dataesr/scientific-paragraphs-categorization huggingface dataset:
Eventually, the interim analysis was performed after 150 events: the efficacy boundary was crossed ( = 0. 0009) and the trial stopped. Overall, 409 patients were randomized, 202 to RTX maintenance and 207 to standard of care. At the time of the planning of this trial in 2007, information on RTX maintenance therapy in CLL was scarce. Nevertheless, at the beginning of the inclusion period in December 2010, results of the PRIMA trial (NCT00140582) were published, demonstrating the benefit of 2-year RTX maintenance in patients with follicular lymphoma (FL) receiving a RTX plus chemotherapy regimen as first-line treatment (progression free survival: HR=0. 55, 95% CI 0. 44-0. 68) [15]. Although FL is a different population from CLL, these hematologic malignancies share some characteristics and evolution profiles.
They both are indolent Bcell lymphoid malignancies, which progress slowly by acute phases. They both develop similar complications related to the immune system, notably infections. In both cases, there was no curative therapy currently available. Last, in both trials, eligible patients had to be treatment-naive. In that sense, it appeared relevant to consider the FL population from the PRIMA trial, though non perfectly, similar and exchangeable to the CLL population from the CLL7-SA trial. Therefore, results from the PRIMA trial on the effect of RTX in FL patients could provide clinically relevant information on the effect of RTX maintenance in CLL patients. Moreover, we also considered the acceptability of this historical dataset for the combination to the current data  [16]. Both trials were large European multicenter randomized phase 3 trials.
In the present post-hoc re-analysis, we propose to use Bayesian methods to incorporate information from the PRIMA trial as soon as it was published to the analysis of the CLL7-SA trial, a phase 3 trial with a survival endpoint. In particular, we propose using the power prior approach which allows leveraging the external data  [17] , accounting for the disease heterogeneity between the current trial (CLL patients) and external information (FL patients).
Notations Let  be the number of observations in the dataset, and let  1 , … ,   , denote the right-censored times, i. e. ,   = (  ,   ) and   = (  ≤   ) with   the failure time and   the censoring time of individuals ,  = 1, … ,. Data are given by ( 1 ,  1 ,  1 ), … , (  ,   ,   ), where  is the observation time and  the indicator variable for randomization group (  = 0 if the patients is allocated to the control group and  = 1 for the intervention group). We consider the proportional hazards model: 𝑇 𝑖 |𝑍 𝑖 ∼ 𝐹 (𝑡|𝑍 𝑖 ) = 1 -(1 -𝐹 0 (𝑡)) 𝑒𝑥𝑝(𝑍 ′ 𝑖 𝛽) (1) or, equivalently, 𝐻(𝑡|𝑍 𝑖 ) = 𝑒𝑥𝑝(𝑍 ′ 𝑖 𝛽)𝐻 0 (𝑡) where  0 () = 1 - 0 () is the baseline distribution function of the right-censored time (either time to airflow decline or death, whatever occurred first, and time to progression or death, for the ALLOZITHRO and CLL7-SA trials, respectively),  0 () = ∫  0 ℎ 0 () is the cumulative baseline hazard function, and  the log hazard ratio ().
In the PEM, the baseline hazard takes constant values ℎ  in each time interval   defined over the observation period: ℎ 0 (𝑡) = 𝐾 ∑ 𝑘=1 ℎ 𝑘 𝐼{𝑡 ∈ 𝐼 𝑘 } where   = ( -1 ,   ],  = 1, … , , is an interval resulting from the partition in  intervals of the observation period, with  0 = 0 and   = ∞. The time partition was defined following Murray et al. 's approach  [27] , with  = max{5, min(  8 , 20)} intervals, where  is the observed number of events in the current trial dataset, and the left bounds  -1 of the intervals   correspond to the (100×   ) th percentiles of the event times in the current trial dataset. Specifically, since we assumed a time-invariant effect of the treatment arm, therefore, it resulted that the count of events   per interval   = ( -1 ,   ], conditionally on treatment, is Poisson-distributed: 𝛥 𝑘 ∼ Poisson(𝜆 𝑘 ) with rate   = (  - -1 ) exp(  +  ′ ) for the whole time interval.
