Enzo Coviello - Academia.edu (original) (raw)
Papers by Enzo Coviello
Expert Review of Vaccines
Boston College Department of Economics, 2006
Background Increased success in the treatment of hematological cancers contributed to the increas... more Background Increased success in the treatment of hematological cancers contributed to the increase of 5-year survival for most adolescent and young adults (AYAs) with these tumours. However, as 5-year survival increased, it became clear that AYA long-term survivors were at increased risk for severe late effects. Moreover, limited information on long-term cancer impact is available for AYAs, since most studies focused on children and adolescents. We aimed to assess various long-term outcomes on AYA survivors of hematological cancers. Methods We selected patients diagnosed with a first primary hematological cancer between 1997 and 2006, in the Italian nationwide population-based cohort of AYA cancer survivors (i.e. alive at least 5 years after cancer diagnosis). Long-term outcomes of interest were: second malignant neoplasms (SMNs), hospitalizations and overall mortality. We calculated standardized incidence ratios (SIRs), standardized hospitalization rate ratios (SHRs) and standardiz...
Vaccine, 2022
Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on ... more Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Epidemiologia & Prevenzione, 2007
In cohort studies the event occurrence is usually described by the incidence rate and the survivo... more In cohort studies the event occurrence is usually described by the incidence rate and the survivor function. In comparison with these estimators the plot of the hazard function has the advantage to show the variations of the occurrence of the event along the period of observation, which often are important to be highlighted. Furthermore, when comparing individuals with different characteristics, the hazard function is a valuable support to check the assumption and to interpret the results of a Cox regression model. This paper illustrates the method for estimating the hazard function and an example is given from a real case by using the survival data of the breast cancers collected in the IMPACT study, aimed to detect the efficacy of the mammographic screening program. The relationship between the usual estimators and the hazard function is shown and its role in the survival regression modelling is emphasized. In the example the estimate of the hazard function allows to point out tha...
Statistical Software Components, 2007
stcoxgof is a post-estimation command testing the goodness of fit after a Cox model. So you must ... more stcoxgof is a post-estimation command testing the goodness of fit after a Cox model. So you must use this command after stcox. To compute Gronnesby and Borgan test and to obtain Arjas like plots Martingale residuals must also be saved specifying stcox's mgale() option; see help stcox. stcoxgof calls scoretest_cox, written by Isabel Canette of StataCorp, to compute score test statistics.
Statistical Software Components, 2005
stkerhaz computes nonparametric estimates of the baseline hazard or baseline SMR and draws the gr... more stkerhaz computes nonparametric estimates of the baseline hazard or baseline SMR and draws the graph of the results. This command can be used after stcox. In this case it requires that you previously specified stcox's basech option. Otherwise you can specify varname storing cumulative baseline hazard.
stselpre returns estimates and standard errors from proportional hazards fit to case-cohort data.... more stselpre returns estimates and standard errors from proportional hazards fit to case-cohort data. Coefficients are estimated according to two methods: Self-Prentice scheme. Risk sets uses just the sub-cohort member at risk, or Prentice scheme. Risk sets are augmented by non sub-cohort cases when they fail. The asymptotic Self-Prentice model variance-covariance matrix and standard errors are computed using the simplification described in Therneau and Li (1999). The stcascoh routine is used to generate data in the appropriate format for stselpre. This is version 1.1.0 of the software.
stpepemori tests the equality of cumulative incidences or conditional probabilities across two gr... more stpepemori tests the equality of cumulative incidences or conditional probabilities across two groups. So varname specifying the groups to be compared can take just two values.
stcompadj estimates the adjusted cumulative incidence function based on a Cox or a flexible param... more stcompadj estimates the adjusted cumulative incidence function based on a Cox or a flexible parametric regression model in the presence of competing risks. Cox regression in the presence of competing risks is usually performed by fitting separate models for each failure type. It is possible to obtain the same results by using a single analysis after appropriately adapting the data set. In short this consists of expanding each observation for each cause of failure, creating a stratum indicator taking on a value of 1 for the first n records, 2 for the following n records and so on, and modifying the failure indicator so that it attains the value 1 for each observation of death caused by the main event in the first stratum, for each observation of death caused by the competing event in the second stratum and so on. This way of representing data (expanded format) allows to model both identical and different effects of the same covariate on the main and competing events.
