A two-stage approach for joint modeling of longitudinal measurements and competing risks data

Joint modeling of longitudinal measurements and time-to-event data is used in many practical studies of medical sciences. Most of the time, particularly in clinical studies and health inquiry, there are more than one event and they compete for failing an individual. In this situation, assessing the competing risk failure time is important. In most cases, implementation of joint modeling involves complex calculations. Therefore, we propose a two-stage method for joint modeling of longitudinal measurements and competing risks (JMLC) data based on the full likelihood approach via the conditional EM (CEM) algorithm. In the first stage, a linear mixed effect model is used to estimate the parameters of the longitudinal sub-model. In the second stage, we consider a cause-specific sub-model to construct competing risks data and describe an approximation for the joint log-likelihood that uses the estimated parameters of the first stage. We express the results of a simulation study and perform this method on the "standard and new anti-epileptic drugs" trial to check the effect of drug assaying on the treatment effects of lamotrigine and carbamazepine through treatment failure.

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

Molecular detection of Trypanosoma evansi based on ITS1 rDNA gene in Camelus dromedarius in Sistan Region, Iran

Trypanosomiasis is a disease caused by a flagellate protozoon called Trypanosoma and can be mechanically transmitted by vectors to humans and animals. Various species of Trypanosoma are found in livestock and poultry, which include Trypanosoma evansi, T. brucei, T. vivax and T. congolense. The camel is the most sensitive livestock for T. evansi, so the exact identification of infection is very important for epidemiological studies and the design of control programs. The present study was conducted with the aim of molecular detection of camel trypanosomiasis in the Sistan region in 2015. Previous studies have shown that internal transcribed spacer one (ITS1) of the ribosomal DNA is a reliable genetic marker for carrying out systematic molecular studies of trypanosomes. In order to investigate infections of camels with T. evansi, a total of 113 blood samples were collected randomly and the presence of parasites in each sample was evaluated using the microscopic method and polymerase chain reaction (PCR) test. Genomic DNA was extracted and the ITS-1 was amplified by PCR. In comparison to the nucleotide sequence obtained with the sequences recorded in GenBank, it was determined that there is a 99% homology with the recorded sequence of T. evansi. The obtained sequence was registered in Gen Bank with kx900449 code. The T. evansi sequences from different countries such as India, Taiwan, Thailand, the Philippines, China and Argentina and etc., were extracted from the Gene bank and aligned using the ClustalW2 sequence alignment tool and MEGA software. In this study the prevalence of T. evansi infection using the molecular method was 6.19% and no positive samples were found by microscopic observation.

A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies

Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.

An index of local sensitivity to non-ignorability for multivariate longitudinal mixed data with potential non-random dropout

Multivariate longitudinal data with mixed continuous and discrete responses with the possibility of non-ignorable missingness are often common in follow-up medical studies and their analysis needs to be developed. Standard methods of analysis based on the strong and the unverifiable assumption of missing at random (MAR) mechanism could be highly misleading. A way out of this problem is to start with methods that simultaneously allow modelling non-ignorable mechanism, which includes somehow troubling computations that are often time consuming, then we can use a sensitivity analysis, in which one estimates models under a range of assumptions about non-ignorability parameters to study the impact of these parameters on key inferences. A general index of sensitivity to non-ignorability (ISNI) to measure sensitivity of key inferences in a neighborhood of MAR model without fitting a complicated not MAR (NMAR) model for univariate generalized linear models and for models used for univariate longitudinal normal and non-Gaussian data with potentially NMAR dropout are well presented in the literature. In this paper we extend ISNI methodology to analyze multivariate longitudinal mixed data subject to non-ignorable dropout in which the non-ignorable dropout model could be dependent on the mixed responses. The approach is illustrated by analyzing a longitudinal data set in which the general substantive goal of the study is to better understand the relations between parental assessment of child's antisocial behavior and child's reading recognition skill.

A transition model for ordinal response data with random dropout: an application to the fluvoxamine data

Many methods are available to analyze incomplete longitudinal ordinal responses. In this paper a general transition model is proposed for longitudinal ordinal responses with random dropout. Maximum likelihood estimates are obtained for the transition probabilities when there are repeated observations. The likelihood function of the general model is partitioned to make possible the use of existing software to estimate model parameters. Some reduced forms of the model are also considered where for estimation of parameters in these models one has to use numerical optimization methods. The approach is applied to the well-known Fluvoxamine data. For these data, two important results, which have not been previously reported, are obtained: (1) some transition probabilities are estimated to be zero and (2) the model for current response, which conditions on previous response, removes the effects of some covariates that had previously been significant.

First service pregnancy rates following post-AI use of HCG in Ovsynch and Heatsynch programmes in lactating dairy cows

Lactating dairy cows (n = 667) at random stages of the oestrous cycle were assigned to either ovsynch (O, n = 228), heatsynch (H, n = 252) or control (C, n = 187) groups. Cows in O and H groups received 100 microg of GnRH agonist, i.m. (day 0) starting at 44 +/- 3 days in milk (DIM), and 500 microg of cloprostenol, i.m. (day 7). In O group, cows received 100 microg of GnRH (day 9) and were artificially inseminated without oestrus detection 16-20 h later. In H group, cows received 1 mg oestradiol benzoate (EB) i.m., 24 h after the cloprostenol injection and were artificially inseminated without oestrus detection 48-52 h after the EB injection. Cows in C group were inseminated at natural oestrus. On the day of artificial insemination (AI), cows in all groups were assigned to subgroups as follows: human Chorionic Gonadotrophin (O-hCG) (n = 112), O-saline (n = 116), H-hCG (n = 123), H-saline (n = 129), C-hCG (n = 94) and C-saline (n = 93) subgroups. Cows in hCG and saline subgroups received 3000 IU hCG i.m. and or 10 ml saline at day 5 post-AI (day 15), respectively. Pregnancy status was assessed by palpation per rectum at days 40 to 45 after AI. The logistic regression model using just main effects of season (summer and winter), parity (primiparous and pluriparous), method(1) (O, H and C) and method(2) (hCG and saline) showed that all factors, except method(1), were significant. Significant effects of season (p < 0.01), hCG and parity (p < 0.01), and a trend of parity and season (p < 0.1) were detected. A clear negative effect of warm period on first service pregnancy rate was noted (p < 0.01). The pregnancy rate was the lowest in the H protocol during warm period (p < 0.05). Treatment with hCG 5 days after AI significantly improved pregnancy rates in those cows that were treated with the H protocol compared with saline treatments (41.5% vs 24.8%; p < 0.01). O and H were more effective in primiparous than in pluriparous cows (46.1% vs 29.9%; p < 0.1 and 43.6% vs 24.6%; p < 0.01). First service pregnancy rates were higher in primiparous hCG-treated than in pluriparous hCG-treated cows (57.9% vs 32.3%; p < 0.01). The pregnancy rate was higher for the hCG-treated cows compared with saline-treated cows during warm period (37.9% vs 23.6%; p < 0.001).

Incidence and risk factors of neonatal hypothermia at referral hospitals in Tehran, Islamic Republic of Iran

To identify the incidence rate and risk factors of neonatal hypothermia at referral hospitals in Tehran, Islamic Republic of Iran, 900 neonates were randomly selected. Body temperature was measured repeatedly at different time points after birth. More than 50% became hypothermic soon after birth. Multiple regression analysis showed that low birth weight, low gestational age environmental temperature, low Apgar score, multiple pregnancy and receiving cardiopulmonary resuscitation were significantly associated with hypothermia. These findings suggested that there is an urgent need to sensitize and educate all levels of staff dealing with neonates in our country.