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1.  Longitudinal Studies With Outcome-Dependent Follow-up: Models and Bayesian Regression 
We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study.
doi:10.1198/00
PMCID: PMC2288578  PMID: 18392118
Bayesian cubic smoothing spline; Latent variable; Partially linear model
2.  Health systems performance in sub-Saharan Africa: governance, outcome and equity 
BMC Public Health  2011;11:237.
Background
The literature on health systems focuses largely on the performance of healthcare systems operationalised around indicators such as hospital beds, maternity care and immunisation coverage. A broader definition of health systems however, needs to include the wider determinants of health including, possibly, governance and its relationship to health and health equity. The aim of this study was to examine the relationship between health systems outcomes and equity, and governance as a part of a process to extend the range of indicators used to assess health systems performance.
Methods
Using cross sectional data from 46 countries in the African region of the World Health Organization, an ecological analysis was conducted to examine the relationship between governance and health systems performance. The data were analysed using multiple linear regression and a standard progressive modelling procedure. The under-five mortality rate (U5MR) was used as the health outcome measure and the ratio of U5MR in the wealthiest and poorest quintiles was used as the measure of health equity. Governance was measured using two contextually relevant indices developed by the Mo Ibrahim Foundation.
Results
Governance was strongly associated with U5MR and moderately associated with the U5MR quintile ratio. After controlling for possible confounding by healthcare, finance, education, and water and sanitation, governance remained significantly associated with U5MR. Governance was not, however, significantly associated with equity in U5MR outcomes.
Conclusion
This study suggests that the quality of governance may be an important structural determinant of health systems performance, and could be an indicator to be monitored. The association suggests there might be a causal relationship. However, the cross-sectional design, the level of missing data, and the small sample size, forces tentative conclusions. Further research will be needed to assess the causal relationship, and its generalizability beyond U5MR as a health outcome measure, as well as the geographical generalizability of the results.
doi:10.1186/1471-2458-11-237
PMCID: PMC3095561  PMID: 21496303
3.  Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors 
Bayesian analysis (Online)  2008;3(3):585-614.
In this paper, we consider theoretical and computational connections between six popular methods for variable subset selection in generalized linear models (GLM’s). Under the conjugate priors developed by Chen and Ibrahim (2003) for the generalized linear model, we obtain closed form analytic relationships between the Bayes factor (posterior model probability), the Conditional Predictive Ordinate (CPO), the L measure, the Deviance Information Criterion (DIC), the Aikiake Information Criterion (AIC), and the Bayesian Information Criterion (BIC) in the case of the linear model. Moreover, we examine computational relationships in the model space for these Bayesian methods for an arbitrary GLM under conjugate priors as well as examine the performance of the conjugate priors of Chen and Ibrahim (2003) in Bayesian variable selection. Specifically, we show that once Markov chain Monte Carlo (MCMC) samples are obtained from the full model, the four Bayesian criteria can be simultaneously computed for all possible subset models in the model space. We illustrate our new methodology with a simulation study and a real dataset.
doi:10.1214/08-BA323
PMCID: PMC2680310  PMID: 19436774
Bayes factor; Conditional Predictive Ordinate; Conjugate prior; L measure; Poisson regression; Logistic regression
4.  Bayesian Design of Superiority Clinical Trials for Recurrent Events Data with Applications to Bleeding and Transfusion Events in Myelodyplastic Syndrome 
Biometrics  2014;70(4):1003-1013.
Summary
In many biomedical studies, patients may experience the same type of recurrent event repeatedly over time, such as bleeding, multiple infections and disease. In this article, we propose a Bayesian design to a pivotal clinical trial in which lower risk myelodysplastic syndromes (MDS) patients are treated with MDS disease modifying therapies. One of the key study objectives is to demonstrate the investigational product (treatment) effect on reduction of platelet transfusion and bleeding events while receiving MDS therapies. In this context, we propose a new Bayesian approach for the design of superiority clinical trials using recurrent events frailty regression models. Historical recurrent events data from an already completed phase 2 trial are incorporated into the Bayesian design via the partial borrowing power prior of Ibrahim et al. (2012, Biometrics 68, 578–586). An efficient Gibbs sampling algorithm, a predictive data generation algorithm, and a simulation-based algorithm are developed for sampling from the fitting posterior distribution, generating the predictive recurrent events data, and computing various design quantities such as the type I error rate and power, respectively. An extensive simulation study is conducted to compare the proposed method to the existing frequentist methods and to investigate various operating characteristics of the proposed design.
doi:10.1111/biom.12215
PMCID: PMC4276515  PMID: 25041037
Clinical trial design; Gibbs sampling; Myelodysplastic syndrome; Power prior; Recurrent events; Type I error rate and power
5.  Prevalence of Impacted Molar Teeth among Saudi Population in Asir Region, Saudi Arabia – A Retrospective Study of 3 Years 
Aim: To report the prevalence of impacted third molars according to the age, gender and type among Saudi population.
Materials and methods: This retrospective study involved 3800 panoramic radiographs of subjects aged 18 to 45 years who presented to the College of Dentistry, King Khalid University, Abha, Kingdom of Saudi Arabia for oral care during the period from February 2009 to February 2011. Data collected was entered into a spreadsheet (Excel 2000; Microsoft, US) and analyzed using Statistical Package for Social Sciences (SPSS) version 16.0. Results: A total of 713 impacted teeth were identified (18.76%) (p=0.003). The male to female ratio with impacted third molars was 604:109 (5.54:1) and the ratio of patients with impacted teeth was (5:1). Age group 1 (i.e., 20 to 25 years)had the highest prevalence of third molar tooth impaction (64.5%) and this decreased with increasing age. Conclusion: Incidence of tooth impaction is higher in the mandible than in maxilla. Males had a higher incidence of third molar impaction as compared to the females. Highest incidence is found in the age group of 20-25 years. Mesio-angular impaction was the most predominant type.
How to cite this article: Syed KB, Kota Z, Ibrahim M, Bagi MA, Assiri MA. "Prevalence of Impacted Molar Teeth among Saudi Population in Asir Region, Saudi Arabia – A Retrospective Study of 3 Years". J Int Oral Health 2013; 5(1):43-47.
PMCID: PMC3768082  PMID: 24155577
Impacted; third molar; mandibular; maxillary
6.  A review of the handling of missing longitudinal outcome data in clinical trials 
Trials  2014;15:237.
The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%). Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials.
doi:10.1186/1745-6215-15-237
PMCID: PMC4087243  PMID: 24947664
Review; Missing; Data; Handling; Longitudinal; Repeated; Measures
7.  Variable Selection in the Cox Regression Model with Covariates Missing at Random 
Biometrics  2009;66(1):97-104.
Summary
We consider variable selection in the Cox regression model (Cox, 1975, Biometrika 362, 269–276) with covariates missing at random. We investigate the smoothly clipped absolute deviation penalty and adaptive least absolute shrinkage and selection operator (LASSO) penalty, and propose a unified model selection and estimation procedure. A computationally attractive algorithm is developed, which simultaneously optimizes the penalized likelihood function and penalty parameters. We also optimize a model selection criterion, called the ICQ statistic (Ibrahim, Zhu, and Tang, 2008, Journal of the American Statistical Association 103, 1648–1658), to estimate the penalty parameters and show that it consistently selects all important covariates. Simulations are performed to evaluate the finite sample performance of the penalty estimates. Also, two lung cancer data sets are analyzed to demonstrate the proposed methodology.
doi:10.1111/j.1541-0420.2009.01274.x
PMCID: PMC3303197  PMID: 19459831
ALASSO; Missing data; Partial likelihood; Penalized likelihood; Proportional hazards model; SCAD; Variable selection
8.  Flexible Cure Rate Modeling Under Latent Activation Schemes 
With rapid improvements in medical treatment and health care, many datasets dealing with time to relapse or death now reveal a substantial portion of patients who are cured (i.e., who never experience the event). Extended survival models called cure rate models account for the probability of a subject being cured and can be broadly classified into the classical mixture models of Berkson and Gage (BG type) or the stochastic tumor models pioneered by Yakovlev and extended to a hierarchical framework by Chen, Ibrahim, and Sinha (YCIS type). Recent developments in Bayesian hierarchical cure models have evoked significant interest regarding relationships and preferences between these two classes of models. Our present work proposes a unifying class of cure rate models that facilitates flexible hierarchical model-building while including both existing cure model classes as special cases. This unifying class enables robust modeling by accounting for uncertainty in underlying mechanisms leading to cure. Issues such as regressing on the cure fraction and propriety of the associated posterior distributions under different modeling assumptions are also discussed. Finally, we offer a simulation study and also illustrate with two datasets (on melanoma and breast cancer) that reveal our framework’s ability to distinguish among underlying mechanisms that lead to relapse and cure.
doi:10.1198/016214507000000112
PMCID: PMC2964090  PMID: 21031152
Bayesian hierarchical model; Cure fraction; Cure rate model; Latent activation scheme; Markov chain Monte Carlo algorithm; Moment-generating functions; Survival analysis
9.  Effects of small particle numbers on long-term behaviour in discrete biochemical systems 
Bioinformatics  2014;30(17):i475-i481.