A correlated random-walk process was used as the prior for the baseline hazard: 𝛼 1 ∼  (𝛼 0 , 𝜎 2 𝛼0 ), 𝛼 𝑘 ∼  (𝛼 𝑘-1 , 𝜎 2 𝛼 ) , with  0 = 0,   0 = 10, and   ∼ Uniform(0. 01, 100)  [9, 27]. We chose a randomwalk process to allow smoothing of the baseline hazard function over time. Indeed, the vague prior on the first interval initiates the random walk, and then subsequent interval parameters are shrunk toward the previous one, given the random walk process. The models were estimated with Hamiltonian Monte Carlo (HMC) simulations in Stan on R statistical platform, using the rstan and rstanarm packages  [26, 28]. We used 4 chains of each 5000 iterations after warm-up, thinning of 5, yielding 4000 iterations overall to include for the analyses. R code is available on GitHub platform at https:// github.
Motivating example 2: Incorporating individual external information Incorporating historical data to a current analysis relies on the assumption that the different datasets are relevant to the population of interest. Depending on the assumption about the homogeneity across the datasets and populations (identity, exchangeability, bias, etc. ), different modeling strategies are available  [18]. L. Biard et al. In the present setting of the CLL7-SA trial, we wished to incorporate results from a single external phase 3 trial clinically relevant to the CLL7-SA objective but in a slightly different population. To account for such external data in a discounted manner, we chose to use the power prior approach proposed by  [17]. Although the true parameter , modeling the effect of the treatment RTX, is assumed the same across the datasets, the information borrowed from historical external data is discounted compared to the current data in the estimation model.
), and 0 ≤  0 ≤ 1 a scalar parameter that represents the weight of the historical data in estimating the prior for. It ranges from 0 (ignoring any previous information from dataset  0 ) up to 1 (where the historical data is pooled to the current without leveraging). Conditionally on the value of  0 , the posterior distribution for  after observing the current data  is given by: (|,  0 ,  0 ) = (|)(| 0 ,  0 ) ∫ 𝛩 𝐿(𝐷|𝜃)𝜋(𝜃|𝐷 0 , 𝑎 0 )𝑑𝜃 (3) The parameter  0 can be also considered as an unknown parameter. In that case,  0 is to be estimated from the datasets (current and historical) and the model includes an hyperprior for  0 , ( 0 | 0 ), with hyperparameter  0. As explained by Ibrahim & Chen  [32] , it is reasonable to set a beta prior for  0 , such as 0 ≤  0 ≤ 1, although other choices such as truncated gamma or normal prior distributions are possible.
When considering a beta prior for  0 , they argued that it is easier to elicit a mean   0 and standard deviation   0 for  0 from physicians, rather than directly setting the hyperparameters vector for the beta distribution  0 = ( 0 ,  0 ); then, hyperparameters can be derived by back substitution using the equations for a beta distribution, as follows  [32] : 𝜇 𝑎 0 = 𝑝 0 𝑝 0 + 𝑞 0 ; 𝜎 2 𝑎 0 = 𝜇 𝑎 0 (1 -𝜇 𝑎 0 ) 𝑝 0 + 𝑞 0 + 1. Nevertheless, one should note that, in the case of a random  0 , we obtain a joint posterior distribution, (,  0 | 0 ), which must include the normalizing constant  [33] : 𝐶(𝑎 0 ) = 1∕ ∫ 𝐿(𝐷 0 |𝜃) 𝑎 0 𝜋(𝜃|𝑐 0 )d𝜃. In the present study, we chose to set  0 fixed instead of random, given the single historical dataset used to enrich the CLL7-SA analysis, thus the rather limited information to estimate between-trial information via  0.
CLL7-SA example We examined how published results of the PRIMA trial could have been used, right away after its publication, on December, 2010, to provide information on the effect of RTX in the CLL7-SA trial. More specifically, we aim to illustrate how this information may have been incorporated in an interim Bayesian analysis. At that time, 216 patients had been randomized, with an actual median follow-up 19. 1 months, and 20 and 11 observed failures in the SoC and RTX arms, respectively. Kaplan-Meier estimates of the 24-month PFS were 75% (95%CI 65;86) for the SoC group, and 87% (78;96) for the RTX group. Since we did not have access to the original individual data from the PRIMA trial, we used reconstructed observations from the initial publication of the PRIMA trial. Specifically, based on the published Kaplan-Meier curves and numbers of patients at risk, using DigitizeIt software and iterative numerical methods solving the inverted Kaplan-Meier equations, we obtained a reconstructed dataset mimicking the PRIMA trial results  [34].
