Historical Controls?
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ABSTRACT:
SUBMITTER: Gaynon PS
PROVIDER: S-EPMC5394945 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this paper, we develop the fixed-borrowing adaptive design, a Bayesian adaptive design which facilitates information borrowing from a historical trial using subject-level control data while assuring a reasonable upper bound on the maximum type I error rate and lower bound on the minimum power. First, one constructs an informative power prior from the historical data to be used for design and analysis of the new trial. At an interim analysis opportunity, one evaluates the degree of prior-data conflict. If there is too much conflict between the new trial data and the historical control data, the prior information is discarded and the study proceeds to the final analysis opportunity at which time a noninformative prior is used for analysis. Otherwise, the trial is stopped early and the informative power prior is used for analysis. Simulation studies are used to calibrate the early stopping rule. The proposed design methodology seamlessly accommodates covariates in the statistical model, which the authors argue is necessary to justify borrowing information from historical controls. Implementation of the proposed methodology is straightforward for many common data models, including linear regression models, generalized linear regression models, and proportional hazards models. We demonstrate the methodology to design a cardiovascular outcomes trial for a hypothetical new therapy for treatment of type 2 diabetes mellitus and borrow information from the SAVOR trial, one of the earliest cardiovascular outcomes trials designed to assess cardiovascular risk in antidiabetic therapies.
| S-EPMC6327964 | biostudies-literature
Project description:Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40-70 Pg carbon from soil to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration-moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall.
| S-EPMC5474806 | biostudies-literature
Project description:Several dynamic borrowing methods, such as the modified power prior (MPP), the commensurate prior, have been proposed to increase statistical power and reduce the required sample size in clinical trials where comparable historical controls are available. Most methods have focused on cross-sectional endpoints, and appropriate methodology for longitudinal outcomes is lacking. In this study, we extend the MPP to the linear mixed model (LMM). An important question is whether the MPP should use the conditional version of the LMM (given the random effects) or the marginal version (averaged over the distribution of the random effects), which we refer to as the conditional MPP and the marginal MPP, respectively. We evaluated the MPP for one historical control arm via a simulation study and an analysis of the data of Alzheimer's Disease Cooperative Study (ADCS) with the commensurate prior as the comparator. The conditional MPP led to inflated type I error rate when there existed moderate or high between-study heterogeneity. The marginal MPP and the commensurate prior yielded a power gain (3.6%-10.4% vs. 0.6%-4.6%) with the type I error rates close to 5% (5.2%-6.2% vs. 3.8%-6.2%) when the between-study heterogeneity is not excessively high. For the ADCS data, all the borrowing methods improved the precision of estimates and provided the same clinical conclusions. The marginal MPP and the commensurate prior are useful for borrowing historical controls in longitudinal data analysis, while the conditional MPP is not recommended due to inflated type I error rates.
| S-EPMC9356117 | biostudies-literature
Project description:BackgroundWhile placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions.MethodsWe discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer.ResultsA number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be.ConclusionHistorical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.
| S-EPMC7649932 | biostudies-literature
Project description:Many community-based translations of evidence-based interventions are designed as one-arm studies due to ethical and other considerations. Evaluating the impacts of such programs is challenging. Here, we examine the effectiveness of the lifestyle intervention implemented by the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) demonstration project, a translational lifestyle intervention among American Indian and Alaska Native communities. Data from the landmark Diabetes Prevention Program placebo group was used as a historical control. We compared the use of propensity score (PS) and disease risk score (DRS) matching to adjust for potential confounder imbalance between groups. The unadjusted hazard ratio (HR) for diabetes risk was 0.35 for SDPI-DP lifestyle intervention vs. control. However, when relevant diabetes risk factors were considered, the adjusted HR estimates were attenuated toward 1, ranging from 0.56 (95% CI 0.44-0.71) to 0.69 (95% CI 0.56-0.96). The differences in estimated HRs using the PS and DRS approaches were relatively small but DRS matching resulted in more participants being matched and smaller standard errors of effect estimates. Carefully employed, publicly available randomized clinical trial data can be used as a historical control to evaluate the intervention effectiveness of one-arm community translational initiatives. It is critical to use a proper statistical method to balance the distributions of potential confounders between comparison groups in this kind of evaluations.
| S-EPMC6520136 | biostudies-literature
Project description:BackgroundA retrospective cohort study was conducted in patients undergoing postoperative home monitoring (POHM) following elective primary hip or knee replacements.ObjectiveThe objectives of our study were to compare the cost per patient, readmissions rate, emergency room visits, and mortality within 30 days to the historical standard of care using descriptive analysis.MethodsAfter Research Ethics Board approval, patients who were enrolled and had completed a POHM study were individually matched to historical controls by age, American Society of Anesthesiology class, and procedure at a ratio 1:2.ResultsA total of 54 patients in the study group and 107 in the control group were eligible for the analysis. Compared with the historical standard of care, the average cost per case was Can $5826.32 (SD 1418.89) in the POHM group and Can $9198.58 (SD 1513.59) for controls. After 30 days, there were 2 emergency room visits (3.7%) and 0 readmissions in the POHM group, whereas there were 8 emergency room visits (7.5%) and 2 readmissions (1.9%) in the control group. No mortalities occurred in either group.ConclusionsThe POHM study offers an early hospital discharge pathway for elective hip and knee procedures at a 38% reduction of the standard of care cost. The multidisciplinary transitional POHM team may provide a reliable forum to minimize readmissions, and emergency room visits within 30 days postoperatively.Trial registrationClinicalTrials.gov NCT02143232; https://clinicaltrials.gov/ct2/show/NCT02143232 (Archived by WebCite at http://www.webcitation.org/73WQ9QR6P).