Motivation: The functioning of many biological processes depends on the appearance of only a small number of a single molecular species. Additionally, the observation of molecular crowding leads to the insight that even a high number of copies of species do not guarantee their interaction. How single particles contribute to stabilizing biological systems is not well understood yet. Hence, we aim at determining the influence of single molecules on the long-term behaviour of biological systems, i.e. whether they can reach a steady state.
Results: We provide theoretical considerations and a tool to analyse Systems Biology Markup Language models for the possibility to stabilize because of the described effects. The theory is an extension of chemical organization theory, which we called discrete chemical organization theory. Furthermore we scanned the BioModels Database for the occurrence of discrete chemical organizations. To exemplify our method, we describe an application to the Template model of the mitotic spindle assembly checkpoint mechanism.
Availability and implementation: http://www.biosys.uni-jena.de/Services.html.
Contact: bashar.ibrahim@uni-jena.de or dittrich@minet.uni-jena.de
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btu453
PMCID: PMC4147906  PMID: 25161236
10.  Fixed and Random Effects Selection in Mixed Effects Models 
Biometrics  2010;67(2):495-503.
SUMMARY
We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation (SCAD) and adaptive LASSO (ALASSO) penalty functions. The maximum penalized likelihood estimates are shown to posses consistency and sparsity properties and asymptotic normality. A model selection criterion, called the ICQ statistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu and Tang, 2008). The variable selection procedure based on ICQ is shown to consistently select important fixed and random effects. The methodology is very general and can be applied to numerous situations involving random effects, including generalized linear mixed models. Simulation studies and a real data set from an Yale infant growth study are used to illustrate the proposed methodology.
doi:10.1111/j.1541-0420.2010.01463.x
PMCID: PMC3041932  PMID: 20662831
ALASSO; Cholesky decomposition; EM algorithm; ICQ criterion; Mixed Effects selection; Penalized likelihood; SCAD
11.  Primary Effects of Intravitreal Bevacizumab in Patients with Diabetic Macular Edema 
Pakistan Journal of Medical Sciences  2013;29(4):1018-1022.
Objective: To evaluate the efficacy of primary intra vitreal bevacizumab (IVB) injection on macular edema in diabetic patients with improvement in best corrected visual acuity (BCVA) and central macular thickness (CMT) on optical coherence tomography (OCT).
Methods: This prospective interventional case series study was conducted at Retina Clinic, Al-Ibrahim Eye Hospital, and Isra Postgraduate Institute of Ophthalmology Karachi. Between December 2010 to June 2012. BCVA measurement with Early Treatment in Diabetic Retinopathy Study (ETDRS) charts and ophthalmic examination, including Slit-lamp bio microscopy, indirect ophthalmoscopy, Fundus fluorescein angiography (FFA) and OCT were done at the base line examination. At monthly interval all patients were treated with 3 injections of 0.05 ml intra vitreal injection containing 1.25 mg bevacizumab. Patients were followed up for 6 months and BCVA and OCT were taken at the final visit at 6 month.
Results: The mean BCVA at base line was 0.42±0.14 Log Mar units. This improved to 0.34±0.13, 0.25±0.12, 0.17±0.12 and 0.16±0.14 Log Mar units at 1 month after 1st, 2nd 3rd injections and at final visit at 6 months respectively, a difference that was statistically significant (P>0.0001) from base line. The mean 1mm CMT measurement was 452.9 ± 143.1 µm at base line, improving to 279.8 ± 65.2 µm (P<0.0001) on final visit. No serious complications were observed.
Conclusions: Primary IVB at a dose of 1.25 mg on monthly interval seems to provide stability and improvement in BCVA and CMT in patient with DME.
PMCID: PMC3817758  PMID: 24353679
Best Corrected Visual Acuity (BCVA); Central Macular Thickness (CMT); Diabetic Macular Edema (DME); Intra Vitreal Bevacizumab (IVB)
12.  Visual outcome and complications in Ab-externo scleral fixation IOL in aphakia in pediatric age group 
Objective: To assess the visual outcome and complications in patients after Ab-externo scleral fixation of intraocular lens in pediatric age group (15 years or less).
Methods: This quasi experimental study was conducted at Isra Postgraduate Institute of Ophthalmology, Al-Ibrahim Eye Hospital, Karachi, from January 2012 to December 2012. All cases included were worked up according to the protocol. All patients underwent Ab-externo scleral fixation of IOL under general anesthesia. Patients were followed up at 1stday, 1stweek, 1stmonth, 2ndmonth and 3rdmonth. Complete eye examination including best-corrected visual acuity and complications were noted on each visit.
