Meta-analysis of rare events: the challenge of combining the lack of information

For both count and incidence rate data, it is complicated to provide reliable inference of a treatment effect when the number of observed events is too low. Therefore, the idea of regrouping several studies to increase the amount of available information seems particularly appealing in such settings. Unfortunately, standard meta-analysis methods break down with rare events. This thesis aimed at studying the challenge of combining the lack of information. Throughout four articles, we assessed, via simulations, the performance of several alternative meta-analysis methods that better accommodate rare events. Not only did we consider existing methods, but we also designed innovative methods for both count and incidence rate data. Based on the results obtained in these different papers, we were able to draw several recommendations for applied researchers. With count data, and under the assumption of a homogeneous treatment effect, the Mantel-Haenszel method can be used safely, no matter the scarcity level considered. A newly designed pseudo-likelihood approach performed as well as the Mantel-Haenszel method and allowed a gain of precision when the meta-analysis included studies with missing treatment arms. Moreover, unlike Mantel-Haenszel, this pseudo-likelihood approach could be extended to settings with treatment effect heterogeneity and was shown to provide good estimates of the mean treatment effect and informative prediction intervals, even in extremely rare event settings. As for the meta-analysis of incidence rate data, we found that accounting for over-dispersion using a negative-binomial model allowed for improvements in the performance of the classical Poisson model, even in the presence of studies reporting zero event and/or only one treatment arm

Meta-Analysis of Incidence Rate Data in the Presence of Zero-Event and Single-Arm Studies

Unlike the classical two-stage DerSimonian and Laird meta-analysis method, the one-stage random-effects Poisson and Negative-binomial models have the great advantage of including the information contained in studies reporting zero event in one or both arms and in studies with one missing arm. Since the Negative-binomial distribution relaxes the assumption of equi-dispersion made by the Poisson, it should perform better when data exhibit over-dispersion. However, the superiority of the Negative-binomial model with rare events and single-arm studies is unclear and needs to be investigated. Moreover, to the best of our knowledge, this model has never been investigated in the context of a meta-analysis of incidence rate data with heterogeneous intervention effect. Therefore, we assessed the performance of the univariate and bivariate random-effects Poison and Negative-binomial models using simulations calibrated on a real dataset from a study on the surgical management of phyllodes tumors. Results suggested that the bivariate random-effects Negative-binomial model should be favored for the meta-analysis of incidence rate data exhibiting over-dispersion, even in the presence of zero-event and single-arm studies

Real-world evidence was feasible for estimating effectiveness of chemotherapy in breast cancer; a cohort study

Objective: Evidence-based guidelines recommend adjuvant chemotherapy in early stage breast cancer whenever treatment benefit is considered sufficient to outweigh the associated risks. However, many groups of patients were either excluded from or underrepresented in the clinical trials that form the evidence base for this recommendation. This study aims to determine whether using administrative healthcare data – Real World Data (RWD) - and econometric methods for causal analysis to provide ‘Real World Evidence’ (RWE) are feasible methods for addressing this gap.Methods: Cases of primary breast cancer in women from 2001 to 2015 were extracted from the Scottish cancer registry (SMR06) and linked to other routine health records (inpatient and outpatient visits). Four methods were used to estimate the effect of adjuvant chemotherapy on disease-specific and overall mortality: (1) regression with adjustment for covariates (2) propensity score matching (3) instrumental variables analysis and (4) regression discontinuity design. Hazard ratios for breast cancer mortality and all-cause mortality were compared to those from a meta-analysis of randomised trials.Results: 39,805 cases included in the analyses. Regression adjustment, propensity score matching and instrumental variables were feasible while regression discontinuity was not. Effectiveness estimates were similar between RWE and randomised trials for breast cancer mortality but not for all-cause mortality.Conclusions: RWE methods are a feasible means to generate estimates of effectiveness of adjuvant chemotherapy in early stage breast cancer. However, such estimates must be interpreted in the context of the available randomised evidence and the potential biases of the observational methods.<br/

Meta-Analysis of Incidence Rate Data in the Presence of Zero-Event and Single-Arm Studies

Unlike the classical two-stage DerSimonian and Laird meta-analysis method, the one-stage random-effects Poisson and Negative-binomial models have the great advantage of including the information contained in studies reporting zero event in one or both arms and in studies with one missing arm. Since the Negative-binomial distribution relaxes the assumption of equi-dispersion made by the Poisson, it should perform better when data exhibit over-dispersion. However, the superiority of the Negative-binomial model with rare events and single-arm studies is unclear and needs to be investigated. Moreover, to the best of our knowledge, this model has never been investigated in the context of a meta-analysis of incidence rate data with heterogeneous intervention effect. Therefore, we assessed the performance of the univariate and bivariate random-effects Poison and Negative-binomial models using simulations calibrated on a real dataset from a study on the surgical management of phyllodes tumors. Results suggested that the bivariate random-effects Negative-binomial model should be favored for the meta-analysis of incidence rate data exhibiting over-dispersion, even in the presence of zero-event and single-arm studies

Width of margins in phyllodes tumors of the breast: the controversy drags on?—a systematic review and meta-analysis

Phyllodes tumors (PT) of the breast are rare fibroepithelial neoplasms. Information is controversial in the literature regarding to the optimal surgical management. Most studies suggested margins of at least 10 mm while some recent studies suggested narrower margins without an increased risk of local recurrences (LR) and distant metastases (DM). The objective of this systematic review was to identify and compare studies that assessed these different practices. A systematic review was performed through five databases up to April 2019. Studies exploring the association between the width of margins, subtypes of PT, and the LR and DM rates were considered for inclusion. A statistical model for analyzing sparse data and rare events was used. Thirteen studies met eligibility criteria and were selected. Considering a threshold of 10 mm (margins &lt; 10 vs margins ≥ 10 mm), the 5-year incidence rate of LR was estimated to be 5.22 vs. 3.63 (diff. -1.59) per 100 person-years for benign PT, 9.60 vs. 7.33 (diff. -2.27) for borderline PT, and 28.58 vs. 21.84 (diff. -6.74) for malignant PT. For DM, it was estimated to be 0.88 vs. 0.86 (diff. -0.02) for benign PT, 1.61 vs. 1.74 (diff. 0.13) for borderline PT, and 4.80 vs 5.18 (diff. 0.38) for malignant PT. The data for a threshold of 1 mm were not sufficient to draw any conclusions. Irrespective of tumor grade, we found that DM was a rarer event than LR. Malignant PT had the highest incidence rate of LR and DM. This meta-analysis found a clear association between width of margins and LR rates. Whatever the tumor grade, surgical margins ≥ 10 mm guaranteed a lower risk of LR than margins &lt; 10 mm. On the other hand, the width of margin did not influence the apparition of DM