In survival or cohort studies the failure of an individual may be one of several distinct failure... more In survival or cohort studies the failure of an individual may be one of several distinct failure types. In such a situation we observe an event of interest and one or more competing events whose occurrence precludes or alters the probability of occurence of the first one. stcompet creates variables containing Cumulative Incidence, a function that in this case appropriately estimates the probability of occurrence of each endpoint, corresponding Standard Error and Confidence Bounds. The values in numlist of the previous stset are assumed as occurrence of event of interest. In compet() options you can specify numlist relating to the occurrence of up to six competing events. This version has been updated from that published in Stata Journal, 4:2.
The cumulative risk (CR) of developing cancer is a measure of the spread of cancer in a populatio... more The cumulative risk (CR) of developing cancer is a measure of the spread of cancer in a population used because it is intuitive to understand. However, there are various methods to calculate it leading to results with different interpretations. With real data we calculated the CR of developing any tumour in males and females to age 84 using three approaches. With the former, which uses only the cancer incidence, a CR to age 84 is estimated equal to 51.7% in males and 36% in females. With the second, which takes into account the competing risk of dying for other causes, the CR to age 84 is estimated equal to 44% in males and 32.9% in females. Finally, after adjusting for multiple primaries in the same person, the CR to age 84 boils down to 37.7% in males and 29.2% in females. Only methods taking into account the competing risk of dying for other causes and adjusting for multiple primaries in the same person are appropriate to estimate the real risk of developing any tumour in the cou...
In several medical reports, the survival function is graphed along with the confidence intervals.... more In several medical reports, the survival function is graphed along with the confidence intervals. The endpoints of the confidence intervals are usually connected to draw an area where the entire survival curve is contained with a given confidence. Confidence intervals are pointwise, i.e., they refer to the survival probability at a single time, but they are not valid for all the estimates of the entire survival curve. To this aim, the appropriate measure is confidence bands, not yet available within Stata. Two methods are usually employed to construct these confidence bands. The first was proposed by Hall and Wellner (1980), and the second was proposed by Nair (1984). The latter produces the so-called equal precision (EP) confidence bands. For both methods, log-minus-log and arcsine square-root transformed versions have been proposed. stcband is a new Stata command that allows the user to graph the survival function, together with the confidence bands constructed according to the Ha...
Research Papers in Economics, 2008
stcascoh is used to create an appropriate dataset for analysis as case-cohort study, sampling the... more stcascoh is used to create an appropriate dataset for analysis as case-cohort study, sampling the cohort at time of entry and including all failures whether they occur in the random sample or not. To this aim stcascoh expands observations who fail in two parts: (1) time interval (t0,t-eps] and (2) time interval (t-eps,t]. This is version 1.2.1 of the software, revised to prepare data for use with stselpre (q.v.)
The Stata Journal: Promoting communications on statistics and Stata
In this article, we illustrate the command distrate, which calculates age-standardized rates with... more In this article, we illustrate the command distrate, which calculates age-standardized rates with efficient interval estimation by using formulas developed by Tiwari, Clegg, and Zou (2006, Statistical Methods in Medical Research 15: 547–569) as a modification of the method proposed by Fay and Feuer (1997, Statistics in Medicine 16: 791–801). This method is currently used in the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute in Bethesda, Maryland; the Italian Association of Cancer Registries (Associazione Italiana Registro Tumori, AIRTUM); and the Lombardy Mesothelioma and Sinonasal Cancer Registry in Northern Italy. The command produces a compact output and allows for the possibility of specifying a rate multiplier, for instance, x100,000 or x1,000,000. Furthermore, rates and confidence limits can be easily exported to an external dataset for further processing (for example, for making graphs). The command distrate is a useful addition to the of...
The Stata Journal: Promoting communications on statistics and Stata
Cancer registries are often interested in estimating net survival (NS), the probability of surviv... more Cancer registries are often interested in estimating net survival (NS), the probability of survival if the cancer under study is the only possible cause of death. Pohar Perme, Stare, and Estéve (2012, Biometrics 68: 113-120) proposed a new estimator of NS based on inverse probability weighting. They demonstrated that existing estimators of NS based on relative survival were biased, whereas the new estimator was unbiased. The new estimator was developed for continuous survival times, yet cancer registries often have only discrete survival times (for example, survival time in completed months or years). Therefore, we propose an approach to estimation for when survival times are discrete. In this article, we describe the stnet command for life-table estimation of NS, adapting the Pohar Perme estimation approach to life-table estimation. Estimates can be made using a period or hybrid approach in addition to the traditional cohort (or complete) approach, and age-standardized survival estimates are available.
