1. Formal experiments: randomisation + study size

2. Concept of randomisation Biology, 1926: Sir Ronald Fisher Medicine, 1947: Sir Austin Bradford Hill Randomised Controlled Trial Criminal justice ?

3. Randomisation in medicine Toss of coin determines experimental or control treatment RCT assignment unpredictable Fair [=> ethical] allocation of scarce resource Balance treatment numbers overall, in each hospital, and for major prognostic factors

4. RCT Telephone Randomisation

5. Licensing of pharmaceuticals: requires efficacy in RCTs Patients’ informed consent [ethics] Sufficiently-large [precise answer] Randomised [unbiassed: like with like]

6. “What works” in UK criminal justice? Large RCTs essentially untried... (bar restorative justice)

7. Judges prescribe sentence on lesser evidence than doctors prescribe medicines Is public aware?

8. Drug Treatment &Testing Orders (DTTOs) England & Wales: 210 clients Scotland: 96 clients Targets for DTTO clients in E&W: 6,000+ per annum DTTO clients: 21,000+ by end 2003

9. RSS Court DTTO-eligible offenders: do DTTOs work ? Off 1 DTTO Off 2 DTTO Off 3 alternative = Off 4 DTTO Off 5 alternative = Off 6 alternative = Count offenders’ deaths, re-incarcerations etc...

10. UK courts’ DTTO-eligible offenders: ? guess Off 7 DTTO [ ? ] Off 8 DTTO [ ? ] Off 9 DTTO [ ? ] Off10 DTTO [ ? ] Off11 DTTO [ ? ] Off12 DTTO [ ? ] Off13 DTTO [ ? ] Off14 DTTO [ ? ] (before/after) Interviews versus... [ ? ]

11. Evaluations-charade Failure to randomise Failure to find out about major harms Failure even to elicit alternative sentence  funded guesswork on relative cost-effectiveness Volunteer-bias in follow-up interviews Inadequate study size re major outcomes...

12. Power (study size) matters! Back-of-envelope sum for 80% power e. g. Percentages If MPs/social scientists don’t know, UK plc keeps hurting

13. For 80% POWER, 5% significance: compare failure (re-conviction) rates Randomise per treatment group, 8 times STEP 1 answer STEP 1: Success * fail rate + Success * fail rate for new disposal for control ------------------------------------------------------------ (new success rate – control success rate) 2

14. DTTOs: target 60% versus control 70% re-conviction rate? Randomise per ‘CJ disposal’ group, 8 times STEP 1 answer = 8 times 45 = 360 STEP 1 answer: 40 * 60 + 30 * 70 2400 + 2100 DTTOs control --------------------------------- = --------------- (40 – 30) 2 100

15. Four PQs for every CJ initiative PQ1: Minister, why no randomised controls? PQ2: Minister, why have judges not even been asked to document offender’s alternative sentence that this CJ initiative supplants? {cf electronic tagging} PQ3: What statistical power does Ministerial pilot have re well-reasoned targets? {or, just kite flying...} PQ4: Minister, cost-effectiveness is driven by longer-term health & CJ harms, how are these ascertained? {  database linkage}

16. Randomised controlled trials to police Policy by Home Office Prisons & Criminal Justice

17. HMP Peterborough PILOT: Kalyx prison, Social Finance run, & payment by results. What happens? HMP Peterborough Eligible Pre-release Inmate (serving less than 12 months) Taken on by SF YesNo Transfer’d Out... NoYesNoYes Deselected by SF NoYesN/A or Yes Already Done! N/A HMP Peterborough release YESYes, but... NOYes, but... NO Matched controls? ??

18. HMP Peterborough PILOT: Kalyx prison, Social Finance run, & payment by results. Per SF-release, comparators are? 10 same-sex ‘matched’ prisoners who also served less than 12 months & were released on same day but from other prisons {All Kalyx-run? Where? Functionality? Locality? De-selections & transfers?} Reduce convictions in 1 st year post-release by 7.5%... Conviction costed how... ??? {eg 60% to 55.5% convicted within 1 year of release ~ or reduce conviction-count by 7.5%}

20. Guardian Society: 17 Nov. 2004 “Some statisticians are so severe that they would stop social policy making in its tracks. For example, Bird would forbid the government to introduce any policy that had not been assessed through controlled trials... ”

21. SIMPLE RANDOMISATION STEP 1: Correspondence between random number (see tables) & CJ disposal: EVEN random number (0)  DTTO ODD random number  alternative STEP 2: Document starting point in tables & direction of reading: SMB = down 03 (row) 07 (column)

22. RANDOM NUMBER tables 72137 73850 32733 48083 50731 50584 16602 26772 81250 row 3, column 7: 55480 29910 89693 read down 77708 83761 47184 12601 54432 65664 73669

23. SIMPLE RANDOMISATION: down OffenderRandom #DisposalOutcomeCourt & J CJ016DTTOCB2 CJ029alternativeCB2 CJ033alternativeCB2 CJ044DTTOCB2 CJ053alternativeCB2 CJ06etcCB2 CJ07CB2 CJ08CB2 Correspondence: random # & disposal EVEN = DTTO ODD = alternative Row 03 Col 07

24. Randomisation by minimisation: next client = Cambridge, male, 18-24 years, >5 prison terms Client RCT assignments characteristic so far to DTTO alternative Cambridge 20 15 * male 205 190 * 18-24 years 100 * 108 >5 in-prison 180 * 185 SUM points : 505 498 ** RANDOMISE this client preferentially (eg 80:20) to ** because lower on points

25. Critical reading: BMJ, Lancet etc Statistical guide-lines for contributors to medical journals (20+ years): beware “bars” Structured ABSTRACT: essential design & primary outcome (s) CONSORT flowchart for reporting RCTs: beware i) post-randomization exclusions from analysis; ii) post-hoc subgroups. STROBE guide-lines for reporting observational studies: beware bias in many guises (especially: how explanatory variable is coded at analysis - eg binary!! & retrospective classification: deaths on transplant waiting list ~ count survival contribution on the waiting list of transplantees beofre operation)