Briefly, for each time interval reported on the publication, the algorithm combines published numbers of at-risk patients, Kaplan-Meier curve coordinates, and iterative calculations using the Kaplan-Meier estimator. We refer the reader to the original publication for a detailed presentation of this algorithm and the corresponding R code  [34]. The reconstructed data was consistent with the published results, yielding a HR=0. 54 (95%CI 0. 44;0. 68) versus HR=0. 55 (95%CI 0. 44;0. 68) in the original publication  [15]. Fig. 1  presents the Kaplan-Meier estimates of the December 2010 interim CLL7-SA data, with the reconstructed PRIMA estimates superimposed, for progression-free survival in patients with RTX maintenance or SoC observation. Results are reported in Table  3. Combined with a weakly informative prior on the effect of RTX (with  0 = 0 and  ( 0 = 0,  0 = 10) as aggregate weakly informative prior on the treatment coefficient, see Section 2.
We chose the power prior approach to downweight the reconstructed data from the PRIMA trial published results, to account for the similar but different disease population (chronic lymphocytic leukemia versus follicular lymphoma). Alternatively, we could have used an informative Gaussian prior for the log () defined based on the PRIMA results, rather than the reconstructed individual data, and discounted this external information by increasing the prior variance on the log ()  [3]. In the case several external sources are available, more complex models with hyperparameterization for the between-source variability, can be considered: Bayesian hierarchical modeling and meta-analytical approaches, power priors with random power parameter  0  [8, 9, 41, 44]. We presented post-hoc analyses of two trials to advocate the use of Bayesian methods in phase 3 trials with survival endpoints.
RESULTS AND DISCUSSION Here, we provide unprecedented information through wall-to-wall mapping of TMF cover changes over a long period  (January 1990  to December 2019) with Landsat imagery at 0. 09-ha resolution (pixel size of 30 m by 30 m), which is freely available from https://forobs. jrc. ec. europa. eu/TMF/ (see Materials and Methods). A description of the forest types included in the TMF is provided in the "Study area and forest types" section. This validated dataset depicts the TMF extent, the related disturbances (natural and anthropogenic degradation or deforestation), and the postdeforestation recovery on an annual basis over the past three decades (see Supplementary Text on annual change dataset, as well as fig. S1 ). One major innovation is the characterization of the sequential dynamics of changes by providing transition stages from the initial observation period to the end of 2019, i.
e. , undisturbed forest, degraded forest, forest regrowth, deforested land, conversion to plantations, conversion to water, afforestation, and changes within the mangroves (Figs. 1  and 2  and see Supplementary Text on transition map and figs. S2 to S7), as well as the timing (dates and duration), recurrence, and intensity of each disturbance. Although no ecosystem may be considered truly undisturbed, as some degree of human impact is present everywhere  (26) , we define an undisturbed TMF as a closed evergreen or semievergreen forest without any disturbance observed over the full Landsat historical dataset (see the "Study area and forest types" section). Consequently, our map baseline of undisturbed forests may include old secondary forests or forests that have been degraded in years before the start of the Landsat archive.
This is because we do not have detailed spatial data before the observation period of existing satellite imagery. At the pantropical scale, the occurrence and extent of forest cover degradation are documented on an annual basis, adding to deforestation data. We define forest degradation as a disturbance in the tree cover canopy that is visible from space over a short time period (less than 2. 5 years), leading to a loss of biodiversity and/or carbon storage. The degraded pixel remains forested land (covered by existing or regrowing trees), whereas deforestation leads to the longterm (visible more than 2. 5 years) conversion into nonforested land. The detection of the degradation has been achieved through the analysis of each individual valid observation of the Landsat archive (see the "Data" and "Mapping method" sections), making it possible to capture short-duration disturbances, such as selective logging (Fig.
