# L.F. Meira-MachadoUniversity of Minho · Departamento de Matemática (DMAT)

L.F. Meira-Machado

PhD

## About

64

Publications

16,653

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945

Citations

Citations since 2017

Introduction

Additional affiliations

September 1993 - present

## Publications

Publications (64)

An important aim in biomedical studies is to study how an intermediate event and prognostic factors influence the course of disease of a patient. In most cases, the effect of the intermediate event is considered a timedependent covariate and studied using extensions of the Cox proportional hazards model. Additionally, many of these studies often in...

Purpose:
To observe whether the initial implant stability, evaluated by two different methods-the insertion torque value (ITV) and implant stability quotient (ISQ)-may be influenced by different clinical conditions as well as to understand whether it is possible to establish an overall positive correlation between both methods and whether the obta...

In many longitudinal studies, information is collected on the times of
different kinds of events. Some of these studies involve repeated events, where a subject or sample unit may experience a well-defined event several times throughout their history. Such events are called recurrent events. In this paper, we introduce nonparametric methods for est...

Multi-state models are a useful tool for analyzing survival data with multiple events. The transition probabilities play an important role in these models since they allow for long-term predictions of the process in a simple and summarized manner. Recent papers have used the idea of subsampling to estimate these quantities, providing estimators wit...

Generalized additive models provide a flexible and easily-interpretable method for uncovering a nonlinear relationship between response and covariates. In many situations, the effect of a continuous covariate on the response varies across groups defined by the levels of a categorical variable. When confronted with a considerable number of groups de...

The development of applications for obtaining interpretable results in a simple and summarized manner in multi-state models is a research field with great potential, namely in terms of using open source tools that can be easily implemented in biomedical applications. In this tutorial, we introduce MSM.app, an interactive web application using the S...

Purpose: The development of applications for obtaining interpretable
results in a simple and summarized manner in multi-state mod-
els is a research �eld with great potential, namely in terms
of using open source tools that can be easily implemented in
biomedical applications. This paper introduces MSM.app, an interac-
tive web application using th...

Background
Crown-to-implant ratio and crown height space, associated with the use of short implants, have been related with marginal bone loss. However, it is unclear which of the two entities would play the most important role on the bone remodelling process. Using a finite element analysis, the present work aims to help clarifying how those two f...

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time. The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate "diseased" state before entering into a te...

The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. This assumption has an important role in the estimation of the transition probabilities. When the multi-state model is Markovian, the Aalen–Johansen estimator gives consist...

In many situations, it could be interesting to ascertain whether groups of curves can be performed, especially when confronted with a considerable number of curves. This paper introduces an R package, known as clustcurv, for determining clusters of curves with an automatic selection of their number. The package can be used for determining groups in...

Purpose/Background: Sarcopenia (decrease of muscle mass and function) has been linked with atherosclerosis [1]. The EWGSOP2 updated consensus, uses low muscle strength as the primary indicator of sarcopenia [2]. It is acknowledged that strength is better than mass for predicting adverse outcomes [2]. Handgrip strength (HGS) is a simple assessment t...

Background: One of the most serious complications of diabetes mellitus (DM) is a diabetic foot ulcer
(DFU), with lower extremity amputation (LEA).
Aims: This study aims to explore the role of anxiety and depression on mortality, reamputation and
healing, after a LEA due to DFU.
Methods: A sample of 149 patients with DFU who underwent LEA answered t...

Background:
One of the most serious complications of diabetes mellitus (DM) is a diabetic foot ulcer (DFU), with lower extremity amputation (LEA).
Aims:
This study aims to explore the role of anxiety and depression on mortality, reamputation and healing, after a LEA due to DFU.
Methods:
A sample of 149 patients with DFU who underwent LEA answe...

Simulation studies play an important role in the evaluation of the performance of a variety of statistical methods. Such assessment is performed under computer intensive procedures and cannot be achieved with studies of real data alone. These studies are increasingly employed in evaluating the properties of the proposed methods being the generation...

In this work, we revisit the problem of estimation of the transition probabilities of an irreversible, possibly non-Markov model. However, unlike the previous contributions, we are interested to estimate these probabilities given a continuous covariate measured repeatedly over time. To this end we will use the subsampling approach, also termed as l...

The topic of joint modeling of longitudinal and survival data has received remarkable attention in recent years. In cancer studies for example, these models can be used to assess the impact that a longitudinal marker has on the time to death or relapse. Analyzes of such studies, in which individuals may experience several events, can be successfull...

