1c for T2DM, Disease Activity Score-28 (DAS28) for RA, and % predicted FEV1 for COPD. Using generalised gamma regression, we modelled FI on age, sex, and disease severity. In negative binomial regression, we modelled serious adverse event rates on FI and combined results for each index condition in a random-effects meta-analysis. RESULTS:All trials included frail participants: prevalence 7-21% in T2DM trials, 33-73% in RA trials, and 15-22% in COPD trials. The 99th centile of the FI ranged between 0.35 and 0.45. Female sex was associated with higher FI in all trials. Increased disease severity was associated with higher FI in RA and COPD, but not T2DM. Frailty was associated with age in T2DM and RA trials, but not in COPD. Across all trials, and after adjusting for age, sex, and disease severity, higher FI predicted increased risk of serious adverse events; the pooled incidence rate ratios (per 0.1-point increase in FI scale) were 1.46 (95% CI 1.21-1.75), 1.45 (1.13-1.87), and 1.99 (1.43-2.76) for T2DM, RA, and COPD, respectively. CONCLUSION:The upper limit of frailty in trials is lower than has been described in the general population. However, mild to moderate frailty was common, suggesting trial data may be harnessed to inform disease management in people living with frailty. Participants with higher FI experienced more serious adverse events, suggesting screening for frailty in trial participants would enable identification of those that merit closer monitoring. Frailty is identifiable and prevalent among middle-aged and older participants in phase 3/4 drug trials and has clinically important safety implications.

Project description:ObjectivesWe examined major issues associated with sharing of individual clinical trial data and developed a consensus document on providing access to individual participant data from clinical trials, using a broad interdisciplinary approach.Design and methodsThis was a consensus-building process among the members of a multistakeholder task force, involving a wide range of experts (researchers, patient representatives, methodologists, information technology experts, and representatives from funders, infrastructures and standards development organisations). An independent facilitator supported the process using the nominal group technique. The consensus was reached in a series of three workshops held over 1 year, supported by exchange of documents and teleconferences within focused subgroups when needed. This work was set within the Horizon 2020-funded project CORBEL (Coordinated Research Infrastructures Building Enduring Life-science Services) and coordinated by the European Clinical Research Infrastructure Network. Thus, the focus was on non-commercial trials and the perspective mainly European.OutcomeWe developed principles and practical recommendations on how to share data from clinical trials.ResultsThe task force reached consensus on 10 principles and 50 recommendations, representing the fundamental requirements of any framework used for the sharing of clinical trials data. The document covers the following main areas: making data sharing a reality (eg, cultural change, academic incentives, funding), consent for data sharing, protection of trial participants (eg, de-identification), data standards, rights, types and management of access (eg, data request and access models), data management and repositories, discoverability, and metadata.ConclusionsThe adoption of the recommendations in this document would help to promote and support data sharing and reuse among researchers, adequately inform trial participants and protect their rights, and provide effective and efficient systems for preparing, storing and accessing data. The recommendations now need to be implemented and tested in practice. Further work needs to be done to integrate these proposals with those from other geographical areas and other academic domains.

Dataset Information

Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials.

Background

Methods

Results

Conclusions

Publications

Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets