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A G Babiker

Publications and source records attributed to A G Babiker.

17 recordsLinked to original sources
PubMed2001

Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.

Many cohort studies and clinical trials are designed to compare rates of change over time in one or more disease markers in several groups. One major problem in such longitudinal studies is missing data due to patient drop-out. The bias and efficiency of six different methods to estimate rates of changes in longitudinal studies with incomplete observations were compared: generalized estimating equation estimates (GEE) proposed by Liang and Zeger (1986); unweighted average of ordinary least squares (OLSE) of individual rates of change (UWLS); weighted average of OLSE (WLS); conditional linear model estimates (CLE), a covariate type estimates proposed by Wu and Bailey (1989); random effect (RE), and joint multivariate RE (JMRE) estimates. The latter method combines a linear RE model for the underlying pattern of the marker with a log-normal survival model for informative drop-out process. The performance of these methods in the presence of missing data completely at random (MCAR), at random (MAR) and non-ignorable (NIM) were compared in simulation studies. Data for the disease marker were generated under the linear random effects model with parameter values derived from realistic examples in HIV infection. Rates of drop-out, assumed to increase over time, were allowed to be independent of marker values or to depend either only on previous marker values or on both previous and current marker values. Under MACR all six methods yielded unbiased estimates of both group mean rates and between-group difference. However, the cross-sectional view of the data in the GEE method resulted in seriously biased estimates under MAR and NIM drop-out process. The bias in the estimates ranged from 30 per cent to 50 per cent. The degree of bias in the GEE estimates increases with the severity of non-randomness and with the proportion of MAR data. Under MCAR and MAR all the other five methods performed relatively well. RE and JMRE estimates were more efficient(that is, had smaller variance) than UWLS, WLS and CL estimates. Under NIM, WLS and particularly RE estimates tended to underestimate the average rate of marker change (bias approximately 10 per cent). Under NIM, UWLS, CL and JMRE performed better in terms of bias (3-5 per cent) with the JMRE giving the most efficient estimates. Given that markers are key variables related to disease progression, missing marker data are likely to be at least MAR. Thus, the GEE method may not be appropriate for analysing such longitudinal marker data. The potential biases due to incomplete data require greater recognition in reports of longitudinal studies. Sensitivity analyses to assess the effect of drop-outs on inferences about the target parameters are important.

Adolescent↗
PubMed2001

Analysis of failure time hierarchical data in the presence of competing risks with application to oral contraceptive pill use in Egypt.

Problems of practical interest in the analysis of data on contraceptive use, from Demographic and Health Surveys (DHS), include the estimation of the cause-specific probability of discontinuation by time t (the cumulative incidence function), in the presence of other competing causes and the evaluation of the effect of covariates on the cause-specific hazards of discontinuation. Methods of analysis of failure time data with competing risks are by now fairly well developed in the case of a simple random sample. However, the data from the DHS are clustered by geographical areas and include multiple episodes per woman. For a marginal (population average) approach, we propose using methods developed for simple random samples with standard errors calculated using a double bootstrap to take account of the clustered hierarchical nature of the data. In the conditional approach, the cause-specific hazards are modelled as log-linear functions of the covariates conditional on random effects of clusters and women, using a three-level multinomial discrete-time logit model. The methods are applied to data from Egypt 1992 DHS on the oral contraceptive pill use.

Adolescent↗
PubMed2001

Age as a determinant of survival in HIV infection.

Age is a major determinant of mortality for many diseases including HIV infection, yet the effect of age is rarely studied directly. In this article, we review what is known about the effect of age at seroconversion on HIV disease progression and survival prior to the widespread use of HAART before describing appropriate methods for adjusting for background mortality in more detail. We then investigate the impact of HAART on the effect of age at seroconversion on mortality and consider the estimation of the age effect in seroprevalent cohorts with regard to lack of knowledge of the true age at infection. Finally, we discuss mechanisms by which age at seroconversion might impact on disease progression and death. Throughout, we use published results by the Collaborative Group on AIDS Incubation and HIV Survival (CGAIHS), and published results and data from the Concerted Action on SeroConversion to AIDS and Death in Europe (CASCADE) for illustration.