Results: Thirty patients were included in the study, with mean age of 8.6 years (±3.93569). Most of the patients, 20 (66.7%), had visual acuities of 6/18 or better. No complication was seen in 18 (60%) of the patients intra operatively while soft eye was observed in 7 (23.3%) of the patients. Another complication noted was vitreous hemorrhage, which was seen in 5 (16.7%) patients. Most common post-operative complication was Uveitis followed by astigmatism. Lens dislocation and iris abnormalities were seen in only one patient. Most of the patients showed significant visual improvement after surgery.
Conclusion: Ab-externo scleral fixation of an IOL was found to be safe and showed favorable postoperative results with fewer complications.
PMCID: PMC3817764  PMID: 24353665
Astigmatism; Complication; Scleral fixation
13.  Prevalence of syphilis among antenatal clinic attendees in Karachi: Imperative to begin universal screening in Pakistan 
Objectives
Sexually transmitted infections are thought by some to be rare in socially conservative Muslim countries. Little is known about prevalence of syphilis in Pakistani women from the general population. We determined syphilis prevalence in a multi-center cross-sectional study of low risk pregnant women in Karachi, Pakistan.
Methods
We administered a structured questionnaire and obtained a blood sample for syphilis serology (rapid plasma reagin test with Treponema pallidum hemagglutination assay confirmation) from all women giving informed consent over six weeks in 2007.
Results
The prevalence of confirmed syphilis was less than one percent (0.9%; 95%CI: 0.4, 1.8) in a sample size of 800 women recruited from three urban sites (≈1% refusal rate). Women who lived in an area where male drug use is prevalent (Ibrahim Hyderi Hospital) had 1% (1.5%) higher prevalence rates than women from the other two sites 0.5%.
Conclusions
We documented higher-than-expected syphilis seroprevalence rates in a low risk population of antenatal clinic attendees in Pakistan. Bridge populations for syphilis may include drug users, who are usually married, and Hijras or their clients. Hijras are transgender and/or transvestite men who may provide sex for money to men. In accordance with our results, the national policy for syphilis control in Pakistan should be modified to include universal syphilis screening in antenatal clinics with subsequent partner notification.
PMCID: PMC3574871  PMID: 22356034
syphilis; prevalence; pregnancy; antenatal care; policy; Pakistan
14.  Missing Data in Clinical Studies: Issues and Methods 
Journal of Clinical Oncology  2012;30(26):3297-3303.
Missing data are a prevailing problem in any type of data analyses. A participant variable is considered missing if the value of the variable (outcome or covariate) for the participant is not observed. In this article, various issues in analyzing studies with missing data are discussed. Particularly, we focus on missing response and/or covariate data for studies with discrete, continuous, or time-to-event end points in which generalized linear models, models for longitudinal data such as generalized linear mixed effects models, or Cox regression models are used. We discuss various classifications of missing data that may arise in a study and demonstrate in several situations that the commonly used method of throwing out all participants with any missing data may lead to incorrect results and conclusions. The methods described are applied to data from an Eastern Cooperative Oncology Group phase II clinical trial of liver cancer and a phase III clinical trial of advanced non–small-cell lung cancer. Although the main area of application discussed here is cancer, the issues and methods we discuss apply to any type of study.
doi:10.1200/JCO.2011.38.7589
PMCID: PMC3948388  PMID: 22649133
15.  Are we missing the importance of missing values in HIV prevention Randomized clinical trials? Review and Recommendations 
AIDS and Behavior  2012;16(6):1382-1393.
Missing data in HIV prevention trials is a common complication to interpreting outcomes. Even a small proportion of missing values in randomized trials can cause bias, inefficiency and loss of power. We examined the extent of missing data and methods in which HIV prevention randomized clinical trials (RCT) have managed missing values. We used a database maintained by the HIV/AIDS Prevention Research Synthesis (PRS) Project at the Centers for Disease Control and Prevention (CDC) to identify related trials for our review. The PRS cumulative database was searched on June 15, 2010 and all citations that met the following criteria were retrieved: All RCTs which reported HIV/STD/HBV/HCV behavioral interventions with a biological outcome from 2005 to present.