| S-EPMC7728407 | biostudies-literature
Project description:BackgroundDealing with the giant pheochromocytomas (maximum diameter ≥ 6 cm) has long been a tough challenge for urologists. We introduced a new retroperitoneoscopic adrenalectomy method modified with renal-rotation techniques to treat giant pheochromocytomas.Methods28 diagnosed patients were prospectively recruited as the intervention group. Meanwhile, by referring to the historical records in our database, matched patients who had undergone routine retroperitoneoscopic adrenalectomy (RA), transperitoneal laparoscopic adrenalectomy (TA), or open adrenalectomy (OA) for giant pheochromocytomas were selected as controls. Perioperative and follow-up data were collected for comparative assessment.ResultsAmong all the groups, the intervention group had the minimal bleeding volume (28.93 ± 25.94 ml, p < 0.05), the least intraoperative blood pressure variation (59.11 ± 25.68 mmHg, p < 0.05), the shortest operation time (115.32 ± 30.69 min, p < 0.05), the lowest postoperative ICU admission rates (7.14%, p < 0.05), and shortest drainage time length (2.57 ± 0.50 days, p < 0.05). Besides, compared with TA and OA groups, intervention group was also characterized by lower pain scores (3.21 ± 0.63, p < 0.05), less postoperative complications (p < 0.05), earlier diet initiation time (1.32 ± 0.48 postoperative days, p < 0.05) and ambulation time (2.68 ± 0.48 postoperative days, p < 0.05). Follow-up blood pressure and metanephrine and normetanephrine levels in all intervention group patients remained normal.ConclusionCompared with RA, TA, and OA, retroperitoneoscopic adrenalectomy with renal-rotation techniques is a more feasible, efficient, and secure surgical treatment for giant pheochromocytomas.Trial registrationThis study has been prospectively registered on the Chinese Clinical Trial Registry website (ChiCTR2200059953, date of first registration: 14/05/2022).
| S-EPMC10061753 | biostudies-literature
Project description:BACKGROUND:Ensuring treatment adherence is important for the internal validity of clinical trials. In intervention studies where touch points decrease over time, there is even more of an adherence challenge. Trials with multiple cohorts offer an opportunity to innovate on ways to increase treatment adherence without compromising the integrity of the study design, and previous cohorts can serve as historical controls. Electronically delivered nudges offer low-cost opportunities to increase treatment adherence. OBJECTIVE:This study aimed to evaluate the effectiveness of electronic messages (e-messages) on treatment adherence to the last cohort of a parent weight loss intervention during the second half of a year-long trial, when intervention checkpoint frequency decreases. Treatment adherence is measured by intervention class attendance and adherence to the intervention diet. METHODS:All participants in the last cohort (cohort 5, n=128) of a large randomized weight loss study were offered an e-message intervention to improve participant adherence during the last 6 months of a 1-year weight loss program. Overall, 3 to 4 electronic weekly messages asked participants about intervention diet adherence. A propensity score model was estimated using 97 participants who opted to receive e-messages and 31 who declined in cohort 5 and used to pair match cohort 5 e-message participants to a historical control group from cohorts 1 to 4. Moreover, 88 participants had complete data, yielding 176 participants in the final analyses. After matching, intervention and matched control groups were compared on (1) proportion of class attendance between the 6 and 12 month study endpoints, (2) diet adherence, as measured by total carbohydrate grams for low-carbohydrate (LC) and total fat grams for low-fat (LF) diets at 12 months, and (3) weight change from 6 to 12 months. The dose-response relationship between the proportion of text messages responded to and the 3 outcomes was also investigated. RESULTS:Compared with matched controls, receiving e-messages had no effect on (1) treatment adherence; class attendance after 6 months +4.6% (95% CI -4.43 to 13.68, P=.31), (2) adherence; LC -2.5 g carbohydrate, 95% CI -29.9 to 24.8, P=.85; LF +6.2 g fat, 95% CI -4.1 to 17.0, P=.26); or on (3) the secondary outcome of weight change in the last 6 months; +0.3 kg (95% CI -1.0 to 1.5, P=.68). There was a positive significant response correlation between the percentage of messages to which participants responded and class attendance (r=.45, P<.001). CONCLUSIONS:Although this e-message intervention did not improve treatment adherence, future studies can learn from this pilot and may incorporate more variety in the prompts and more interaction to promote more effective user engagement. Uniquely, this study demonstrated the potential for innovating within a multicohort trial using propensity score-matched historical control subjects. TRIAL REGISTRATION:ClinicalTrials.gov NCT01826591; https://clinicaltrials.gov/ct2/show/NCT01826591. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR2-10.1016/j.cct.2016.12.021.
| S-EPMC6534047 | biostudies-literature
Project description:BackgroundPerforming well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial's analysis through a prior distribution.MethodsMotivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions.ResultsThe operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data.ConclusionIn the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings.
| S-EPMC6480797 | biostudies-literature
Project description:Reconstructing genome evolution in historic samples of Phytophthora infestans
| PRJEB4015 | ENA