Italian Stata Users Group Meetings 2008, 2009
Expert Review of Vaccines
Boston College Department of Economics, 2006
Background Increased success in the treatment of hematological cancers contributed to the increas... more Background Increased success in the treatment of hematological cancers contributed to the increase of 5-year survival for most adolescent and young adults (AYAs) with these tumours. However, as 5-year survival increased, it became clear that AYA long-term survivors were at increased risk for severe late effects. Moreover, limited information on long-term cancer impact is available for AYAs, since most studies focused on children and adolescents. We aimed to assess various long-term outcomes on AYA survivors of hematological cancers. Methods We selected patients diagnosed with a first primary hematological cancer between 1997 and 2006, in the Italian nationwide population-based cohort of AYA cancer survivors (i.e. alive at least 5 years after cancer diagnosis). Long-term outcomes of interest were: second malignant neoplasms (SMNs), hospitalizations and overall mortality. We calculated standardized incidence ratios (SIRs), standardized hospitalization rate ratios (SHRs) and standardiz...
Vaccine, 2022
Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on ... more Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Epidemiologia & Prevenzione, 2007
In cohort studies the event occurrence is usually described by the incidence rate and the survivo... more In cohort studies the event occurrence is usually described by the incidence rate and the survivor function. In comparison with these estimators the plot of the hazard function has the advantage to show the variations of the occurrence of the event along the period of observation, which often are important to be highlighted. Furthermore, when comparing individuals with different characteristics, the hazard function is a valuable support to check the assumption and to interpret the results of a Cox regression model. This paper illustrates the method for estimating the hazard function and an example is given from a real case by using the survival data of the breast cancers collected in the IMPACT study, aimed to detect the efficacy of the mammographic screening program. The relationship between the usual estimators and the hazard function is shown and its role in the survival regression modelling is emphasized. In the example the estimate of the hazard function allows to point out tha...
Statistical Software Components, 2007
stcoxgof is a post-estimation command testing the goodness of fit after a Cox model. So you must ... more stcoxgof is a post-estimation command testing the goodness of fit after a Cox model. So you must use this command after stcox. To compute Gronnesby and Borgan test and to obtain Arjas like plots Martingale residuals must also be saved specifying stcox's mgale() option; see help stcox. stcoxgof calls scoretest_cox, written by Isabel Canette of StataCorp, to compute score test statistics.
Statistical Software Components, 2005
stkerhaz computes nonparametric estimates of the baseline hazard or baseline SMR and draws the gr... more stkerhaz computes nonparametric estimates of the baseline hazard or baseline SMR and draws the graph of the results. This command can be used after stcox. In this case it requires that you previously specified stcox's basech option. Otherwise you can specify varname storing cumulative baseline hazard.
stselpre returns estimates and standard errors from proportional hazards fit to case-cohort data.... more stselpre returns estimates and standard errors from proportional hazards fit to case-cohort data. Coefficients are estimated according to two methods: Self-Prentice scheme. Risk sets uses just the sub-cohort member at risk, or Prentice scheme. Risk sets are augmented by non sub-cohort cases when they fail. The asymptotic Self-Prentice model variance-covariance matrix and standard errors are computed using the simplification described in Therneau and Li (1999). The stcascoh routine is used to generate data in the appropriate format for stselpre. This is version 1.1.0 of the software.
stpepemori tests the equality of cumulative incidences or conditional probabilities across two gr... more stpepemori tests the equality of cumulative incidences or conditional probabilities across two groups. So varname specifying the groups to be compared can take just two values.
stcompadj estimates the adjusted cumulative incidence function based on a Cox or a flexible param... more stcompadj estimates the adjusted cumulative incidence function based on a Cox or a flexible parametric regression model in the presence of competing risks. Cox regression in the presence of competing risks is usually performed by fitting separate models for each failure type. It is possible to obtain the same results by using a single analysis after appropriately adapting the data set. In short this consists of expanding each observation for each cause of failure, creating a stratum indicator taking on a value of 1 for the first n records, 2 for the following n records and so on, and modifying the failure indicator so that it attains the value 1 for each observation of death caused by the main event in the first stratum, for each observation of death caused by the competing event in the second stratum and so on. This way of representing data (expanded format) allows to model both identical and different effects of the same covariate on the main and competing events.