Out of the 57 intervention trials identified, all had some level of missing values. We found that the average missing values per study ranged between 3% to 97%. Averaging over all studies the percent of missing values was 26%. None of the studies reported any assumptions for managing missing data in their RCTs. Under some relaxed assumptions discussed below, we expect only 12% of studies to report unbiased results. There is a need for more detailed and thoughtful consideration of the missing data problem in HIV prevention trials. In the current state of managing missing data we risk major biases in interpretations. Several viable alternatives are available for improving the internal validity of RCTs by managing missing data.
doi:10.1007/s10461-011-0125-6
PMCID: PMC3358416  PMID: 22223301
Incomplete data; missing data; bias; HIV prevention; RCT
16.  The impact of missing data on analyses of a time-dependent exposure in a longitudinal cohort: a simulation study 
Background
Missing data often cause problems in longitudinal cohort studies with repeated follow-up waves. Research in this area has focussed on analyses with missing data in repeated measures of the outcome, from which participants with missing exposure data are typically excluded. We performed a simulation study to compare complete-case analysis with Multiple imputation (MI) for dealing with missing data in an analysis of the association of waist circumference, measured at two waves, and the risk of colorectal cancer (a completely observed outcome).
Methods
We generated 1,000 datasets of 41,476 individuals with values of waist circumference at waves 1 and 2 and times to the events of colorectal cancer and death to resemble the distributions of the data from the Melbourne Collaborative Cohort Study. Three proportions of missing data (15, 30 and 50%) were imposed on waist circumference at wave 2 using three missing data mechanisms: Missing Completely at Random (MCAR), and a realistic and a more extreme covariate-dependent Missing at Random (MAR) scenarios. We assessed the impact of missing data on two epidemiological analyses: 1) the association between change in waist circumference between waves 1 and 2 and the risk of colorectal cancer, adjusted for waist circumference at wave 1; and 2) the association between waist circumference at wave 2 and the risk of colorectal cancer, not adjusted for waist circumference at wave 1.
Results
We observed very little bias for complete-case analysis or MI under all missing data scenarios, and the resulting coverage of interval estimates was near the nominal 95% level. MI showed gains in precision when waist circumference was included as a strong auxiliary variable in the imputation model.
Conclusions
This simulation study, based on data from a longitudinal cohort study, demonstrates that there is little gain in performing MI compared to a complete-case analysis in the presence of up to 50% missing data for the exposure of interest when the data are MCAR, or missing dependent on covariates. MI will result in some gain in precision if a strong auxiliary variable that is not in the analysis model is included in the imputation model.
doi:10.1186/1742-7622-10-6
PMCID: PMC3751092  PMID: 23947681
Simulation study; Missing exposure; Multiple imputation; Complete-case analysis; Repeated exposure measurement
17.  Toward an Understanding of Disengagement from HIV Treatment and Care in Sub-Saharan Africa: A Qualitative Study 
PLoS Medicine  2013;10(1):e1001369.
Norma Ware and colleagues conducted a large qualitative study among patients in HIV treatment programs in sub-Saharan Africa to investigate reasons for missed visits and provide an explanation for disengagement from care.
Background
The rollout of antiretroviral therapy in sub-Saharan Africa has brought lifesaving treatment to millions of HIV-infected individuals. Treatment is lifelong, however, and to continue to benefit, patients must remain in care. Despite this, systematic investigations of retention have repeatedly documented high rates of loss to follow-up from HIV treatment programs. This paper introduces an explanation for missed clinic visits and subsequent disengagement among patients enrolled in HIV treatment and care programs in Africa.
Methods and Findings
Eight-hundred-ninety patients enrolled in HIV treatment programs in Jos, Nigeria; Dar es Salaam, Tanzania; and Mbarara, Uganda who had extended absences from care were tracked for qualitative research interviews. Two-hundred-eighty-seven were located, and 91 took part in the study. Interview data were inductively analyzed to identify reasons for missed visits and to assemble them into a broader explanation of how missed visits may develop into disengagement. Findings reveal unintentional and intentional reasons for missing, along with reluctance to return to care following an absence. Disengagement is interpreted as a process through which missed visits and ensuing reluctance to return over time erode patients' subjective sense of connectedness to care.
Conclusions
Missed visits are inevitable over a lifelong course of HIV care. Efforts to prevent missed clinic visits combined with moves to minimize barriers to re-entry into care are more likely than either approach alone to keep missed visits from turning into long-term disengagement.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The human immunodeficiency virus (HIV) infects cells of the immune system, destroying or impairing their function. As the infection progresses, the immune system becomes weaker, and the affected person becomes more susceptible to life-threatening infections. Over the past three decades, 25 million people have died from HIV, and according to the World Health Organization, in 2011, there were roughly 34.2 million people living with HIV, over 60% of whom lived in sub-Saharan Africa. Although HIV cannot be cured, the virus can be suppressed by combination antiretroviral therapy (ART) consisting of three or more antiretroviral drugs. ART controls viral replication and strengthens the immune system, allowing the affected person to fight off infections. With ART, HIV can be managed as a chronic disease: people living with HIV can live healthy lives as long as they take antiretroviral drugs regularly for the rest of their lives.