Adult↗
PubMed2000

Comparison of a point mutation assay with a line probe assay for the detection of the major mutations in the HIV-1 reverse transcriptase gene associated with reduced susceptibility to nucleoside analogues.

This study compares the performance of a line probe assay (LiPA) for the detection of the major mutations associated with reduced sensitivity to nucleoside analogues with a well characterised point mutation assay (PMA). Plasma samples obtained from patients in a trial of four reverse transcriptase inhibitors (MRC Quattro Trial) were tested by both LiPA and PMA at baseline, 32nd and 64th weeks for the presence of drug resistance associated mutations in the reverse transcriptase (RT) gene. HIV-1 RNA was extracted from plasma by the Boom method and amplified by RT-PCR prior to being tested by LiPA or PMA. Assay discrepancies were further investigated by sequencing of the RT gene. Of 275 samples available from 98 trial subjects, 246 samples were successfully amplified by PCR and analysed by LiPA and PMA for six mutations. Of the 1476 individual codons analysed, LiPA successfully assayed 1444 (97.8%) and PMA gave a result with 1418 (96.1%). LiPA failed to give a result for 32 codons from 22 samples and PMA failed with 58 codons from 38 samples. Gross differences between the two assays, in which one scored a codon as wild-type only and the other as mutant only or vice versa, occurred at 28 codons analysed (1.9%) representing 26 samples from 20 subjects. Sequencing of 22 of the 26 samples confirmed the LiPA result in nine cases, the PMA result in 11 and detected a novel variant at codon 215 in four cases. The PMA and LiPA approach to the detection of the major mutations that are genotypically associated with reduced sensitivity to nucleoside analogues can correctly detect mutations in 97% of the cases.

Acetamides↗
PubMed2000

Analysis of multivariate failure-time data from HIV clinical trials.

We illustrate the use of marginal methods for the analysis of multivariate failure-time data using a large trial in HIV infection in which the composite endpoint of AIDS or death incorporates more than 20 events with varying severity. Multivariate failure-time methods are required to investigate whether treatment delays development of new AIDS events. AIDS events can be grouped and treatment effects estimated using only the first event to occur in each group for each individual. Alternatively, all events can be included by fitting a separate baseline hazard for development of each event, and restricting treatment effects to be common within groups of events. In either case, model-based or minimum-variance estimates of the overall effect of treatment can be constructed. The covariance matrix for the treatment-effect estimates can be used in multiple testing procedures. Results from the Delta trial suggest that combination antiretroviral therapy with AZT plus either ddI or ddC may delay progression to more severe AIDS events compared to AZT monotherapy. These late events are generally untreatable and prophylaxis is not available. Trials are not generally powered to detect treatment effects on individual events making up a composite endpoint, and therefore all analyses are exploratory rather than providing definitive evidence. However, marginal multivariate models provide an easily available approach for modeling the effect of covariates on multiple disease processes, and allow the likely effects of treatment to be presented in a manner which is easily understood. They can be used in a variety of ways to explore different patterns of treatment effects and are also useful for testing multiple hypotheses regarding treatment effects on several different composite endpoints.

Acquired Immunodeficiency Syndrome↗
PubMed1999

Randomization-based methods for correcting for treatment changes: examples from the Concorde trial.

We develop analysis methods for clinical trials with time-to-event outcomes which correct for treatment changes during follow-up, yet are based on comparisons of randomized groups and not of selected groups. A causal model relating observed event times to event times that would have been observed under other treatment scenarios is fitted using the semi-parametric approach of Robins and Tsiatis (avoiding assumptions about the relationship between treatment changes and prognosis). The methods are applied to the Concorde trial of immediate versus deferred zidovudine, to investigate how the results would have differed if no participant randomized to deferred zidovudine had started treatment before reaching ARC or AIDS. We consider issues relating to model choice, non-constant treatment effects and censoring.