Multi-state models can be successfully used for describing complicated event history data, for example, describing stages in the disease progression of a patient. In these models one important goal is the estimation of the transition probabilities since they allow for long term prediction of the process. Traditionally these quantities have been est...

One major goal in clinical applications of multi-state models is the estimation of transition probabilities. Estimators based on subsampling were recently introduced by de Uña-Álvarez and Meira-Machado to estimate these quantities, and their superiority with respect to the competing estimators has been proved in situations in which the Markov condi...

Organizations interact with the environment and with other organizations, and these interactions constitute an important way of learning and evolution. To overcome the problems that they face during their existence, organizations must certainly adopt survival strategies, both individually and in group. The aim of this study is to evaluate the effec...

Multistate models can be successfully used for describing complex event history data, for example, describing stages in the disease progression of a patient. The so‐called “illness‐death” model plays a central role in the theory and practice of these models. Many time‐to‐event datasets from medical studies with multiple end points can be reduced to...

Prevalence of overweight and obesity in young children has risen dramatically in the last decades in most developed countries. Childhood overweight and obesity are known to have immediate and long-term health consequences and are now recognized important public health concerns. We used a Markov 4-state model with states defined by four body mass in...

Survival analysis includes a wide variety of methods for analyzing time‐to‐event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for censored data. When the null hypothesis of eq...

Survival analysis includes a wide variety of methods for analyzing time-to-event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for cen-sored data. When the null hypothesis of e...

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time. The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate 'diseased' state before entering into a te...

Introduction
Childhood obesity is of major public health concern with significant health, social and economic impacts, having even potential to reverse the increase in longevity that has been observed. We aimed to estimate the instantaneous rate of transition among various states of body mass index (BMI) categories, in children and to evaluate the...

We present the R npregfast package via some applications involved with the study of living organisms. The package implements nonparametric estimation procedures in regression models with or without factor-by-curve interactions. The main feature of the package is its ability to perform inference regarding these models. Namely, the implementation of...

In many longitudinal studies information is collected on the times of different kinds of events. Some of these studies involve repeated events, where a subject or sample unit may experience a well-defined event several times along his history. Such events are called recurrent events. In this work we consider the estimation of the marginal and joint...

The Cox proportional hazards model is themost widely used survival prediction model for analysing time-to-event data. To measure the discrimination ability of a survival model the concordance probability index is widely used. In this work we studied and compared the performance of two different estimators of the concordance probability when a conti...

One major goal in clinical applications of time-to-event data is the estimation of survival with censored data. The usual nonparametric estimator of the survival function is the time-honored Kaplan-Meier product-limit estimator. Though this estimator has been implemented in several R packages, the development of the condSURV R package has been moti...

For the central parish of the city of Guimarães, Oliveira, from the late 16th century to the early 20th century, and by using the methodology of parish reconstitution, we have a demographical/genealogical database organized from nominative data of baptism, marriage and burial registers.
By extending the research up to 1910, we aim to systematically...

In multiple regression models, when there are a large number (p) of explanatory variables which may or may not be relevant for predicting the response, it is useful to be able to reduce the model. To this end, it is necessary to determine the best subset of q (q ≤ p) predictors which will establish the model with the best prediction capacity. FWDse...

One important goal in clinical applications of multi-state models is the estimation of transition probabilities. Recently, landmark estimators were proposed to estimate these quantities, and their superiority with respect to the competing estimators has been proved in situations in which the Markov condition is violated. As a weakness, it provides...

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual...

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional di...

One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions of the process. In recent years significant contributions have been made regarding this topic. However, most of the approaches assume indep...

One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978). However, two problems may arise from using this estimator: first, its standard error m...

In many medical studies, patients may experience several events during follow-up. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work we consider the estimation of the bivariate distribution function for censored gap times. Some related problems such as...

Nonparametric regression with a doubly truncated response is introduced. Local constant and local linear kernel-type estimators are proposed. Asymptotic expressions for the bias and the variance of the estimators are obtained, showing the deterioration provoked by the random truncation. To solve the crucial problem of bandwidth choice, two differen...

In many medical studies, there are covariates that change their values over time and their analysis is most often modeled using the Cox regression model. However, many of these time-dependent covariates can be expressed as an intermediate event, which can be modeled using a multi-state model. Using the relationship of time-dependent (discrete) cova...

Times between consecutive events are often of interest in medical studies. Usually the events represent different states of the disease process and are modeled using multi-state models. This paper introduces and studies a feasible estimation method for the transition probabilities in a progressive three-state model. We assume that the vector of gap...