Why Was This Study Done?
Unfortunately, poor retention in HIV programs is a huge problem: a large proportion—30%–60% in some settings in sub-Saharan Africa—of people starting ART, are lost to follow-up and stop taking treatment. Few studies have looked in depth at the reasons why people with HIV in sub-Saharan Africa miss clinic appointments or even stop coming altogether. So in this study in Tanzania, Uganda, and Nigeria, the researchers did a qualitative analysis from the patients' perspective on the reasons for missing clinic visits. Qualitative research can use information-gathering techniques, such as open-ended interviews, to develop an in-depth understanding of human behavior and the reasons behind such behavior.
What Did the Researchers Do and Find?
The researchers indentified people to interview by using “tracking lists” from HIV/AIDS care clinics in the three countries—patient tracking by clinical trackers is increasingly used as a way to contact patients who have missed clinic appointments. The researchers included people in the study who had been tracked and contacted by clinic trackers, had been absent from the clinic for three months or more, and gave consent to be re-contacted by the researchers. The researchers interviewed participants, using their local language, on several topics, including their experiences of care at the clinic and of tracking, and the circumstances of missed appointments. The detailed accounts were transcribed, and then the researchers categorized the reasons for missing appointments into intentional and unintentional reasons.
Eight-hundred-ninety patients in the three countries were tracked during the study period, but only 287 were located, of whom 91 participated in the study. Of the 91 participants, 76 were being prescribed ART, and 15 had not started treatment. The main unintentional reason for missing clinic visits was a conflicting demand on the patients' time, which was often unexpected and for complex reasons, such as caring for a dying relative, going to a funeral, or traveling to work. These reasons were often transient and changed over time. Intentional reasons were often related to dissatisfaction with the care received at the clinic, especially “the harsh treatment” they received from health workers, which typically referred to behavior perceived by patients to be rude. For example, participants reported being spoken to “roughly” or feeling that the clinic staff “didn't care.” Such behavior made patients feel hurt, humiliated, and angry, and reluctant to return to the clinic. The researchers found that, overall, disengagement from care appeared to be a process through which missed visits and subsequent reluctance to return over time eroded patients' sense of connectedness to care.
What Do These Findings Mean?
Absences from care will be inevitable over a lifetime course of treatment for HIV/AIDS. These findings indicate that absences may be unintentional as well as intentional, and that the reasons are complex and can change over time. Initial reasons for missing may disappear, leaving patients free, but reluctant, to return to care. Reasons for reluctance include shame at having been absent and the anticipation of a negative response to return from care providers. Patient education for ART initiation in sub-Saharan Africa often includes stern warnings about the lifelong commitment beginning ART represents. Paradoxically, educational efforts intended to maximize the benefits of ART for patients may be driving some away from care. Therefore, efforts to prevent missed clinic visits coupled with strategies to minimize any obstacles to coming back to care are necessary to keep patients' missed visits from turning into long-term disengagement from treatment.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001369.
This study is further discussed in a PLOS Medicine Perspective by Edward Mills
The World Health Organization has the latest data on access to ART
The US National Institute of Allergy and Infectious Diseases has more information on types of ART, and also on adherence to treatment
The US Centers for Disease Control and Prevention has some information about interventions to help improve adherence
doi:10.1371/journal.pmed.1001369
PMCID: PMC3541407  PMID: 23341753
18.  The effect of tertiary surveys on missed injuries in trauma: a systematic review 
Background
Trauma tertiary surveys (TTS) are advocated to reduce the rate of missed injuries in hospitalized trauma patients. Moreover, the missed injury rate can be a quality indicator of trauma care performance. Current variation of the definition of missed injury restricts interpretation of the effect of the TTS and limits the use of missed injury for benchmarking. Only a few studies have specifically assessed the effect of the TTS on missed injury. We aimed to systematically appraise these studies using outcomes of two common definitions of missed injury rates and long-term health outcomes.
Methods
A systematic review was performed. An electronic search (without language or publication restrictions) of the Cochrane Library, Medline and Ovid was used to identify studies assessing TTS with short-term measures of missed injuries and long-term health outcomes. ‘Missed injury’ was defined as either: Type I) any injury missed at primary and secondary survey and detected by the TTS; or Type II) any injury missed at primary and secondary survey and missed by the TTS, detected during hospital stay. Two authors independently selected studies. Risk of bias for observational studies was assessed using the Newcastle-Ottawa scale.
