Effect of age, sex, and morbidity count on trial attrition: meta-analysis of individual participant level data from phase 3/4 industry funded clinical trials

Abstract

ObjectivesTo estimate the association between individual participant characteristics and attrition from randomised controlled trials.DesignMeta-analysis of individual participant level data (IPD).Data sourcesClinical trial repositories (Clinical Study Data Request and Yale University Open Data Access).Eligibility criteria for selecting studiesEligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202).Main outcome measuresAssociation between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes.ResultsIn 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%.ConclusionsIncreased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis

“…Thirdly, while our definitions were prespecified, based on high-quality data, and have previously been used to demonstrate associations between comorbidity and both trial withdrawal and trial serious adverse events [ 5 , 31 ], the trials were not designed to measure comorbidity. It is possible that stronger associations may have been found if bespoke measures had been available.…”

“…In previous individual participant data (IPD) analyses from a set of 124 clinical trials ranging across a number of index conditions and treatment comparisons, we have previously shown that multimorbidity, although underrepresented is present among trial participants and predicts increased rates of both serious adverse events and trial attrition [ 5 , 30 , 31 ]. Using the 56 trials from this set for which measures of quality of life are available, we now aim to determine whether comorbidity count, which is increased in multimorbidity, predicts change in quality life, and whether treatment effects on quality of life differ by comorbidity count at baseline.…”

Participant characteristics and exclusion from trials: a meta-analysis of individual participant-level data from phase 3/4 industry-funded trials in chronic medical conditions

“…As previously described 6,16,17 , comorbidities were defined using concomitant medications and pre-specified medical history-based definition (MedDRA codes) for cardiovascular disease, chronic pain, arthritis, affective disorders, acid-related disorders, asthma/chronic obstructive pulmonary disease, diabetes mellitus, osteoporosis, thyroid disease, thromboembolic disease, inflammatory conditions, benign prostatic hyperplasia, gout, glaucoma, urinary incontinence, erectile dysfunction, psychotic disorders, epilepsy, migraine, parkinsonism and dementia 6 . Individuals were considered to have a comorbidity if they had evidence of this comorbidity from either concomitant medications or medical history-based definition (or both).…”

Assessing trial representativeness using serious adverse events: an observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data

“…However, many patients treated in routine clinical care do not meet trial eligibility criteria; older patients and those with multimorbidity (the presence of two or more conditions) or frailty are often excluded or under-represented [1]. Although not always an explicit exclusion criterion, investigators also routinely exclude people who have concerns over an individual's ability to manage the burdens of trial participation [2], in order to minimise loss to follow-up [3].…”