Anti-HIV Agents↗
PubMed1999

The rabbit study: ritonavir and saquinavir in combination in saquinavir-experienced and previously untreated patients.

Thirteen protease inhibitor-naive patients with HIV-1 infection, and 12 patients with a median of 58 months prior treatment with saquinavir (SQV) monotherapy, were treated with SQV (400 mg twice daily) and ritonavir (RIT, 500 mg twice daily) in a study designed to assess the effect of prior treatment with SQV monotherapy on the antiretroviral activity of RIT-SQV combination therapy. Median baseline viral load and CD4+ cell counts were 155,000 and 262,000 copies/ml and 333 and 225 cells/mm3 in the naive and experienced groups, respectively. Mean viral load changes at 24 weeks were -1.63 and -0.27 log copies/ml in the naive and SQV-experienced groups, respectively (intent-to-treat analysis). Baseline genotype by point mutation assay and sequencing in the SQV-experienced group was highly predictive of virological response. Eight of 11 SQV-experienced patients had evidence of phenotypic resistance to RIT at baseline, despite previous treatment with SQV only. There was strong correlation between phenotypic resistance to RIT and the presence of the L90M mutation. We conclude that prolonged prior treatment with saquinavir monotherapy may produce cross-resistance to ritonavir and reduce the subsequent response to ritonavir-saquinavir in combination. In this study, both phenotypic resistance to ritonavir and presence of the L90M mutation predicted the viral load response to ritonavir-saquinavir.

Adult↗
PubMed1999

Estimation and comparison of rates of change in longitudinal studies with informative drop-outs.

Many cohort studies and clinical trials have designs which involve repeated measurements of disease markers. One problem in such longitudinal studies, when the primary interest is to estimate and to compare the evolution of a disease marker, is that planned data are not collected because of missing data due to missing visits and/or withdrawal or attrition (for example, death). Several methods to analyse such data are available, provided that the data are missing at random. However, serious biases can occur when missingness is informative. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this paper we consider the problem of estimation of the rate of change of a disease marker in longitudinal studies, in which some subjects drop out prematurely (informatively) due to attrition, while others experience a non-informative drop-out process (end of study, withdrawal). We propose a method which combines a linear random effects model for the underlying pattern of the marker with a log-normal survival model for the informative drop-out process. Joint estimates are obtained through the restricted iterative generalized least squares method which are equivalent to restricted maximum likelihood estimates. A nested EM algorithm is applied to deal with censored survival data. The advantages of this method are: it provides a unified approach to estimate all the model parameters; it can effectively deal with irregular data (that is, measured at irregular time points), a complicated covariance structure and a complex underlying profile of the response variable; it does not entail such complex computation as would be required to maximize the joint likelihood. The method is illustrated by modelling CD4 count data in a clinical trial in patients with advanced HIV infection while its performance is tested by simulation studies.

Biomarkers↗
PubMed1999

Are HIV-infected patients with rapid CD4 cell decline a subgroup who benefit from early antiretroviral therapy?

We have developed a model to determine whether asymptomatic HIV-infected individuals who have a rapid CD4 cell decline are a subgroup who might benefit from early antiretroviral therapy. Data were obtained from a subgroup of participants in the Concorde and EACG020 trials, two randomized, double-blind, comparative trials of immediate (IMM) versus deferred (DEF) zidovudine therapy in asymptomatic HIV-infected individuals. The subgroup comprised 297 patients (IMM = 154, DEF = 143) who had at least one CD4 cell count before and after randomization. The median CD4 cell count at randomization was 491 x 10(6)/L, and the median follow-up was 61 months. The rate of CD4 decline before and after randomization was estimated using multi-level linear regression analysis, and patients were stratified into quartiles according to the rate of CD4 cell decline before randomization. Outcome measures were the development of AIDS, a 50% drop in CD4 count from the baseline, and death. A Cox proportional hazards model was used to examine whether the effect of zidovudine on disease progression varied according to the previous rate of CD4 decline. We found that a more rapid rate of CD4 decline before randomization was associated with a greater reduction in the rate of CD4 decline following IMM antiretroviral therapy (r = -0.5, P = 0.03). The greatest risk reduction in disease progression with IMM antiretroviral therapy was seen in the quartile of patients with the highest rate of CD4 decline (> or = 26 x 10(6) cells/L per 6 months) (hazards ratio (HR) = 0.61, 95% CI = 0.35-1.05). However, this effect was statistically significant in only the Concorde trial (HR = 0.48, 95% CI = 0.29-0.89). In contrast, we found no evidence in the EACG020 trial of any trend towards greater benefit in those with the most rapid CD4 cell decline. These findings suggest that asymptomatic patients with rapid CD4 cell decline are a subgroup likely to benefit from early antiretroviral therapy. This analytic approach should now be replicated in trials of combination therapy, and these should include viral load data.