Results
Ten observational studies met our inclusion criteria. None was randomized and none reported long-term health outcomes. Their risk of bias varied considerably. Nine studies assessed Type I missed injury and found an overall rate of 4.3%. A single study reported Type II missed injury with a rate of 1.5%. Three studies reported outcome data on missed injuries for both control and intervention cohorts, with two reporting an increase in Type I missed injuries (3% vs. 7%, P<0.01), and one a decrease in Type II missed injuries (2.4% vs. 1.5%, P=0.01).
Conclusions
Overall Type I and Type II missed injury rates were 4.3% and 1.5%. Routine TTS performance increased Type I and reduced Type II missed injuries. However, evidence is sub-optimal: few observational studies, non-uniform outcome definitions and moderate risk of bias. Future studies should address these issues to allow for the use of missed injury rate as a quality indicator for trauma care performance and benchmarking.
doi:10.1186/1757-7241-20-77
PMCID: PMC3546883  PMID: 23190504
Tertiary survey; Missed injury; Multiple trauma; Patient safety; Quality of care
19.  Missing Data in Longitudinal Trials - Part B, Analytic Issues 
Psychiatric annals  2008;38(12):793-801.
Longitudinal designs in psychiatric research have many benefits, including the ability to measure the course of a disease over time. However, measuring participants repeatedly over time also leads to repeated opportunities for missing data, either through failure to answer certain items, missed assessments, or permanent withdrawal from the study. To avoid bias and loss of information, one should take missing values into account in the analysis. Several popular ways that are now being used to handle missing data, such as the last observation carried forward (LOCF), often lead to incorrect analyses. We discuss a number of these popular but unprincipled methods and describe modern approaches to classifying and analyzing data with missing values. We illustrate these approaches using data from the WECare study, a longitudinal randomized treatment study of low income women with depression.
PMCID: PMC2722118  PMID: 19668352
20.  Techniques for Handling Missing Data in Secondary Analyses of Large Surveys 
Academic pediatrics  2010;10(3):205-210.
Objective
Using an appropriate method to handle cases with missing data when performing secondary analyses of survey data is important to reduce bias and to reach valid conclusions for the target population. Many published secondary analyses using child health data sets do not discuss the technique employed to treat missing data or simply delete cases with missing data. Missing data may threaten statistical power by reducing sample size or, in more extreme situations, estimates derived by deleting cases with missing values may be biased, particularly if the cases with missing values are systematically different from those with complete data. The aim of this study was to determine which of 4 techniques for handling missing data most closely estimates the true model coefficient when varying proportions of cases are missing data.
Methods
We performed a simulation study to compare model coefficients when all cases had complete data and when 4 techniques for handling missing data were employed with 10%, 20%, 30% or 40% of the cases missing data.
Results
When more than 10% of the cases had missing data, the re-weight and multiple imputation techniques were superior to dropping cases with missing scores or hot deck imputation.
Conclusions
These findings suggest that child health researchers should use caution when analyzing survey data if a large percentage of cases have missing values. In most situations, the technique of dropping cases with missing data should be discouraged. Investigators should consider re-weighting or multiple imputation, if a large percentage of cases are missing data.
doi:10.1016/j.acap.2010.01.005
PMCID: PMC2866831  PMID: 20338836
missing data; non-response bias; secondary analysis; hot deck imputation; weighting; multiple imputation
21.  A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures 
Background
Retaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data.
Methods
A systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included.
Results
Among the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n = 54, 66%). Other papers used more sophisticated methods including multiple imputation (n = 5) or fully Bayesian modeling (n = 1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n = 7), the missing indicator method (n = 1), and mean value substitution (n = 3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated.
Conclusions
This review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies.
doi:10.1186/1471-2288-12-96
PMCID: PMC3464662  PMID: 22784200
Longitudinal cohort studies; Missing exposure data; Repeated exposure measurement; Missing data methods; Reporting
22.  Intricacy of missing data in clinical trials: Deterrence and management 
Missing data is frequently encountered in clinical studies. Unfortunately, they are often neglected or not properly handled during data analysis and this may significantly bias the results of the study, reduce study power and lead to invalid conclusions. Substantial instances of missing data are a serious problem that undermines the scientific trustworthiness of causal conclusions from clinical trials. The assumption that statistical analysis methods can compensate for such missing data is not justified. Hence aspects of clinical trial design that limit the probability of missing data should be an important objective, while planning a clinical trial. In addition to specific aspects of trial design, many components of clinical trial conduct can also limit the extent of missing data. The topic of missing data is often not a major concern until it is time for data collection and data analysis. This article discusses some basic issues about missing data as well as prospective “watch outs” which could reduce the occurrence of missing data. It provides some possible design considerations that should be considered in order to alleviate patients from dropping out of a clinical trial. In addition to these the concept of the missing data mechanism has also been discussed. Three types of missing data mechanisms missing completely at random, missing at random and not missing at random have been discussed in detail.