2%) compared with other continents (25. 6 and 28. 9% in the Americas and Africa, respectively). 5) As much as 82. 8% of the TMF mapped as degraded in December 2019 corresponds to short-term disturbances that have never been identified at the pantropical scale. Over the period covered by the Global Forest Change (GFC) product  (24) , i. e. , 2001 to 2019, about 21. 2 million ha has been captured as tree cover loss compared with 86 million ha detected as degraded forests by our study during the same period (see the "Comparison with the GFC dataset" section, Fig. 3 , and table  S5 ). 6) We show that the annual rate of degradation is closely related to climatic conditions (Figs. 3  and 4  and fig. S11 ). Whereas the trends in deforestation rates seem to be related to changes in national territorial policies, degradation rates usually show peaks during drought periods and do not seem to be affected by forest conservation policies.
At the continental scale, Tyukavina et al. (32)  shows lower estimates than our study for Africa (-23%) and for Asia (-4%) and higher estimates for Latin America (+16%). 2) Comparison with GFC loss (see the "Comparison with the GFC dataset" section and Fig. 3 )  (24)  shows a lower deforestation rate (-33%) than our study for the period 2000-2012 over the same forest extent (using our TMF extent for the year 2000) (Table  11 ). Underestimation of GFC loss has been documented by previous studies  (32, 33). Tyukavina et al. (32)  reported an underestimation of GFC loss of 19. 4% when considering the entire forest cover (moist and deciduous) loss during the period 2001-2012, with a larger underestimation for Africa (-39. 4%) than for other continents (-13% for Latin America and -5. 7% for Asia).
The ranking of this underestimation by continent is consistent with the ranking observed in our study (first, Africa; second, Latin America; and, third, Asia). The differences with GFC loss are explained by three specific assets of our approach: (i) the use of single-date images, enabling the detection of short-duration disturbance events (i. e. , visible from space for only a few weeks), compared with the use of annual syntheses; (ii) a dedicated algorithm for TMF, enabling the monitoring of seven classes of forest cover change, compared with the global monitoring of forest clearance; and (iii) cloud masking and quality control optimized for equatorial regions, enabling a more comprehensive analysis of the Landsat archive. 3) Comparison with the Brazilian Projeto de Monitoramento do Desmatamento na Amazônia Legal por Satélite (PRODES) data  (30)  using their primary forest extent (Fig.
Data The Landsat archive is the only free and long-term satellite image record suitable for analyzing land cover changes on an annual basis over a long period at fine spatial resolution. We used the entire L1T The Landsat archive coverage presents large geographical and temporal unevenness  (40, 44). The main reason for the limited availability of images for some regions is that Landsat 4 and Landsat 5 had no onboard data recorders, and links with data relay satellites failed over time; cover was therefore often limited to the line of sight of receiving stations  (42). Commercial management of the program from 1985 to the early 1990s led to data being acquired mostly when preordered  (40). From 1999 onward, the launch of Landsat 7 and its onboard data recording capabilities, associated with the continuation of the Landsat 5 acquisitions, considerably improved global coverage.
S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 12 of 21 The geographical unevenness of the first year of acquisition constrains the monitoring capability period. Our method accounts for this constraint notably by recording the effective duration of the archive at the pixel level (see next subsection). Data quality issues affecting the Landsat collection were addressed by excluding pixels where (i) detector artifacts occur (manifested as random speckle or striping), (ii) one or more spectral bands are missing (typically occurring at image edges), or (iii) scene geolocation is inaccurate.
Mapping method To map the extent and changes of the TMF over a long period, we developed an expert system that exploits the multispectral and multitemporal attributes of the Landsat archive to identify the main change trajectories over the past three decades and that uses ancillary information to identify subclasses of forest conversion (see Supplementary Text on ancillary data). The inference engine of our system is a procedural sequential decision tree, where the expert knowledge is represented in the form of rules. Techniques for big data exploration and information extraction, namely, visual analytics  (45)  and evidential reasoning  (46) , were used similarly to a recent study dedicated to global surface water mapping  (44). The advantages of these techniques for remotely sensed data analysis are presented in this previous study  (44).