Adult↗
PubMed1998

Markers of HIV infection in the Concorde trial. Concorde Co-ordinating Committee.

The Concorde trial compared immediate (Imm) with deferred (Def) AZT monotherapy in asymptomatic HIV-positive participants. Haematological and immunological markers and weight were measured throughout, and correlated with clinical endpoints. Markers associated with disease progression (CD4 lymphocyte count and percentage, platelets, p24 antigen and beta 2 microglobulin favoured Imm: those associated with toxicity (haemoglobin, neutrophils and white cell count) favoured Def. CD8 and total lymphocyte count did not differ significantly between groups. In multivariate analysis, the combination of baseline CD4, p24 antigen and beta 2m was the best baseline predictor of disease. Including change in CD4 and beta 2m at 12 weeks, or changes over follow-up in these markers significantly improved the fit. Markers were also incorporated into the definition of 'clinical' endpoints. Hazard ratio estimates from end-points that included CD4 < 50 and CD4 < 25 were closest to those for AIDS or death alone, but added very few extra events. Use of other landmark CD4 counts (100 or greater) or relative decreases in counts (25% or more) increased the number of events, but overestimated the effect of immediate AZT. Although AZT had a beneficial effect on the surrogate markers of efficacy evaluated, these changes did not predict clinical outcome, nor could the markers be usefully incorporated into an endpoint definition.

Anti-HIV Agents↗
PubMed1997

Impact of treatment changes on the interpretation of the Concorde trial.

BACKGROUND: The Concorde trial compared two policies of therapy with zidovudine (ZVD) in individuals with asymptomatic HIV infection: immediate or deferred ZDV. Participants in both groups could stop their blinded trial therapy for several reasons and/or could start open-label ZDV. The difference in survival and disease progression between the two groups was estimated allowing for treatment changes. METHODS: The relationship between latest CD4 count, treatment changes and time to AIDS-related complex (ARC), AIDS or death was investigated using time-updated proportional hazards models, but these models gave seriously biased estimates of the effect of ZDV. Therefore, a method based on the comparison of the randomized groups was used. A model relating a participant's events times to the treatment actually received was used to estimate what would have been observed if the deferred group had not received ZDV before ARS or AIDS, and to explore alternative policies for starting Pneumocystis carinii pneumonia (PCP) prophylaxis. RESULTS: The major treatment changes during the trial were the termination of blinded therapy because of adverse events or personal reasons (575 out of 1749 participants), starting open-label ZDV (745 participants), and starting PCP prophylaxis (613 participants). Starting open-label ZDV and PCP prophylaxis were strongly related to latest CD4 count. The uncorrected hazard ratios for immediate compared with deferred groups were 0.89 for time to ARC, AIDS or death [95% confidence interval (CI), 0.75-1.05], 1.01 for time to AIDS or death (95% CI, 0.82-1.24), and 1.26 for time to death (95% CI, 0.93-1.70). After correction for treatment changes, these hazard ratios were 0.79 (95% CI, 0.57-1.11), 1.01 (95% CI, 0.81-1.26), and 1.37 (95% CI, 0.91-2.08), respectively. Correction for PCP prophylaxis made little difference to the results. CONCLUSIONS: Open-label ZDV before ARC or AIDS in the deferred group was likely to have diluted any differences between the immediate and deferred groups. After correction for this dilution, both the estimated benefit of immediate treatment in delaying progression to ARC, AIDS or death and the estimated disadvantage of immediate treatment in accelerating death were somewhat increased, but both remained consistent with chance alone. This study demonstrated the large potential bias inherent in non-randomization-based methods of analysis of clinical trials.