One major goal in clinical applications of multi-state models is the estimation
of transition probabilities. The usual nonparametric estimator of the transition
matrix for non-homogeneous Markov processes is the Aalen-Johansen
estimator (Aalen and Johansen [1]). However, two problems may arise from
using this estimator: �first, its standard error m...

One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978 [1]). In this paper we propose a modification of the Aalen-Johansen estimator in the ill...

The Cox proportional hazards regression model has become the traditional choice for modeling survival data in medical studies. To introduce flexibility into the Cox model, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuou...

In this paper nonparametric regression with a doubly truncated response is introduced. Local
constant and local linear kernel-type estimators are proposed. Asymptotic expressions for the bias
and the variance of the estimators are obtained, showing the deterioration provoked by the random
truncation. To solve the crucial problem of bandwidth choice...

One important goal in multi-state modeling is the estimation of transition
probabilities. In longitudinal medical studies these quantities are particularly
of interest since they allow for long-term predictions of the process. In recent
years signifi�cant contributions have been made regarding this topic. However,
most of the approaches assume inde...

In many medical studies, patients can experience several events. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work we consider the estimation of the bivariate distribution function for censored gap times, using survivalBIV a software application for R...

One major goal in clinical applications of multi-state models is the estimation of transition probabilities. In a recent paper, Meira-Machado etÂ al. (2006) introduce a substitute for the Aalen-Johansen estimator in the case of a non-Markov illness-death model. The idea behind their estimator is to weight the data by the Kaplan-Meier weights pertai...

In longitudinal studies of disease, patients can experience several events across a follow-up period. Analysis of such studies can be successfully performed by multi-state models. This paper considers nonparametric and semiparametric estimation of important targets in multi-state modeling, such as the transition probabilities and bivariate distribu...

Let (T1,T2) be gap times corresponding to two consecutive events, which are observed subject to (univariate) random right-censoring. The censoring variable corresponding to the second gap time T2 will in general depend on this gap time. Suppose the vector (T1,T2) satisfies the nonparametric location-scale regression model T2=m(T1)+σ(T1)ɛ, where the...

In longitudinal studies of disease, patients can experience several events across a followup period. Analysis of such studies can be successfully performed by multi-state models. In the multi-state framework, issues of interest include the study of the relationship between covariates and disease evolution, estimation of transition probabilities, an...

Multi-state models (MSMs) are very useful for describing complicated event history data. These models may be considered as a generalization of survival analysis where survival is the ultimate outcome of interest but where intermediate (transient) states are identified. One major goal in clinical applications of MSMs is to study the relationship bet...

Let (T1,T2) be gap times corresponding to two consecutive events, which are observed subject to random right-censoring. In this paper a simple estimator of the bivariate distribution function of (T1,T2) is proposed. We investigate the conditions under which the introduced estimator is consistent. Applications to the estimation of the marginal distr...

The experience of a patient in a survival study may be modelled as a process with two states and one possible transition from an "alive" state to a "dead" state. In some studies, however, the "alive" state may be partitioned into two or more intermediate (transient) states, each of which corresponding to a particular stage of the illness. In such s...

In longitudinal studies of disease, patients can experience several events through a follow-up period. In these studies, the sequentially ordered events (gap times) are often of interest. The events of concern may be of the same nature (e.g. cancer patients may experience recurrent disease episodes) or represent different states in the disease proc...

The aim of this paper is to present an R library, called tdc.msm, developed to analyze multi-state survival data. In this library, the time-dependent regression model and multi-state models are included as two possible approaches for such data. For the multi-state modelling five different models are considered, allowing the user to choose between M...

Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Mark...

Let (T1;T2) be gap times corresponding to two consecutive events, which are observed subject to random right-censoring, and suppose the vector (T1;T2) satises the nonparametric location-scale regression model T2 = m(T1) + (T1)", where the functions m and are 'smooth', and " is independent of T1. The aim of this paper is twofold. First, we propose a...

In many medical studies, patients may experience several events. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work we consider a new nonparametric estimator of the bivariate distribution function for censored gap times. We explore the behaviour of the...

Resumo: A experiência de um paciente num estudo de sobrevivência pode ser pensada como um processo que envolve dois estados, com uma única transição do estado "vivo" para "morte". Em alguns estudos, o estado representando os pacientes "vivos" pode ser subdividido em dois ou mais estados intermédios, cada um correspondendo a um estado particular no...

The introduction of time-dependent covariates in the survival process can make the patients survival change from one time point to the next as the values of the covariate change. A popular choice for the analysis of this data is the time-dependent Cox regression model. In the present work we present multi-state models as an alternative for the anal...