These notably include accounting for uncertainty in data, guiding and informing the expert's decisions, and incorporating image interpretation expertise and multiple data sources. The expert system was developed and operated in the Google Earth Engine (GEE) geospatial cloud computing platform  (22). The mapping method includes four main steps described hereafter: (i) single-date multispectral classification into three classes, (ii) analysis of trajectory of changes using the temporal information and tion of a "transition" map (with seven classes) (Figs. 1  and 2  and figs. S2 to S7), (iii) identification of subclasses of transition based on ancillary datasets (see Supplementary Text on ancillary datasets) and visual interpretation, and (iv) production of annual change maps (fig. S1 ). In the first step, each image of the Landsat archive was analyzed on a single-date basis (through a multispectral classification), whereas previous large-scale studies used annual syntheses or intraannual statistics, such as the mean and SD of available Landsat observations  (47) (48) (49) (50) (51) (52) (53).
The information held in the spectral library was analyzed through visual analytics to extract equations describing class cluster hulls in the multidimensional feature space (fig. S17 ). An exploratory data analysis tool designed in a previous study  (44)  was used to support the interactive analysis. In the second step of the mapping approach, the temporal sequence of single-date classifications at pixel scale was analyzed to first determine the initial extent of the TMF domain and then to identify the change trajectories from this initial forest extent (fig. S2 ). Long-term changes cannot be determined uniformly for the entire pantropical region because the observation record varies (see the "Data" section), e. g. , the first year of observation (fig. S18 ) is c. 1982  for Brazil and c. 2000 along the Gulf of Guinea.
We have addressed these geographic and temporal discontinuities of the Landsat archive by determining at the pixel level (i) a reference initial period (baseline) for mapping the initial TMF extent and (ii) a monitoring period for detecting the changes. The data gaps at the beginning of the archive were tackled by requiring a minimum period of 4 years with a minimum of three valid observations per year or a minimum of 5 years with two valid observations per year from the first available valid observation. Hence, the annual number of valid observations is lower, while the initial period is longer. This minimizes the risk of inclusion of nonforest cover types (such as agriculture) and deciduous forests in the baseline when there are few valid observations over a short period. In addition, we have reduced the commission errors in our baseline by accounting for possible confusion with commodities, wetlands, bamboo, and deciduous forest (see Supplementary Text on ancillary datasets and specific tropical forest types).
Deforested land is also characterized by the recurrence of disruptions, i. e. , the ratio between the number of years with at least one disruption observation and the total number of years between the first and last disruption observations. This information allowed us to discriminate between (i) deforestation of undisturbed forest and (ii) deforestation of degraded forest (when deforestation happens at least 1 year after degradation), with the second case (ii) having a S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 16 of 21 lower recurrence of disruption events due to the period without any disruption between the degradation and deforestation stages (see Supplementary Text on annual change dataset). For recent degradation and deforestation (class 4) that was initiated in the past 3 years (after year 2016) and that cannot yet be attributed to a long-term conversion to nonforest cover, owing to the limited historical period of observation, specific rules were applied.
The third mapping step allowed us to identify three subclasses from the deforested land class. We geographically assigned deforestation to the following classes: conversion from TMF to tree plantations, mainly oil palm and rubber (class 2a); water surface (discriminating between permanent and seasonal water), mostly due to new dams (class 2b); and other land cover, such as agriculture, infrastructure, etc. (class 2c). This was carried out using ancillary spatial datasets completed by visual interpretation of high-resolution (HR) imagery (see Supplementary Text on ancillary data). Last, we reassigned disturbances within two geographically specific tropical forest forma-tions: (i) the bamboo-dominated forest and (ii) the semideciduous transition tropical forest (see Supplementary Text on specific tropical forest formations).
Each disturbed pixel (degraded forest, deforested land, or forest regrowth) is characterized by the timing and intensity of the observed disruption events. The start and end dates of the disturbance allow us to identify, in particular, the timing of new road creation or of logging activities and the age of forest regrowth or degraded forests. Three decadal periods have been used in the transition map to identify age subclasses of degradation and forest regrowth: (i) before 2000, (ii) within 2000-2009, and (iii) within 2010-2019. The number of annual disruption observations combined with the duration can be used as a proxy for the disturbance intensity and impact level. In the last mapping step, we created a collection of 30 maps providing the spatial extent of the TMF and disturbance classes on a yearly basis, from 1990 to 2019, using dedicated decision rules (see Supplementary Text on annual change dataset and thematic maps).