doi:10.4103/2229-516X.140706
PMCID: PMC4181125  PMID: 25298936
Data monitoring and co-ordination; International Conference for Harmonization E9 Guidelines; missing data; missing data mechanisms; study conduct; study design
23.  Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates 
Biometrics  2011;67(3):10.1111/j.1541-0420.2010.01541.x.
Summary
Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the expectation–maximization algorithm give consistent estimators for model parameters when data are missing at random (MAR) provided that the response model and the missing covariate model are correctly specified; however, we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation, which gives consistent estimators if the missing data and response models are correctly specified; however, we do not need to specify the distribution of the covariates that have missing values. In this article, we develop a doubly robust estimation method for longitudinal data with missing response and missing covariate when data are MAR. This method is appealing in that it can provide consistent estimators if either the missing data model or the missing covariate model is correctly specified. Simulation studies demonstrate that this method performs well in a variety of situations.
doi:10.1111/j.1541-0420.2010.01541.x
PMCID: PMC3652597  PMID: 21281272
Doubly robust; Estimating equation; Missing at random; Missing covariate; Missing response
24.  Longitudinal data analysis with non-ignorable missing data 
Statistical methods in medical research  2012;10.1177/0962280212448721.
A common problem in the longitudinal data analysis is the missing data problem. Two types of missing patterns are generally considered in statistical literature: monotone and non-monotone missing data. Non-monotone missing data occur when study participants intermittently miss scheduled visits, while monotone missing data can be from discontinued participation, loss to follow-up and mortality. Although many novel statistical approaches have been developed to handle missing data in recent years, few methods are available to provide inferences to handle both types of missing data simultaneously. In this article, a latent random effects model is proposed to analyze longitudinal outcomes with both monotone and non-monotone missingness in the context of missing not at random (MNAR). Another significant contribution of this paper is to propose a new computational algorithm for latent random effects models. To reduce the computational burden of high dimensional integration problem in latent random effects models, we develop a new computational algorithm that uses a new adaptive quadrature approach in conjunction with the Taylor series approximation for the likelihood function to simplify the E step computation in the EM algorithm. Simulation study is performed and the data from the Scleroderma lung study are used to demonstrate the effectiveness of this method.
doi:10.1177/0962280212448721
PMCID: PMC3883866  PMID: 22637472
Adaptive quadrature; Missing not at random; Joint model; Scleroderma study
25.  The Advantage of Imputation of Missing Income Data to Evaluate the Association Between Income and Self-Reported Health Status (SRH) in a Mexican American Cohort Study 
Missing data often occur in cross-sectional surveys and longitudinal and experimental studies. The purpose of this study was to compare the prediction of self-rated health (SRH), a robust predictor of morbidity and mortality among diverse populations, before and after imputation of the missing variable “yearly household income.” We reviewed data from 4,162 participants of Mexican origin recruited from July 1, 2002, through December 31, 2005, and who were enrolled in a population-based cohort study. Missing yearly income data were imputed using three different single imputation methods and one multiple imputation under a Bayesian approach. Of 4,162 participants, 3,121 were randomly assigned to a training set (to derive the yearly income imputation methods and develop the health-outcome prediction models) and 1,041 to a testing set (to compare the areas under the curve (AUC) of the receiver-operating characteristic of the resulting health-outcome prediction models). The discriminatory powers of the SRH prediction models were good (range, 69–72%) and compared to the prediction model obtained after no imputation of missing yearly income, all other imputation methods improved the prediction of SRH (P<0.05 for all comparisons) with the AUC for the model after multiple imputation being the highest (AUC = 0.731). Furthermore, given that yearly income was imputed using multiple imputation, the odds of SRH as good or better increased by 11% for each $5,000 increment in yearly income. This study showed that although imputation of missing data for a key predictor variable can improve a risk health-outcome prediction model, further work is needed to illuminate the risk factors associated with SRH.
doi:10.1007/s10903-010-9415-8
PMCID: PMC3205225  PMID: 21103931
Self-rated health; Missing income data; Data imputation techniques; Mean substitution; Multiple imputation; Minority health

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