In survival or cohort studies the failure of an individual may be one of several distinct failure... more In survival or cohort studies the failure of an individual may be one of several distinct failure types. In such a situation we observe an event of interest and one or more competing events whose occurrence precludes or alters the probability of occurence of the first one. stcompet creates variables containing Cumulative Incidence, a function that in this case appropriately estimates the probability of occurrence of each endpoint, corresponding Standard Error and Confidence Bounds. The values in numlist of the previous stset are assumed as occurrence of event of interest. In compet() options you can specify numlist relating to the occurrence of up to six competing events. This version has been updated from that published in Stata Journal, 4:2.
The cumulative risk (CR) of developing cancer is a measure of the spread of cancer in a populatio... more The cumulative risk (CR) of developing cancer is a measure of the spread of cancer in a population used because it is intuitive to understand. However, there are various methods to calculate it leading to results with different interpretations. With real data we calculated the CR of developing any tumour in males and females to age 84 using three approaches. With the former, which uses only the cancer incidence, a CR to age 84 is estimated equal to 51.7% in males and 36% in females. With the second, which takes into account the competing risk of dying for other causes, the CR to age 84 is estimated equal to 44% in males and 32.9% in females. Finally, after adjusting for multiple primaries in the same person, the CR to age 84 boils down to 37.7% in males and 29.2% in females. Only methods taking into account the competing risk of dying for other causes and adjusting for multiple primaries in the same person are appropriate to estimate the real risk of developing any tumour in the cou...
In several medical reports, the survival function is graphed along with the confidence intervals.... more In several medical reports, the survival function is graphed along with the confidence intervals. The endpoints of the confidence intervals are usually connected to draw an area where the entire survival curve is contained with a given confidence. Confidence intervals are pointwise, i.e., they refer to the survival probability at a single time, but they are not valid for all the estimates of the entire survival curve. To this aim, the appropriate measure is confidence bands, not yet available within Stata. Two methods are usually employed to construct these confidence bands. The first was proposed by Hall and Wellner (1980), and the second was proposed by Nair (1984). The latter produces the so-called equal precision (EP) confidence bands. For both methods, log-minus-log and arcsine square-root transformed versions have been proposed. stcband is a new Stata command that allows the user to graph the survival function, together with the confidence bands constructed according to the Ha...
Research Papers in Economics, 2008
stcascoh is used to create an appropriate dataset for analysis as case-cohort study, sampling the... more stcascoh is used to create an appropriate dataset for analysis as case-cohort study, sampling the cohort at time of entry and including all failures whether they occur in the random sample or not. To this aim stcascoh expands observations who fail in two parts: (1) time interval (t0,t-eps] and (2) time interval (t-eps,t]. This is version 1.2.1 of the software, revised to prepare data for use with stselpre (q.v.)
The Stata Journal: Promoting communications on statistics and Stata
In this article, we illustrate the command distrate, which calculates age-standardized rates with... more In this article, we illustrate the command distrate, which calculates age-standardized rates with efficient interval estimation by using formulas developed by Tiwari, Clegg, and Zou (2006, Statistical Methods in Medical Research 15: 547–569) as a modification of the method proposed by Fay and Feuer (1997, Statistics in Medicine 16: 791–801). This method is currently used in the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute in Bethesda, Maryland; the Italian Association of Cancer Registries (Associazione Italiana Registro Tumori, AIRTUM); and the Lombardy Mesothelioma and Sinonasal Cancer Registry in Northern Italy. The command produces a compact output and allows for the possibility of specifying a rate multiplier, for instance, x100,000 or x1,000,000. Furthermore, rates and confidence limits can be easily exported to an external dataset for further processing (for example, for making graphs). The command distrate is a useful addition to the of...
The Stata Journal: Promoting communications on statistics and Stata
Cancer registries are often interested in estimating net survival (NS), the probability of surviv... more Cancer registries are often interested in estimating net survival (NS), the probability of survival if the cancer under study is the only possible cause of death. Pohar Perme, Stare, and Estéve (2012, Biometrics 68: 113-120) proposed a new estimator of NS based on inverse probability weighting. They demonstrated that existing estimators of NS based on relative survival were biased, whereas the new estimator was unbiased. The new estimator was developed for continuous survival times, yet cancer registries often have only discrete survival times (for example, survival time in completed months or years). Therefore, we propose an approach to estimation for when survival times are discrete. In this article, we describe the stnet command for life-table estimation of NS, adapting the Pohar Perme estimation approach to life-table estimation. Estimates can be made using a period or hybrid approach in addition to the traditional cohort (or complete) approach, and age-standardized survival estimates are available.
Italian Stata Users Group Meetings 2008, 2009