Acquired Immunodeficiency Syndrome↗
PubMed1994

Factors associated with high risk of perinatal and neonatal mortality: an interim report on a prospective community-based study in rural Sudan.

In a community-based prospective study, 6275 deliveries resulting in 6084 livebirths, 150 stillbirths (SB) and 167 neonatal deaths (NND) were monitored over a period of 3 years. The risk of an unfavourable outcome (SB or NND) in multiple pregnancies was more than ninefold that of singletons. Teenage mothers and those over 34 years of age ran nearly twice the risk of having an unfavourable outcome of pregnancy compared with mothers aged 20-29 years. First pregnancy and grand-multiparity (greater than eight previous pregnancies) carried a similar risk of an unfavourable outcome compared with mothers with 1-4 previous pregnancies. The most serious risk factor was the adverse outcome of the previous pregnancy. Compared with mothers whose last outcome had resulted in a livebirth surviving at least 30 days, mothers with a previous SB had seven times the risk (adjusted for age and parity) of SB and more than twice the risk of NND in the current pregnancy. Maternal illiteracy was associated with significantly higher risk of NND, and this rate decreased with increasing years of education. Frequency of antenatal visits had a marginally significant effect on the SB rate. Socioeconomic factors, diet and iron supplementation during pregnancy did not seem to affect the outcome.

Educational Status↗
PubMed1992

The role of the village midwife in detection of high risk pregnancies and newborns.

The preliminary findings of a prospective study of perinatal, neonatal and maternal mortality carried out in a rural community of Sudan are reported. Out of 6275 deliveries monitored over a period of 3 years, 150 stillbirths, 167 neonatal deaths and 27 maternal deaths were observed. An intervention program to upgrade the skills of the village midwives started in the middle of the second year. There was a 25% reduction in the risk of unfavorable outcome of pregnancy (i.e. stillbirth and neonatal death) in the third year relative to the first 2 years. Peer review of the 40 village midwives who took part in the study revealed their tremendous potentials in mobilization of mothers as well as participation in primary health care. Their role in detection of high risk pregnancies and newborns cannot be overemphasized.

Adolescent↗
PubMed1991

Floating absolute risk: an alternative to relative risk in survival and case-control analysis avoiding an arbitrary reference group.

We discuss the problem of describing multiple group comparisons in survival analysis using the Cox model, and in matched case-control studies. The standard method of comparing the risk in each group with a baseline group is unsatisfactory because the standard errors and confidence limits relate to correlated parameters, all dependent on precision within the baseline group. We describe the construction of standard errors for the parameters of all groups, without the need to select a baseline group. These standard errors can be regarded as relating to roughly independent parameters, so that groups can be compared efficiently without knowledge of the covariances. The method should assist in graphical presentation of relative risks, and in the combination of results from published studies. Two examples are presented.

Adult↗
PubMed1989

Pancreatic cancer, alcohol, diabetes mellitus and gall-bladder disease.

A case-control study comprising 216 cases of pancreatic cancer and 279 controls was conducted to investigate the relationship of pancreatic cancer with certain chronic medical conditions and with the consumption of tea, coffee and alcoholic beverages. Significant positive associations with pre-existing diabetes mellitus and gall-bladder disease were observed and there was weak evidence of association with liver disease. The relative risks for diabetes mellitus and gallstones diagnosed at least one year previously were 4.1 (p = 0.005) and 2.8 (p = 0.01) respectively. Cases drank significantly more beer than controls (p = 0.005) and there was evidence of a positive trend in risk with total alcohol consumption. Smoking was a clear risk factor, but cases and controls were very similar with respect to tea and coffee drinking habits.

Aged↗