Recent cases of fraud in clinical trials have attracted considerable media attention, but relatively little reaction from the biostatistical community. In this paper we argue that biostatisticians should be involved in preventing fraud (as well as unintentional errors), detecting it, and quantifying its impact on the outcome of clinical trials. We use the term &fraud' speci"cally to refer to data fabrication (making up data values) and falsi,cation (changing data values). Reported cases of such fraud involve cheating on inclusion criteria so that ineligible patients can enter the trial, and fabricating data so that no requested data are missing. Such types of fraud are partially preventable through a simpli"cation of the eligibility criteria and through a reduction in the amount of data requested. These two measures are feasible and desirable in a surprisingly large number of clinical trials, and neither of them in any way jeopardizes the validity of the trial results. With regards to detection of fraud, a brute force approach has traditionally been used, whereby the participating centres undergo extensive monitoring involving up to 100 per cent veri"cation of their case records. The cost-e!ectiveness of this approach seems highly debatable, since one could implement quality control through random sampling schemes, as is done in "elds other than clinical medicine. Moreover, there are statistical techniques available (but insu$ciently used) to detect &strange' patterns in the data including, but no limited to, techniques for studying outliers, inliers, overdispersion, underdispersion and correlations or lack thereof. These techniques all rest upon the premise that it is quite di$cult to invent plausible data, particularly highly dimensional multivariate data. The multicentric nature of clinical trials also o!ers an opportunity to check the plausibility of the data submitted by one centre by comparing them with the data from all other centres. Finally, with fraud detected, it is essential to quantify its likely impact upon the outcome of the clinical trial. Many instances of fraud in clinical trials, although morally reprehensible, have a negligible impact on the trial's scienti"c conclusions.

HIV/AIDS and depression are often thought to be interlinked. HIV positive cases may trigger symptoms of depression which, in turn, may result in risky sexual behavior and spread of HIV. Interviews were conducted in 104 patients of HIV/AIDS at the Anti-Retroviral Therapy (ART) Clinic of a teaching hospital in Uttar Pradesh (India) to study depression and examine its prevalence and association, if any, with some socio-demographic and clinical variables. The tools used to assess anxiety and depression and their severities were General Health Questionnaire (GHQ) 28 and Montgomery-Asberg Depression Rating Scale (MADRS) . The majority of patients were of age 35 years & above (62%), males (67%), married (85%), Hindus (88%), literate (73.1%), unemployed (35%) and of upper-lower socio-economic status (52%). Significant association of depression was found with religion, occupation and socio-economic status. Depression and anxiety were also found to be significantly associated with each other. There was, however, no association of depression with respondents' age, gender, marital status, education, habitat, income, duration of illness from HIV/AIDS and the CD4 count. The high prevalence rate (67.3%) of depression amongst HIV patients in our study may be taken as marker to alert Counsellors of country's ART Clinics for possible risk of depression in HIV patients. The above findings however, should be interpreted in the light of the fact that a parallel control group in the study was not included, studied sample was not large enough and the tools used to study the subjects for depression and anxiety were not adequately standardized.

Afield study, aimed at measuring the personal cost of illness from five major water-related diseases was undertaken in a rural area of Uttar Pradesh (India) in 1981-82. The diseases included in the study were-entericfever, acute diarrhoeal diseases, infective hepatitis, conjunctivitis and scabies. The measurement of \he cost of illness included information on losses in productivity and treatment costs. The annual costs of illnesses per 100 people in 1981 were Rs 7353

A house-to-house survey of blindness in an Indian rural community covering a population of 20 134 in 12 villages revealed a prevalence rate of 35 blind and 144 partially blind persons per 10 000 population. Blindness was significantly associated with the age, sex, marital status, occupation, and socioeconomic status of the respondents. Caratact, glaucoma, smallpox and trachoma were the main causes of blindness. Preventive measures can reduce the toll of blindness in such a community.