Limitations of Applying Summary Results of Clinical Trials to Individual Patients
There is growing awareness that the results of randomized clinical trials might not apply in a straightforward way to individual patients, even those within the trial. Although randomization theoretically ensures the comparability of treatment groups overall, there remain important differences between individuals in each treatment group that can dramatically affect the likelihood of benefiting from or being harmed by a therapy.1- 4 Averaging effects across such different patients can give misleading results to physicians who care for individual, not average, patients.
Available from: Paquito Bernard
- "placebo medication) with anxiety or depression as primary outcome; (b) two-armed RCTs involving planned stratification analyses according to antidepressant/anxiolytic participants' use during intervention (Kent & Hayward, 2007). New knowledge may be obtained by using several strategies of stratification: stratified randomization according to antidepressant/anxiolytic use at baseline, a posteriori stratification according to antidepressant/anxiolytic use as a time-varying variable in the course of the intervention; molecule type, dosage. "
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ABSTRACT: The aims of this commentary are to (1) identify the potential interactive effects of exercise with anxiety/depression medications, based on mechanistic or observational studies, (2) describe how studies dealt with use of antidepressant or anxiolytic participants' medications in their study design and analyses, based on a narrative review of Randomized Controlled Trials (RCTs), and (3) consider the implications for future research for assessing the use of medications within trials and using this information in analyses. Free Access: http://authors.elsevier.com/a/1RN926F6xNDcY6
Available from: Egbert R te Velde
- "). Investigating how the prognostic score from a model affects the treatment response, rather than the individual treatment predictive factors which made up the score, is called a risk-stratified analysis (Kent, 2007). Due to heterogeneity in the treatment protocols of the included trials in the Van den Boogaard study it was not possible to combine the individual patient data from each trial to conduct a meta-analysis. "
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ABSTRACT: Infertility is defined as failure to conceive after 1 year of unprotected intercourse. This dichotomization into fertile versus infertile, based on lack of conception over 12-month period, is fundamentally flawed. Time to conception is strongly influenced by factors such as female age and whilst a minority of couples have absolute infertility (sterility), many are able to conceive without intervention but may take longer to do so, reflecting the degree of subfertility. This natural variability in time to conception means that subfertility reflects a prognosis rather than a diagnosis. Current clinical prediction models in fertility only provide individualized estimates of the probability of either treatment-independent pregnancy or treatment-dependent pregnancy, but do not take account of both. Together, prognostic factors which are able to predict natural pregnancy and predictive factors of response to treatment would be required to estimate the absolute increase in pregnancy chances with treatment. This stratified medicine approach would be appropriate for facilitating personalized decision-making concerning whether or not to treat subfertile patients. Published models are thus far of little value for decisions regarding when to initiate treatment in patients who undergo a period of, ultimately unsuccessful, expectant management. We submit that a dynamic prediction approach, which estimates the change in subfertility prognosis over the course of follow-up, would be ideally suited to inform when the commencement of treatment would be most beneficial in those undergoing expectant management. Further research needs to be undertaken to identify treatment predictive factors and to identify or create databases to allow these approaches to be explored. In the interim, the most feasible approach is to use a combination of previously published clinical prediction models.
Available from: Nicolas Roche
- "In the past, analyses of potential efficacy of pharmacotherapy on mortality in patients with COPD have relied on conventional (and most often post-hoc) subgroup analyses of individual variables to examine variability in treatment effects among subgroups [3,5]. However, conventional subgroup analyses are inadequate to detect large and clinically important differences in treatment effect among patients when multiple factors determine risk . This is typically the case for mortality risk in patients with COPD, which is determined by multiple factors including the level of FEV1 impairment, age, patient-centered outcomes (e.g. "
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Inhaled therapies reduce risk of chronic obstructive pulmonary disease (COPD) exacerbations, but their effect on mortality is less well established. We hypothesized that heterogeneity in baseline mortality risk influenced the results of drug trials assessing mortality in COPD.
The 5706 patients with COPD from the Understanding Potential Long-term Impacts on Function with Tiotropium (UPLIFT®) study that had complete clinical information for variables associated with mortality (age, forced expiratory volume in 1 s, St George’s Respiratory Questionnaire, pack-years and body mass index) were classified by cluster analysis. Baseline risk of mortality between clusters, and impact of tiotropium were evaluated during the 4-yr follow up.
Four clusters were identified, including low-risk (low mortality rate) patients (n = 2339; 41%; cluster 2), and high-risk patients (n = 1022; 18%; cluster 3), who had a 2.6- and a six-fold increase in all-cause and respiratory mortality compared with cluster 2, respectively. Tiotropium reduced exacerbations in all clusters, and reduced hospitalizations in high-risk patients (p < 0.05). The beneficial effect of tiotropium on all-cause mortality in the overall population (hazard ratio, 0.87; 95% confidence interval, 0.75–1.00, p = 0.054) was explained by a 21% reduction in cluster 3 (p = 0.07), with no effect in other clusters.
Large variations in baseline risks of mortality existed among patients in the UPLIFT® study. Inclusion of numerous low-risk patients may have reduced the ability to show beneficial effect on mortality. Future clinical trials should consider selective inclusion of high-risk patients.
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